For the greater part of the last decade, three tools have come to dominate this category: Ansible, Progress Chef, and Puppet. On a general level, here’s how they stack up:

• Ansible: Lightweight and easy to use; ideal for supporting a wide range of environments and configurations
• Progress Chef: Powerful, secure, and highly customizable; ideal for organizations that require an advanced automation platform
• Puppet: Feature-laden with a large user base and following; ideal for enterprises looking for a premium automation solution

In this article, we’ll delve into each of these automation tools’ respective strengths and shortcomings across eight key categories: pricing, ease-of-use, quality of support and documentation, breadth of features, integrations, supported environments, size/makeup of customer base, and community size.

Ansible vs. Chef vs. Puppet at a Glance

Best for Pricing: Ansible and Progress Chef

When it comes to pricing, premium versions of Ansible and Progress Chef fall in the same range—$137 to $140 a year per node—while Puppet Premium is on the high end at $199 a year per node. It’s worth noting that all three are all available as free open source projects, though the commercial versions come with additional features and support. Organizations with large scale deployments and complex infrastructures may be better suited with the premium versions.

Best for Ease of Use: Ansible

Ansible takes the lead in this category for myriad reasons. From its minimal setup/configuration and agentless architecture to declarative model and use of YAML syntax, the automation platform was designed to be lightweight and easy to use.

On the other hand, Progress Chef and Puppet are notorious for their steep learning curves and require users to learn a domain-specific language (DSL) and understand how to configure/deploy agents on target systems. That said, they also provide more granular control and flexibility for custom use cases at the cost of additional complexity.

Best for Support and Documentation: Ansible

Ansible boasts excellent documentation, featuring comprehensive guides, modules, and examples. Furthermore, its extensive user community contributes to the solution’s continuously expanding knowledge base. Progress Chef and Puppet also provide comprehensive documentation, albeit not as novice-friendly as Ansible’s materials. In terms of support, Puppet, Progress Chef, and Ansible all offer enterprise support as well as community-driven forums to assist users in resolving issues.

Best for Features: Three-way tie

When it comes to the breadth of features, all three automation tools provide robust capabilities.

• Ansible focuses on simplicity and ease of use, providing a wide range of modules for infrastructure provisioning, configuration management, and application deployment.
• Progress Chef emphasizes infrastructure as code, enabling users to define and manage their infrastructure using the Chef DSL.
• Puppet excels in configuration management, featuring a powerful domain-specific language for defining system states.

Ansible Feature Highlights

Some of Ansible’s more notable features include the following:

• Agentless Architecture: Ansible’s standout feature is its agentless architecture, which eliminates the need to install and manage agents on target systems. Ansible connects to remote systems using secure shell (SSH) or other remote protocols, making it lightweight, easy to set up, and non-intrusive to the existing infrastructure.
• YAML-based Playbooks: Ansible uses YAML-based playbooks to define and orchestrate automation tasks. Playbooks provide a human-readable and easily understandable format, allowing users to describe the desired state of systems and execute complex workflows using simple and concise syntax.
• Push-based Model: Ansible follows a push-based model, where the control machine pushes configurations and tasks to the target systems. This model enables real-time execution of tasks, making Ansible well-suited for ad-hoc automation, configuration management, and application deployment.
• Built-in Idempotence: Ansible enforces “idempotent” operations, which means that executing the same playbook multiple times results in the same desired state. This feature eliminates the risk of unintended configuration changes and allows for safe and predictable automation across various systems and environments.
• Extensibility and Integrations: Ansible offers a wide range of modules and plugins, allowing users to extend its functionality and integrate with existing tools and technologies. Ansible can seamlessly integrate with cloud platforms, networking devices, databases, and more, making it a versatile automation tool that can span across different infrastructure components.

Progress Chef Feature Highlights

Some of Progress Chef’s more notable features include the following:

• Chef Infra: Progress Chef’s Infra platform enables developers and operators to define infrastructure configurations using a declarative language, providing version control, repeatability, and consistency across environments.
• Chef Habitat: Progress Chef includes Chef Habitat, a tool that simplifies the packaging, deployment, and management of applications. Habitat provides a framework for creating platform-agnostic application artifacts, enabling easy application migration and seamless deployment across different environments.
• Chef Automate: Progress Chef offers Chef Automate, a comprehensive platform and dashboard that provides visibility and control across the entire automation infrastructure. The platform offers real-time analytics, compliance reporting, and a centralized dashboard for managing infrastructure, applications, and security.
• Cookbook Library: Progress Chef boasts an expansive community-driven cookbook library, which is a collection of pre-built, reusable configuration and automation scripts. This library enables developers to leverage the expertise and best practices of the Chef community, accelerating the deployment of infrastructure and applications.
• Enterprise Security and Compliance: Progress Chef places a strong emphasis on security and compliance and provides features like role-based access control (RBAC), encrypted data transmission, and built-in audit trails to ensure the security of your infrastructure. Additionally, Progress Chef helps organizations adhere to various industry standards and regulations and streamline the process of meeting security and governance requirements.

Puppet Feature Highlights

Some of Puppet’s more notable features include the following:

• Declarative Language: Puppet’s declarative language, Puppet DSL, enables users to describe the desired end state of their infrastructure and configurations, rather than focusing on the specific, step-by-step instructions, thereby simplifying the management and maintenance of complex systems.
• Puppet Forge: Puppet offers Puppet Forge, a vast repository of pre-built modules that provide ready-to-use configurations and automation scripts. This extensive library allows users to leverage community-contributed content, saving time and effort in developing and implementing configurations from scratch.
• IaC Support: Puppet supports IaC by allowing users to define and manage infrastructure configurations in a version controlled, tracked database or repository (e.g., GitHub) as if it were application code.
• Puppet Enterprise: Puppet provides Puppet Enterprise, a centralized and enterprise-grade solution for configuration management. The platform extends the capabilities of the open-source Puppet tool with additional features like a graphical user interface (GUI), RBAC, event inspector, and other advanced orchestration capabilities.
• Master-agent Architecture: Puppet’s master-agent model consists of a Puppet master server controlling and managing the configurations of multiple Puppet agent nodes. This architecture allows for efficient and scalable management of distributed systems, ensuring consistent configurations across a network of nodes.

Best for Integrations: Ansible

Ansible boasts an extensive range of integrations, with support for various cloud platforms, infrastructure providers, and third-party tools through its vast collection of modules. Similarly, Progress Chef and Puppet also offer integrations with popular cloud platforms and tooling.

Ansible Integration Highlights

Some of Ansible’s more notable integrations include the following:

• Cloud Platforms: Ansible integrates with popular cloud platforms like AWS, Microsoft Azure, Google Cloud Platform (GCP), and VMware vSphere, and supports automatic provisioning, deployment, and management of cloud resources, including virtual machines, networking, and storage.
• Version Control Systems: Ansible seamlessly integrates with version control systems like Git/GitHub, enabling users to store and manage Ansible playbooks and configurations in a version-controlled repository.
• Networking Devices: Ansible integrates with network devices from leading vendors like Cisco, Juniper, Arista, and F5 Networks, and provides modules specifically designed for network automation, enabling users to manage and configure network devices programmatically.
• Continuous Integration/Continuous Deployment (CI/CD) Tools: Ansible integrates with popular CI/CD tools like Jenkins, GitLab CI/CD, and Atlassian Bamboo, allowing users to incorporate Ansible automation into their CI/CD pipelines, as well as automate the deployment of applications and infrastructure as part of the continuous delivery process.
• Databases: Ansible provides modules for interacting with leading databases like MySQL, PostgreSQL, Oracle, and MongoDB, allowing users to automate tasks related to database provisioning, configuration, and data management.
• Container Orchestration Platforms: Ansible integrates with container orchestration platforms like Kubernetes and Docker Swarm, allowing users to automate the deployment and management of containers, including provisioning, scaling, and updating containerized applications

Progress Chef Integration Highlights

Some of Progress Chef’s more notable integrations include the following:

• Cloud Platforms: Progress Chef Automate integrates with major cloud platforms such as AWS, Microsoft Azure, and Google Cloud Platform (GCP). This integration enables users to automate the provisioning, configuration, and management of cloud resources and services.
• Container Orchestration Platforms: Progress Chef Automate integrates with container orchestration platforms like Kubernetes and Docker Swarm. It provides capabilities for managing and automating the deployment of containerized applications, including configuration management and scaling.
• CI/CD Tools: Progress Chef Automate integrates with popular CI/CD tools such as Jenkins, GitLab CI/CD, and Atlassian Bamboo. These integrations enable users to incorporate Progress Chef Automate into their CI/CD pipelines, automating the deployment and configuration of applications as part of the continuous delivery process.
• Monitoring and Logging Tools: Progress Chef Automate supports integration with monitoring and logging tools like Prometheus, Elasticsearch, and Splunk. These integrations allow security teams to collect and analyze data from Progress Chef Automate for monitoring, troubleshooting, and compliance purposes.

Puppet Integration Highlights

Some of Puppet’s more notable integrations include the following:

• Cloud Platforms: Puppet integrates with major cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), and VMware vSphere. These integrations enable users to manage and automate the provisioning, configuration, and management of cloud resources.
• CI/CD Tools: Puppet integrates with popular continuous improvement/continuous deployment (CI/CD) tools such as Jenkins, GitLab CI/CD, and Atlassian Bamboo. These integrations allow users to incorporate Puppet into their CI/CD pipelines, automating the deployment and configuration of infrastructure and applications as part of the continuous delivery process.
• Version Control Systems: Puppet integrates with version control systems like Git and Subversion. These integrations allow users to manage Puppet code, manifests, and modules using version control, facilitating collaboration, change tracking, and version management.
• Networking Devices: Puppet supports integration with network devices from various vendors, including Cisco, Juniper Networks, Arista Networks, and F5 Networks. These integrations enable users to automate network device configurations, provisioning, and management.
• Container Orchestration Platforms: Puppet integrates with container orchestration platforms such as Kubernetes and Docker Swarm. These integrations enable users to manage and automate the configuration and deployment of containerized applications.

Best for Supported Environments: Three way tie

All three tools support a wide array of operating systems and environments. As an agentless solution, Ansible can manage heterogeneous environments effortlessly, including Windows, Linux, and network devices. Progress Chef and Puppet, with their agent-based approach, can manage a diverse range of systems and are well-suited for complex environments.

Best for Customer Base: Ansible 

Despite being the youngest of the three automation tools, Ansible has gained significant traction in recent years and has amassed a large, rapidly growing user base. The tool’s ease-of-use and extensive community support are key ingredients driving its popularity. Progress Chef and Puppet also have substantial customer bases, but more so with larger organizations and enterprises.

Best for Community Size: Ansible

Ansible has a thriving, active community that actively contributes to its wealth of resources in the form of blogs, forums, and GitHub repositories. Similarly, Progress Chef and Puppet also have active, sizable communities, though not as large or vocal as Ansible’s crowd.

Top 3 DevOps Configuration Tooling Alternatives

Chef, Ansible, and Puppet aren’t the only automation tooling players in the game. The following alternatives provide similar features and capabilities, and may warrant further investigation

if none of the leading three platforms suit your organization’s needs.

AWS CloudFormation (and Cloud Vendor-provided Tools)

If you’re looking to automate an AWS-based cloud infrastructure, AWS CloudFormation is a formidable alternative to Puppet, Chef, and Ansible. In fact, CloudFormation—like Ansible—enables you to define IaC using JSON or YAML templates However, CloudFormation naturally a more AWS-centric solution for describing and provisioning AWS resources like EC2 instances, load balancers, and databases, along with their configurations and dependencies. Unsurprisingly, similar services like Microsoft’s Azure Resource Manager (ARM) and Google Cloud Deployment Manager (GCDM) also offer automation capabilities that excel in their native vendors’ environments.

SaltStack

Another long standing alternative to Puppet and Progress Chef, SaltStack is an open-source configuration management and orchestration tool known for its scalability and performance. The tool uses a master-minion architecture that has a master node communicating with multiple minion nodes to manage configurations and execute tasks.

Terraform

While not a direct alternative to Ansible, Puppet, or Progress Chef, Terraform does possess some overlapping capabilities for infrastructure provisioning and management. The solution enables users to define infrastructure as code using a declarative language, and supports multiple cloud platforms—AWS, Azure, Google Cloud, as well as on-premises infrastructure. With Terraform, you can define the desired state of your infrastructure and execute plans to create, modify, or delete resources accordingly.

Bottom Line: Ansible vs. Chef. vs. Puppet

In short, Ansible excels in pricing, ease-of-use, and community size, making it an ideal choice for organizations seeking a user-friendly automation tool for basic IT environments. Progress Chef and Puppet provide more granular control mechanisms and robust features, making them suitable for complex environments with specific requirements. By carefully evaluating these three tools based on pricing, ease-of-use, documentation, features, integrations, supported environments, customer base, and community, you’ll be better positioned to make an informed decision that aligns with your organization’s needs and goals.

We’ve rounded up the best intrusion prevention systems to help make the selection process less daunting. Here are our top picks:

• Cisco Secure Next-Generation Intrusion Prevention System: Best for Comprehensive Network Security
• Fidelis Network: Best for Advanced Threat Detection Response
• Palo Alto Networks Advanced Threat Prevention: Best for ZeroDay Exploits
• Trellix Intrusion Prevention System: Best for On-Prem and Virtual Networks

Top Intrusion Prevention System Comparison At-a-Glance

Here’s a look at how the top IPSs compared based on key features.

Jump to:

1. Key Intrusion Prevention System Features
2. How to Choose an IPS
3. Frequently Asked Questions (FAQs)

Cisco Secure Next-Generation Intrusion Prevention System

Best for comprehensive network security

Cisco offers advanced threat protection solutions with Cisco Secure IPS. This cloud-native platform offers robust security with unified visibility and intuitive automation. It gathers and correlates global intelligence in a single view and can handle large traffic volumes without impacting the network performance.

This highly flexible solution can be easily deployed across different network environments as its open architecture supports Amazon Web Services (AWS), VMWare, Azure, and other hypervisors.

Features

• Enhanced visibility with Firepower Management Center
• Constantly updated early-warning system
• Flexible deployment options for inline inspection or passive detection
• Cisco Threat Intelligence Director for third-party data ingestion

Pros

• Real-time data inputs optimize data security
• Easy integration without major hardware changes
• High scalability with purpose-built solutions

Cons

• Expensive for small-scale organizations
• Initial integration challenges

Pricing

Cisco offers free trials for most products, including its IPS, but does not make its pricing readily available. For details, contact Sales Support.

Fidelis Network

Best for Advanced Threat Detection Response

Fidelis Network improves security efficiency by detecting advanced threats and behavioral anomalies, employing a proactive cyber-defense strategy to more quickly detect and respond to threats before they can affect a business. Fidelis Network can bolster data security with rich insights into bi-directional encrypted traffic.

This specific network defense solution helps prevent future breaches with both real-time and retrospective analysis.

Features

• Patented Deep Session Inspection for data exfiltration
• Improved response with the MITRE ATT&CK framework and intelligence feed from Fidelis Cybersecurity
• Unified network detection and response (NDR) solution for simplified network security
• Customizable real-time content analysis rules for proactive network security

Pros

• Faster threat analysis and improved security efficiency
• Deeper visibility and threat detection with more than 300 metadata attributes
• Single-view and consolidated network alerts with rich cyber terrain mapping

Cons

• Complex configuration and setup
• High-traffic environments cause network latency
• Tighter integration with other tools is required

Pricing

Fidelis Network offers a 15-day free trial, and will schedule a demo before it to show off the system’s capabilities and features.

Palo Alto Networks Advanced Threat Prevention 

Best for Zero-Day Exploits

Palo Alto Networks’ Advanced Threat Prevention is based on purpose-built, inline deep learning models that secure businesses from the most advanced and evasive threats. Powered by multi-pronged detection mechanisms that efficiently take care of unknown injection attacks and zero-day exploits, this infinitely scalable solution blocks command and control (C2) attacks in real time without compromising performance.

Features

• ML-Powered NGFWs for complete visibility
• Customized protection with Snort and Suricata signature support
• Real-time analysis with enhanced DNS Security Cloud Service
• Latest security updates from Advanced WildFire

Pros

• Ultra low-latency native cloud service
• Combined App-ID and User-ID identification technologies
• Customized vulnerability signatures
• Complete DNS threat coverage

Cons

• Overly complex implementation for simple configurations
• High upfront costs

Pricing 

Palo Alto Networks offers free trials, hands-on demos, and personalized tours for its products and solutions, but does not make its pricing models publicly available. Contact sales for details.

Trellix Intrusion Prevention System

Best for On-Prem and Virtual Networks

Trellix Intrusion Prevention System offers comprehensive and effective security for business networks, offering two variants: Trellix Intrusion Prevention System and Trellix Virtual Intrusion Prevention System. The virtual variant takes care of the private and public cloud requirements, and secures virtualized environments using advanced inspection technologies.

Features

• Botnet intrusion detection across the network
• Enhanced threat correlation with network threat behavior analysis
• Inbound and outbound SSL decryption
• East-west network visibility

Pros

• Both signature-based and signature-less intrusion detection
• Unified physical and virtual security
• Maximum security and performance (scalability up to 100 Gbps)
• Shared licensing and throughput model

Cons

• Older variants and models still exist
• Confusion pricing options
• High rates of false positives

Pricing

Schedule a demo to learn whether Trellix meets specific requirements. The vendor does not make pricing models publicly available; contact sales.

Key IPS Features

When deciding on an intrusion prevention system, make sure the features and capabilities match specific needs. Key features include the following:

Real-time alerts

Proactive threat detection and prompt incident response require real-time visibility. Timely alerts help implement preventive measures before any significant damage to the security posture. Advanced IPSs have real-time monitoring capabilities to identify potential vulnerabilities and minimize the impact of security incidents.

Integration with other security systems

Intrusion prevention systems cannot operate in isolation. For the efficient protection of the entire business security infrastructure, they must integrate with other security solutions and platforms for a coordinated response. This also helps with the centralized management of security incidents.

Type of intrusion detection

There are mainly two types of intrusion detection: network-based and host-based. While network-based intrusion detection examines and analyzes the network traffic for vulnerabilities, host-based intrusion detection checks individual systems like servers, endpoints, or particular assets.

Automatic updates

Automatic updates can help ensure an IPS adapt to the continuously evolving threat landscape of new threats and newly discovered vulnerabilities. They can also help keep pace with changing compliance and regulatory requirements and implement the latest security patches.

Threat intelligence

Threat intelligence helps an IPS enhance detection capabilities and minimize vulnerabilities with efficient mitigation strategies. With threat intelligence capabilities, IPS solutions access timely and actionable information to develop effective response strategies.

How to Choose an IPS

Here are some factors to consider when choosing an IPS:

Configuration type

There are broadly four types of IPS configurations depending on the network environment, security policies, and requirements where they will be implemented: network-based, host-based, wireless, and network behavior analysis system. Multiple configurations can also support complex pathways.

Detection capabilities

Intrusion prevention systems use different detection techniques to identify malicious activities—primarily signature-based, anomaly-based, and protocol-based. Signature-based detection helps detect consistent cyber threat patterns from a static list of known signatures, while anomaly-based detection can detect abnormalities within normal activity patterns. Protocol-based systems offer the flexibility to set references for benign protocol activities.

Integration options

Intrusion prevention systems can be integrated using dedicated hardware and software, or incorporated within existing enterprise security controls. Businesses that don’t want to upgrade system architecture or invest in products or resources can rely on managed service providers for security, but an IPS purchased and installed on the network offers more control and authority.

Frequently Asked Questions (FAQs)

What is the difference between intrusion detection systems and intrusion prevention systems?

Intrusion detection systems help detect security incidents and threats and send alerts to the Security Operations Center (SOC). Issues are investigated by security personnel and countermeasures executed accordingly. Essentially, they’re monitoring tools. While intrusion prevention systems also detect potential threats and malicious incidents, they automatically take appropriate actions, making them highly proactive, control-based cybersecurity solutions.

How do intrusion prevention systems help businesses?

Intrusion prevention systems are key to enterprise security as they help prevent serious and sophisticated attacks. Some of the key benefits of IPS for businesses are:

• Reduced strain on IT teams through automated response
• Customized security controls as per requirements
• Improved performance by filtering out malicious traffic

Do intrusion prevention systems affect network performance?

Intrusion prevention systems may slow down the network in the case of inadequate bandwidth and capacity, heavy traffic loads, or computational burdens.

Methodology

In order to provide an objective and comprehensive comparison of the various IPSs available in the market, we followed a structured research methodology. We defined evaluation criteria, conducted market research, collected data on each solution, evaluated and scored them, cross-verified our findings, and documented the results. Additionally, we considered user reviews and feedback to gain valuable insights into the real-world performance and customer satisfaction of each intrusion prevention solution.

Bottom Line: Top Intrusion Prevention Systems

The top intrusion prevention systems all work to protect enterprise networks from the ever-present, always evolving threat of cyberattack, but some stand out for different use cases. Selecting the right one will depend on the organization’s security needs, goals, and budget. Regular evaluation and updates are crucial to staying ahead of evolving threats and ensuring a robust security posture—the right IPS can enhance network security, protect sensitive data, and safeguard a business against potential cyber threats.

Organizations continue to face a critical decision when it comes to their IT infrastructure: fully embrace the cloud, or adopt an on-premises model? The question remains pertinent despite the fact that the cloud has been around for almost two decades now. This article will provide deeper context to this question with the goal of helping organizations make better-informed infrastructure decisions unique to their specific requirements and environments.

On-Premises vs. Cloud

As their name implies, on-premises environments have computing resources and systems that are physically located within an organization’s premises or facilities. This gives them direct control and ownership over their IT infrastructure, including the physical infrastructure, security measures, and network connectivity. This means they are also responsible for procuring, installing, configuring, and managing all the necessary components as well as ensuring their maintenance, upgrades, backups, and security.

In contrast, a cloud-based infrastructure involves the deployment and maintenance of servers, storage devices, networking equipment, and other hardware and software resources in the cloud service provider’s data centers. A cloud infrastructure is easier to deploy and manage initially, with no required upfront capital expenditures in hardware. Cost-wise, the cloud uses a metered, pay-per-use model, which—depending on scaling requirements and other factors—can be more cost-effective than on-premises.

Cloud Pros and Cons

The cloud has revolutionized the way organizations consume and manage data, applications, and IT resources. Some crucial benefits of the cloud include:

• Unprecedented Scalability: Cloud infrastructure offers unparalleled scalability, allowing businesses to scale resources up or down based on demand. This ensures optimal performance and cost efficiency.
• Significant Cost Savings: Cloud computing eliminates the need for capital expenditure on hardware, maintenance, and upgrades. Instead, businesses can opt for a pay-as-you-go model, reducing upfront costs and enabling predictable budgeting.
• Expanded Accessibility and Flexibility: Cloud services can be accessed from anywhere with an internet connection, providing seamless collaboration and remote access to resources. This flexibility is especially beneficial for distributed teams and remote work environments.
• Automatic Updates: Cloud providers take care of infrastructure updates and security patches, freeing up internal IT teams from routine maintenance tasks.
• Easier Disaster Recovery: Cloud-based backups and disaster recovery solutions offer data redundancy and high availability, minimizing downtime and ensuring business continuity.

Some potential drawbacks to consider when adopting cloud infrastructures include the following:

• Data Security and Privacy Concerns: Organizations that entrust sensitive data to a cloud service provider may raise security and privacy concerns with their own customers.
• Compliance Issues: Cloud service providers typically implement robust security measures; however, organizations must ensure compliance with relevant regulations and industry standards.
• Vendor Lock-in: Migrating from one cloud service provider to another can be challenging and costly, as organizations may become dependent on specific features or services offered by a particular provider.

On-Premises Pros and Cons

On-premises IT infrastructures provide organizations with significant benefits absent in cloud implementations, including the following:

• More Data Control: On-premises infrastructures provide organizations with complete control over their data and resources—a potential hard requirement in highly regulated industries or for organizations with strict compliance requirements.
• Lower Latency: On-premises infrastructures can offer lower latency, since data processing and storage occur locally.
• More Customization Options: On-premises allows organizations to custom-tailor their IT environments for their specific needs and integrate legacy systems seamlessly.

On-premises infrastructures also have their share of drawbacks:

• High Upfront Costs: Building on-premises infrastructure involves significant upfront costs, including hardware, software licenses, and dedicated IT staff.
• Maintenance and Updates: Organizations are responsible for maintaining and updating their own infrastructure, which can be resource-intensive and require skilled IT personnel.
• Scalability Challenges: Scaling on-premises infrastructures can be complex, time-consuming, and costly, requiring additional hardware purchases and configuration adjustments.
• Limited Accessibility: On-premises infrastructure may pose limitations for remote work and collaboration, restricting accessibility to data and applications.

Cloud vs. On-Premises: How to Decide

The choice between cloud and on-premises infrastructure ultimately depends on the unique needs and priorities of each organization. Here’s a look at how each solution measures up on key feature areas.

Cost

Because cloud service providers handle hardware maintenance, software updates, and security, on-premises solutions may seem costlier; however, once on-premises IT infrastructure is established, the ongoing costs can be lower compared to long-term cloud usage. Additionally, cloud computing costs can easily skyrocket if not properly configured and managed. However, for organizations that need to scale their resources according to fluctuating demand, the cloud’s pay-as-you-go pricing model can result in more predictable monthly costs, if optimized correctly.

Ease of Implementation

To implement a cloud-based infrastructure, organizations must select a cloud service provider, migrate applications and data, and configure the necessary resources. Over the years, standard best practices for migrating from on-premises to the cloud have emerged, and cloud providers offer extensive documentation, support, and tools to facilitate the migration process. However, organizations should nonetheless carefully plan and execute their cloud migrations to ensure minimal disruption and optimal performance.

Implementing on-premises infrastructures also require significant planning, hardware procurement, installation, and configuration; however, in this case organizations must allocate resources for building and maintaining the infrastructure, including skilled IT personnel for ongoing management.

Security

Cloud service providers invest heavily in security measures, including data encryption, access controls, and threat detection systems. They employ dedicated security teams and adhere to industry standards and compliance regulations. However, organizations must also take responsibility for securing their applications, data, and user access through proper configuration and robust security practices.

When it comes to on-premises, organizations are left to their own devices and have direct control over their security measures. They can implement specific security protocols, firewalls, and intrusion detection systems tailored to their requirements. However, this also means that organizations are solely responsible for ensuring the effectiveness of these security measures and staying up to date with the latest threats and vulnerabilities.

Compliance

Cloud service providers often offer compliance certifications and attestations to demonstrate their adherence to industry-specific regulations and security standards. This is crucial for organizations operating in highly regulated industries or handling sensitive data; however, firms must nonetheless ensure that their specific cloud-based IT assets are properly configured, and that any additional security measures are in place to meet specific compliance requirements. On-premises infrastructure allows organizations to maintain full control over compliance and regulatory requirements. They can implement customized security controls and monitoring processes to meet specific compliance standards.

Data Accessibility

Cloud services enable universal accessibility, allowing users to access data and applications from any location with an internet connection. This flexibility is particularly beneficial for remote workforces, enabling seamless collaboration and productivity. On-premises infrastructures may pose limitations on accessibility, especially for remote or geographically distributed teams. Organizations must establish secure remote access mechanisms to enable remote access to on-premises IT resources.

The Hybrid Cloud: Best of Both?

In some cases, organizations may opt for a hybrid cloud approach that combines elements of both cloud and on-premises infrastructures. This model allows organizations to leverage the scalability and flexibility of the cloud while maintaining sensitive data or critical applications on-premises. For many organizations, hybrid cloud environments provide the best of both worlds, allowing for a balance of cost efficiency, flexibility, and data control.

Cloud and On-Premises Use Cases

The choice between cloud and on-premises infrastructures depends on the specific needs, priorities, and circumstances of each organization. The following are several ideal use cases for cloud and on-premises IT infrastructures, with factors such as cost, scalability, data control, compliance requirements, and security all come into play when making an informed decision.

Cloud

• Startups and Small Businesses: The cloud offers a cost-effective solution for startups and small businesses, eliminating the need for substantial upfront investments in infrastructure and IT personnel.
• Scalability and Bursting: Organizations with fluctuating workloads or seasonal demand can benefit from the scalability offered by the cloud. They can easily scale resources up or down as needed, optimizing costs and performance.
• Collaboration and Remote Work: Cloud services enable seamless collaboration among distributed teams, facilitating remote work and improving productivity.

On-Premises

• Highly Regulated Industries: Organizations operating in industries with strict compliance requirements (e.g., finance or healthcare) often go with on-premises to maintain full control over data security and compliance.
• Supporting Legacy Systems: Organizations with legacy systems may go with on-premises in order to integrate and coexist with their existing environment seamlessly.
• Data Sensitivity: Organizations handling highly sensitive data (e.g., government agencies or defense contractors) may need to keep their data on-premises to minimize risks associated with external data storage.

Bottom Line: Cloud vs. On-Premises

The choice between cloud and on-premises infrastructure ultimately depends on the unique needs and priorities of each organization. Cloud computing offers scalability, flexibility, and cost savings, but it requires careful consideration of issues related to data security and potential vendor lock-in, to name a few. On-premises infrastructures provide more data control, customization options, and lower latency, but come with higher upfront costs and limited accessibility. The hybrid cloud approach can be an ideal solution for organizations seeking a balance between cost efficiency and data control. Ultimately, organizations should assess their specific requirements, compliance needs, budget, and long-term goals to determine the most suitable infrastructure model for their organization.

FAQ

What is the cloud?
The cloud refers to the delivery of computing services over the internet, allowing businesses to access and utilize resources such as storage, servers, databases, and software applications on-demand, without the need for physical infrastructure.

What does on-premises mean?
On-premises refers to hosting all hardware, servers, and applications within an organization’s own premises or data center, managed and maintained by its own IT staff.

What are the main benefits of the cloud?
The cloud offers several benefits, including scalability, cost savings, flexibility and accessibility, automatic updates, and streamlined disaster recovery options.

What are the benefits of on-premises infrastructure?
On-premises infrastructure offers complete control over data, lower latency due to the localization of IT resources, and more customization options.

Which is more cost-effective, the cloud or on-premises IT infrastructure?
The cost-effectiveness of the cloud versus on-premises infrastructure depends on various factors such as the size of the organization and workload demands, to name a few. The cloud offers cost savings in terms of upfront capital expenditure and ongoing maintenance, as organizations only pay for resources used. However, on-premises involves higher upfront costs but may result in greater long-term savings once the infrastructure is established.

Is the cloud less secure than on-premises?
Cloud providers implement robust security measures to protect data, including data encryption, access controls, and threat detection systems. However, organizations must also ensure proper configuration and adopt additional security measures to meet specific compliance requirements and protect their applications, data, and user access.

Which option is better for compliance and regulatory requirements?
Both cloud and on-premises infrastructures can be designed to meet compliance and regulatory requirements. Leading cloud service providers typically provide compliance certifications and attestations, whereas on-premises allows organizations to maintain full control over compliance by implementing customized security controls and monitoring processes.

Can I have a mix of cloud and on-premises infrastructure?
Yes, organizations can adopt a hybrid cloud approach that combines elements of both cloud and on-premises infrastructure. The hybrid cloud model allows organizations to leverage the scalability and flexibility of the cloud while maintaining sensitive data or critical applications on-premises.

What are some typical use cases for the cloud and on-premises infrastructure?
Cloud computing is suitable for startups and small businesses, organizations with fluctuating workloads, and collaboration and remote work environments. On-premises infrastructures may be more ideal for organizations that are operating in highly regulated industries, heavily reliant on legacy systems, or handling highly sensitive data.

Meanwhile, the ever-growing value of big data is driving data scientists to increase their access along with their ability to mine and analyze data for insights and information by adapting how data repositories are managed in relation to the type of data they house. While some legacy and on-premises solutions continue to be indispensable, a mass shift to the cloud is proving to be the answer to many of the problems organizations are facing in regards to data volume, compatibility, and accessibility.

Companies of various sizes and industries adapt to progress at different rates and may migrate data for different reasons. The five major trends in data migration in 2023 reflect the industry’s attitude as a whole toward solving specific problems.

1. A Shift Towards Data Lakehouses

Data lakehouses are open data management architectures that combine the flexibility, cost-efficiency, and scale of data lakes with the data management abilities of data warehouses. The result is a unified platform used for the storage, processing, and analysis of both structured and unstructured data. One reason this approach is gaining popularity is a sustained desire to break down data silos, improve quality, and accelerate data-driven decision-making within organizations.

Data lakehouses’ large capacity enables them to handle large volumes of data in real time, making them ideal for live consumer data, Internet of Things (IoT) networks, and physical sensors. Their ability to process data from multiple sources makes it easier for organizations to gain insights from multiple data streams.

Additionally, the centralization of data lakehouses allows for a unified, up-to-date view of data across an entire organization, facilitating inter-departmental collaboration on data-based projects and greatly reducing the costs and complexity of hosting multiple data storage and processing solutions.

2. A Focus on AI and Automation in Governance

Data migration helps organizations keep pace by ensuring their systems are able to accommodate the ever-increasing flow of new data. To simplify the already complex and time-consuming task of data governance, many companies are turning to artificial intelligence (AI)/machine learning (ML) algorithms and automation.

These technologies have revolutionized data migration by allowing organizations and data managers to automate some of the many manual processes it involves. It also enables them to reduce the risk of failures due to human error and execute the migration process more accurately and efficiently. With the help of smart algorithms, organizations can also better gain insights into their data than previously possible while identifying and eliminating data duplicates, which may reduce storage costs and improve performance.

Thanks to the recent boom in AI and ML-based technologies being developed and partially launched by a number of cloud computing giants, including Microsoft and Google, the role of such technologies in the more critical processes of data migration is likely to increase as the models become more and more sophisticated.

3. Expanding Storage Capacity

The world is expected to generate around 120 zettabytes of data in 2023, a nearly 24 percent increase from the prior year. This data is generated from a wide variety of sources, including IoT devices, log files, and marketing research. In this case, bigger is better—many organizations are looking to embrace big data by expanding storage capacities through novel methods of data storage.

One prominent option is cloud storage, which stands out as a scalable, reliable solution that’s also easily accessible over the internet. However, one of the challenges that arises with data migration to the cloud is maintaining security during transit. Organizations must carefully plan their migration strategies—including encryption, backup, and recovery plans—to protect financial and medical data and personal information while it is at risk.

Organizations can also benefit from an increase in agility and compounded value of structured and unstructured data by expanding their overall data storage capacity through flexible and scalable means.

4. Handling Unstructured Data

Most data sources produce semi-structured or unstructured data that cannot be easily organized and categorized. Company mergers and system updates are prominent sources of unstructured data—the initial categorization and structure of the data must be shed in order to fit into a different system. Unstructured data tends to be much larger in volume than structured data carrying the same amount of information and insights.

This poses a problem when migrating data. Not only is the massive volume costly to transfer and secure, both in-transit and at-rest, but it cannot be analyzed or stored in relational databases. However, that doesn’t make it void of value, as many organizations are seeking data science and migration solutions that would help structure incoming data.

Solving the unstructured data problem is a time-sensitive endeavor for many organizations. That’s because situational data quickly loses its value with time and gets replaced by more recent data, often in greater volume.

5. A Move From On-Premises Legacy Systems to Cloud Storage

Most data originates in the cloud, from such sources as digital logs, monitoring devices, customer transactions, and IoT devices and sensors. Many organizations are finding it more efficient to migrate entirely to the cloud rather than remaining split between legacy on-premises systems and cloud storage.

This approach would involve the integration of legacy data and systems with already-present data stored in the cloud, creating a more unified and comprehensive approach to data management and enabling remote access. A move to the cloud would also be accompanied by embracing multi-cloud architectures, allowing companies to optimize costs by working and switching between multiple cloud providers simultaneously.

Moving entirely to the cloud would also facilitate data storage segmentation, enabling data managers to differentiate data by type, purpose, and origin in addition to sensitivity and the level of security it may require. Organizations with data split between legacy and cloud systems may seek to unify the multiple sources in the cloud, enabling them to develop a richer, more holistic view of their data and how they might be able to use it.

Predictions for the Future of Data Migration

Data migration is expected to continue to grow in popularity alongside the exponential growth in the average volume of data produced annually by organizations. As businesses increasingly adopt cloud-based alternatives to everything from computing and processing to hosting software, cloud-based data solutions are likely to follow.

This will spark a wave of innovation, creating modern tools and technologies that aim to simplify the data migration process, ensuring the security and reliability of data in transit. Combined with the latest advancements in AI, ML, and automation, the migration process is likely to become faster, more efficient, and less prone to errors, making data migration as a concept more accessible to startups and emerging businesses who want to shift to the cloud and make the most out of their data.

One common business use is to improve customer service. In this context, a knowledge management system makes it easy to provide relevant and personalized information to customers and the staff who support them. This article looks at specific ways a business can use knowledge management systems to improve their customer service.

Eliminate Silos by Sharing Knowledge

A knowledge management system can help a business break down information silos that prevent different parts of the organization from having access to relevant information or being able to see more holistic views of customers and their interactions.

For example, information in the customer database is not available to the analytics system, or management collects sales data that is not made available to front line workers that spend their days contacting customers.

A knowledge management system implemented in a call center or customer service setting can eliminate these information silos using the following best practices:

• Consolidate knowledge repositories. Implementing systems that make it possible to unify knowledge repositories and databases will help keep all relevant information in a single system accessible by all.
• Adopt federated search. Consolidating data and providing federated search tools make it possible for front-line staff to search all data sources based on one query.
• Design systems from the point of service backwards. A customer-first approach will help ensure all customer data is available at each stage of their interaction with the company.

The easier it is for staff to find customer information, the easier it will be for them to provide high quality call responses and overall customer service.

Provide Consistent Information Across Channels

Call centers can no longer rely on a phone line for customer service. In this multi-channel world, customers looking for support expect online knowledge bases, social media access, chat tools, and more. This can pose challenges for organizations looking to provide consistent information that is optimized for viewing across all channels.

Businesses looking to implement knowledge management across multiple channels should:

• Deliver consistent multi-channel data. Users don’t want to have to repeat themselves by reentering data or explaining their issue multiple times at each stage of their interaction with customer service.
• Optimize content so it is viewable on any channel. Information might look different on a smartphone than on a web browser, and graphics-intensive sites might provide lousy user experience for low-bandwidth customers.
• Integrate all channels. Customer service agents should be able to seamlessly move among the different channels to provide a more seamless, unified customer response.

Some people prefer to call, some want to email, others would rather chat or post on social media. A knowledge management system can make it easier to accommodate all customers, regardless of their preference.

Improve Customer Service Responses

Customer service often depends upon a rapid, user-friendly response. Knowledge management systems can facilitate this by making data available rapidly, on a single screen if possible, with drill-down features that make further information available when necessary.

Businesses looking to speed up customer response with knowledge management should:

• Design systems to answer queries fast. Impatient customers won’t be forgiving of underpowered hardware or glitchy software.
• Provide a single dashboard or screen. Identify the key information to help serve customers quickly and summarize key customer data on a single, easy-to-read dashboard for customer service representatives.
• Include comprehensive drill-down features. When a representative needs more information about a customer or transaction, they should be able to get to it from the main screen without going into another system or location.
• Prevent unnecessary delays. Any additional steps or unnecessary information can result in customer frustration, dropped calls, and customer churn.

Callers expect quick answers based on the correct data. Doing everything possible to provide them with those answers is essential.

Increase Customer Self-Service 

Online knowledge bases may be giving way to artificial intelligence (AI) and chatbots in some cases, but they are not going away—and many of them are poorly designed or outdated. A knowledge management system can be used to help overhaul a business’s online knowledge base with the following steps:

• Enhance online search. Making it easy for users to find information quickly, without wading through endless documentation, will improve user experience and customer satisfaction.
• Devise good systems of taxonomy. Identify the information customers want and how they search for it, and then make it easy for those keywords and search terms to provide relevant results.

Customers are comfortable and familiar with online searches, and delivering bite-sized answers in an easy format can help improve their experience.

How to Design a Knowledge Management System for Customer Service

When designing or implementing a knowledge management system for the specific use of customer service, there are a few things to consider that will help ensure a better result.

Include Customer Service Representative Training

Organizations often focus their knowledge management efforts on the customer, but it must be a resource employees can use to better serve customers. When designing the system, incorporate training modules, use the knowledge base as a training aid during calls, and make it easy for representatives to find the data they need.

Without well-trained agents, any knowledge management system will flounder. Ensure the system serves both customers and agents, especially those learning the trade. Knowledgeable agents provide the best service.

Involve Customer Service Representatives in the Design Phase

One of the flaws of software design is that programmers don’t always understand or take the time to discover the needs of system users. When designing or implementing a knowledge management system, make sure that the system meets the needs of those front-line workers who will use it. Gain their input, let them try out the system at various stages in the build, and find metrics that align with their duties.

Integrate Related Systems

Knowledge management, Customer Relationship Management (CRM), contact center and key sales or management systems should not be separate islands within the enterprise. Avoid systems that are difficult or costly to integrate in favor of platforms that can easily fit into existing infrastructure. A centralized knowledge hub should align fully and integrate well with all other key customer facing systems.

Incorporate Automation

Some call centers use automated voice response systems to reduce call volume, but automation can also be used to deliver better customer service. Implementing response chat systems that provide easy call turnovers to customer representatives can prevent long wait times and boost caller satisfaction. Implement chat systems that provide useful answers rapidly, ensure the system knows when to refer the customer to an agent, and provide a call-back option within a specified time.

Add Artificial Intelligence

AI systems like ChatGPT can be introduced into customer service to forward the mission of enhancing overall customer experience. For example, Natural Language Processing (NLP) AI can help interpret user intent rather than expecting users to know the right keywords to get the answer they need. NLP even takes into account industry-specific terminology, different languages, and special content like product names. Self-learning search engines continuously learn from every interaction to deliver increasingly accurate and targeted results.

AI and Chat are big advances, but they are tools and must always be fitted to a definite business purpose if they are to improve the customer experience. Seek out AI tools geared to vertical markets that would be better-suited to the needs of the specific audience.

Bottom Line: Using Knowledge Management Systems to Improve Customer Service

The modern customer is far different from those of even a decade ago. Knowledge management systems must be adjusted to cope with current needs by providing integrated, multi-channel systems that serve data in the format needed by agents and customers. Considering both customer and customer service representative needs when designing and implementing a system can help improve customer service and customer satisfaction while making staff more efficient and more effective.

To drive growth and maintain a competitive edge, organizations are continuously adopting new technologies to survive in today’s fast-paced digital ecosystems. Automation has emerged as a powerful tool for achieving these ends, enabling firms to streamline processes, enhance efficiency, and promote innovation. By leveraging automation, they can optimize their operations, reduce human error, and unlock new possibilities for growth.

This article explores automation—its benefits, implementation strategies, and its transformative potential across various industries.

What is Automation?

Automation refers to the process of automating repetitive tasks, workflows, and processes within an organization. It involves using software tools and technologies to replace manual, time-consuming activities with automated processes that require minimal human intervention. Automation can encompass a wide range of activities across different departments. For example, IT automation deals with automating server provisioning, software deployment, network configuration, system monitoring, and data backups, whereas customer service automation may involve using chatbots and other automation technologies to manage large caseloads.

Regardless of the department or organizational function involved, the primary goal of automation is to improve operational efficiency, reduce costs, enhance productivity, and increase service quality levels. By automating routine tasks, organizations can free up their staff’s time to focus on more strategic initiatives, such as innovation, problem-solving, and improving customer experiences.

The Role of AI in Automation

Artificial Intelligence (AI) is at the forefront of prominent innovations in automation, and the presence of AI-powered automation is ubiquitous across domains and industries. However, organizations in practice have been using automation for years, long before AI and machine learning (ML). For example, enterprises in heavy industries and manufacturing have been using automation technologies like assembly lines and mechanical robots to streamline their operations for decades. Infrastructure automation in IT involves the use of specialized software to deploy cloud-based servers and networks automatically, also without the use of AI.

That said, AI is a force multiplier for these organizations’ automation efforts. In the case of manufacturing, enterprises these days are using AI/ML-based diagnostic tools and digital twins to anticipate and remediate issues before they turn into business disruptions and failures. AI-based solutions also allow manufacturers to glean critical insights for optimizing processes and workflows. Similarly, the advent of AI has allowed IT departments to integrate infrastructure automation tools with intelligent agents to build automated fault tolerance into their environments and applications.

Automation Benefits By Category

Automation provides myriad benefits across a wide range of industries, but for most enterprises, the biggest draw is cost savings—specifically, reducing costs associated with manual labor, human errors, and system downtime. By minimizing the need for human intervention, businesses can lower labor expenses and redirect resources to higher-value activities. Additionally, automation can improve resource utilization, optimize system performance, and reduce the risk of costly errors or outages.

Operational Excellence Through Automation

Automation can help enhance efficiency and productivity by eliminating the need for manual intervention in repetitive tasks, allowing organizations to achieve greater efficiency and productivity. By automating processes, businesses can accelerate workflows, minimize errors, and ensure consistent results. This in turn results in faster response times, smoother operations, and increased overall productivity. However, in order to maintain sustainable growth through innovation, firms must achieve operational excellence to remain competitive.

IBM defines operational excellence as “an approach to business management that emphasizes continuous improvement across all aspects of the business and within all business processes by creating a culture where management and employees are invested in business outcomes and empowered to implement change.”

In the pursuit of operational excellence, automation also promotes a culture of continuous improvement and learning. As teams automate repetitive tasks, they gain insights into process inefficiencies, bottlenecks, and potential optimization opportunities. This knowledge can drive process reengineering efforts, leading to further efficiency gains and innovation.

Automating Reporting

Across all industries, teams rely on accurate and timely reporting to maintain continuous situational awareness regarding their respective projects and initiatives. To this end, reporting can be automated by pulling data from multiple systems, consolidating it, and presenting it in a predefined format. For example, sales and marketing departments can leverage robotic process automation (RPA) to assemble data from various systems—including Customer Relationship Management systems, marketing automation platforms, and social sentiment and analytics platforms, to name a few—merge the results into a consolidated report, and automate the report’s delivery to relevant stakeholders on a regular basis.

Security and Compliance Automation

Automation plays a crucial role in strengthening security and ensuring compliance with industry regulations. By automating security measures and functions like intrusion detection systems, vulnerability scanning, and log analysis, organizations can identify and respond to threats quicker and more effectively. Furthermore, automation helps organizations maintain consistent security controls and enforce compliance policies across their IT infrastructure.

Automating Integrations

In order to keep up with shifting business demands and requirements, organizations must remain agile and flexible with their tools and processes. However, in many cases these tools and processes are unable to scale accordingly or lack newly-required features. Automation can facilitate seamless integration between different systems and applications within an organization’s infrastructure as well as provide mechanisms to easily integrate with other systems and tools. This interoperability promotes data sharing, collaboration, and the development of interconnected ecosystems that drive digital transformation.

Robot Process Automation (RPA)

Robotic process automation describes technology that enables organizations to automate repetitive and rule-based tasks through the use of software robots. These robots mimic human interactions using software algorithms, allowing them to perform tasks such as data entry, data extraction, and process automation. RPA is designed to work across various applications and systems, interacting with user interfaces, performing calculations, and making decisions based on predefined rules.

Use Cases For Automation

Rendering Financial Decisions

As the underlying mechanics of AI are mathematical, it’s not surprising that banks and financial institutions use machine learning and AI algorithms to analyze large volumes of financial data to detect patterns, predict market trends, and automate trading processes. On the consumer and B2B front, banks are incorporating automation into their lending practices to provide advanced financial analytics and credit assessments. AI allows firms to service consumers and businesses with solutions better suited to their unique situation and requirements.

Improving Customer Service and Support

Customer service organizations are using automation to streamline support processes by automating repetitive tasks such as responding to frequently asked questions, generating standard email responses, or updating customer records. By enhancing the efficiency and effectiveness of customer support operations with AI, firms stand to improve response times and enhance customer satisfaction.

Automating Insurance Processes

The insurance industry was one of the early adopters of automation and has pioneered many applications of AI across the enterprise. For example, AI is crucial for helping risk and insurance professionals develop more comprehensive policies for better claims and underwriting outcomes. On the customer service side, many insurance carriers have automated the claims management process, from initial reporting to settlement.

Automating Retail Operations

According to McKinsey, almost a third of all sales and sales operations tasks can be automated with today’s technologies. And judging by the current AI adoption levels  among sales organizations, this percentage is certainly on the rise. AI automation is used in retail for inventory management, demand forecasting, and personalized marketing. Recommendation systems leverage AI algorithms to analyze customer preferences and browsing history, enabling personalized product recommendations. And in physical storefronts, robots and automated checkout systems are now being employed to streamline in-store operations.

Optimizing Shipping and Logistics

Automation plays a crucial role in shipping and logistics by optimizing route planning, managing fleet operations, and improving supply chain efficiency. Firms can use AI algorithms to analyze data from various sources like weather conditions, traffic patterns, and shipment details to make real-time decisions and improve delivery schedules.

Bolstering Accounting Data Integrity

For accounting departments, automation eliminates the risk of manual mistakes, ensuring accuracy and reliability in routine tasks. By enforcing standardized processes, organizations can achieve consistent results and reduce the likelihood of data integration errors that can lead to delays and costly fixes. For example, accounting and finance professionals are now using RPA to automatically extract data from invoices and input it into the organization’s accounting software, eliminating the need for manual data entry and reducing errors.

Streamlining IT Operations

IT teams these days more often use automation tools for  automatically rolling out IT infrastructure components. This modern approach is preferred over setting up and configuring servers and IT infrastructure manually, especially when it comes to cloud-native environments.

Popular IT automation tools like Ansible, Puppet, and Chef provide a mechanism for managing and scaling complex infrastructures effectively. They bring consistency, repeatability, and control to configuration management, resulting in improved efficiency, reduced errors, enhanced security, and streamlined operations.

Forecasting Energy and Utilities Usage

According to recent statistics, the global AI energy market is anticipated to top $7.7 billion by 2024. The energy sector is applying AI-powered automation technologies to myriad different industry processes, leading to improved worker safety levels and streamlined emergency response efforts. On the inventory side, firms operating in the energy sector are using automation for demand forecasting and energy optimization. Energy providers are using AI algorithms to analyze historical data to predict energy consumption patterns, optimize energy distribution, and identify potential equipment failures, leading to cost savings and improved reliability.

Bottom Line: Automation

Automation is a powerful enabler for businesses seeking to optimize their operations, reduce costs, and drive innovation. By eliminating repetitive tasks and streamlining workflows, organizations can achieve enhanced efficiency, productivity, and accuracy. The benefits of automation span cost reduction, improved reliability, scalability, security, and compliance. As technology continues to advance, the scope and impact of automation will no doubt follow suit. Organizations that embrace automation will position themselves for success in an increasingly competitive and dynamic business environment, where efficiency, agility, and innovation are key to thriving, not just surviving, in today’s business landscape.

What is a digital twin?

A digital twin is a virtual model designed to accurately represent a physical object. Essentially, digital twins are rich virtual environments that can be used for simulations, testing and monitoring without making changes to the actual physical object.

These objects use data obtained from the physical object being modeled. As an example, a digital twin of a jet engine could be created by analyzing data obtained from sensors placed on different parts of the engine and from existing related knowledge. Information about the engine’s performance, energy consumption, the impacts of weather conditions and other factors could help to create an accurate digital twin for use in simulations.

Digital twins are not the same as simulations. Both use digital modeling to study the processes of physical entities, but digital twins offer a much deeper potential for analysis. Essentially, digital twins can operate at scale, modeling multiple simulations at a time, whereas a simulation usually focuses on a single process. Digital twins can also use real-time data, another significant improvement over a single simulation model. They are effectively a more modern iteration of simulation design work.

Types of digital twins

The concept of a digital twin is relatively broad and encompasses four specialized types across different industries: component twins, asset twins, process twins and system twins.

Component twins

Component digital twins represent individual parts of systems or products—for example, a single hinge or gear. This type of digital twin technology can model key components more likely to be subject to stress or factors like extreme heat. By digitally modeling integral parts at risk of failure, analysts can examine how they might improve their integrity by subjecting them to open-ended simulations. Being able to estimate the upper limits of parts (and figuring out how to improve them) can lead to significant improvements in terms of how long products might last and to safety improvements for operators.

Asset twins

Asset twins are sometimes called product twins. In a way, asset twins are the flip side of component twins, as they are virtual representations of an entire physical entity rather than its individual parts. The main function of an asset twin is to help engineers and designers develop an understanding of how individual parts work together within a single product. For example, an asset twin modeled on a car or truck could help automotive engineers pinpoint potential failure points based on mileage, weather conditions and other factors.

System twins

System twins are sometimes called unit twins. These virtual representations replicate entire systems of products working together. These twins build on the asset twin approach, which looks at how individual parts work together within an entity by modeling individual entities within larger systems. For example, a system twin might focus on several identical machines linked together on a factory floor. Factory design engineers could study a system twin to determine the best workflow to improve individual machine integrity, improve productivity and efficiency and identify problems that could impact the entire floor.

Process twins 

Process twins zoom out even further than system twins. These digital twins look at how systems work together. For example, while system twins might study a single manufacturing line or a single factory floor, process twins can also pull in systems like staffing, multiple factory locations, the supply chain and more.

Learn more about the top digital twin solutions for 2023.

Digital twin benefits

What are the benefits of digital twins? The overarching benefit of this technology is the ability to model potential scenarios in a way that would be much more costly and difficult with physical objects.

Four specific benefits of digital twins include:

1. Performance and efficiency improvements
2. Resource savings (including financial resources)
3. User satisfaction as a result of improved product performance and integrity
4. Remote collaboration possibilities (analysts from across the globe can collaborate with virtual modeling in shared locations)

Digital twin applications

While digital twin technology has the potential to benefit a virtually endless list of applications, several industries stand out among the most common digital twin adopters. They include the following:

• Aerospace
• Automotive
• Construction
• Defense
• Energy
• Government
• Infrastructure
• Manufacturing
• Telecommunications
• Retail

Each of these fields employs digital twin technology to model the potential impact of both expected and unexpected occurrences on performance, though the preferable outcomes may differ by application. Governments can use digital twins to determine where to invest in the local economy to spur growth, for example, while telecommunications firms might use digital twins to make decisions about where to add hubs. Any industry that moves goods through the supply chain can make good use of digital twins to estimate the impact of environmental factors and consumer behavior.

Getting started with digital twin technology

McKinsey suggests a three-step process for developing a digital twin:

1. Create a blueprint—essentially, develop a framework based on the desired end result of the digital twin and determine the scope and scale needed.
2. Build an initial digital twin—for many companies and individuals, this will mean connecting with an outside firm or working closely with a specialized IT unit.
3. Boost the digital twin’s capabilities—once developed, work to strengthen the digital twin’s capabilities by adding more sensors and other information-gathering processes.

Bottom line: Digital twins

Digital twins can offer deep insight for analysts, developers, engineers and other professionals across a wide range of industries. These virtual representations of physical entities (including physical objects and larger systems like cities and regions) empower these professionals to model scenarios and estimate their impact on performance, longevity, efficiency and other outcomes. Building a digital twin requires specialized computer skills and dedicated company resources, but the benefits over field testing or single-simulation approaches are tremendous.

Competing providers offer a range of features and prices for businesses considering a low-code platform. This article will compare the top nine platforms and explain the features that differentiate them, making it easier to match them to specific needs.

Table Of Contents

• Top Low-Code Platforms
• Low-Code Platforms Comparison Table
  • Best For Visual Modeling: Appian
  • Best For Customization: Claris FileMaker
  • Best For Pre-Built Templates: Studio Creatio
  • Best For Business Intelligence (BI): Microsoft Power Apps
  • Best For Integration: Mendix
  • Best For Deployment Optimization: Outsystems
  • Best For Oracle Database Users: Oracle Application Express (APEX)
  • Best For Analytics And Reporting: Salesforce Lightning
  • Best For User Accessibility: Zoho Creator
• Key Features Of Low-Code Platforms
• How To Choose What Low-Code Platform Is Best For You
• Bottom Line: Top Low-Code Platforms

Top Low-Code Platforms

The top nine low-code platforms all offer feature-rich plans at varying prices. When selecting a provider, match the use cases and costs outlined below to the specific needs for the platform.

Low-Code Platforms Comparison Table

Best Low-Code Platform For Visual Modeling: Appian

Appian is one of the pioneers of the low-code movement and offers users a robust platform that goes beyond basic scripting to build full, complex applications with complete business logic.

Pricing

Appian does not provide pricing information on its website beyond offering a free trial. Contact sales for a quote based.

Features

• Robotic Process Automation (RPA): RPA is used to execute automated actions, responses and processes and is a core part of the platform.
• Complex Business Process Management (BPM): Non-developers can easily build BPM workflows.
• Integrated Developer Environment (IDE): IDE is included, enabling extensibility for custom coding when needed.

Pros

• Strong user management
• Easy for most users
• Strong customer support

Cons

• Limited documentation management
• Difficult to implement
• Limited reporting tools

Best Low-Code Platform For Customization: Claris FileMaker

A division of Apple Inc., Claris FileMaker takes a template-driven approach to help users quickly build data-driven applications. The low-code platform aims to improve the performance of a company’s custom apps, improve scalability with more hosting capacity and protect data for security protocols.

Pricing

Claris FileMaker offers two available pricing plans. The Essentials plan costs $21 per user, per month, billed annually. The standard plan costs $43 per user, per month, billed annually. The vendor offers a 45-day free test drive.

Features

• Graphical User Interface: Claris FileMaker has a graphical user interface for custom application design layouts for any type of device.
• Customizable: Customized workflows are based on job roles.
• Creates Workflows: The drag-and-drop interface makes it easy to build apps and connect different components to create application workflows.

Pros

• Customizable interface
• Multi-user support
• User-friendly database management

Cons

• Limited character count
• Difficult migration
• Limited automation capabilities

Best Low-Code Platform For Pre-Built Templates: Studio Creatio

Formerly known as BPM’online, Creatio is well suited for low-code development of Business Process Management (BPM) applications. Studio Creatio’s pre-built templates provide unlimited customization and give users the ability to create ready-to-use applications.

Pricing

Studio Creatio offers users a free trial of its Studio enterprise plan; the website does not specify how long the free trial lasts. If a company decides to move forward with the plan, it costs $25 per user, per month. Creatio also has a plan for a free Process Designer tool.

Features

• BPM And CRM: Creatio has a strong focus on BPM and Customer Relationship Management (CRM) enablement.
• Artificial Intelligence (AI) Assistance: AI is an embedded part of the platform, providing assistance and direction for low-code application development.
• Pre-made Templates: The template-driven approach with drag and drop interface simplifies application development.

Pros

• Manages marketing plans
• Strong data visualization
• Intuitive and flexible

Cons

• Limited automatic calculations
• Difficult integration
• Limited customization

Best Low-Code Platform For Business Intelligence (BI): Microsoft Power Apps

For businesses already using Microsoft tools, including Power BI (Business Intelligence), Power Apps is an obvious choice for building out low-code tools that leverage the Microsoft platform. Power Apps’ BI features are  especially helpful for building user friendly reports with company data.

Pricing

Microsoft Power Apps offers both subscription and pay-as-you-go plans. The two subscription plans are on a per app basis at $5 per user, per app, per month. The per user plan is $20 per user, per month. The pay-as-you-go plan is $10 per active user, per app, per month.

Features

• Flexible Integration: Power Apps has available integrations including Microsoft’s Power Automate and the Common Data Service which can work seamlessly with Power BI for analytics.
• AI Builder: Template-driven AI models in the AI Builder module enable citizen developers to build complex workflows.
• Customization: Extensibility is a key part of the platform, with the ability to use custom connectors or make use of the Microsoft Azure Functions service.

Pros

• Pre-build formatting
• User friendly
• Strong integration

Cons

• Limited graphical representation
• Complicated licensing
• Limited options to change code

Best Low-Code Platform For Integration: Mendix 

Mendix, a subsidiary of Siemens, is a solid choice for enterprise developers that use SAP software because of the deep partnership between the two firms. Mendix is a helpful choice for those looking for a service that can work with a private cloud deployment as well. Low-code platforms need integration for their data. Mendix provides an integrated collaboration platform to help companies with their development.

Pricing

Mendix offers four plans: free, basic, standard and premium. Basic pricing starts at $60 per month, Standard at $950 per month. For a premium plan, companies should reach out to Mendix’s sales team.

Features

• Integrated Collaboration: As part of the developer portal, integrated collaboration can bring together multiple users to help build low-code applications.
• Multiple Tools: The product’s deep toolset can help with the initial design and goals for a given application.
• AI Assistant: AI assistant helps guide developers on the right actions to take to ensure optical application development and deployment.

Pros

• Scalable
• Flexible development
• Easy visual coding

Cons

• Expensive licenses
• Slow implementation
• Difficult for beginners

Best For Deployment Optimization: Outsystems

The Outsystems platform provides a full workflow and testing capabilities well suited to teams within organizations to build and operate low-code applications. Outsystems assists developers with mobile development, which is a key part of a low-code platform.

Pricing

Outsystems offers three plans. The single-app plan is free. A multiple-app plan starts at $1,513 per month after a free trial. The company also offers a large app portfolio plan; contact sales for a quote.

Features

• Optimized Applications: A key differentiator for Outsystems is its impact analysis, testing and staging platform capabilities, which enable users to build optimized applications.
• Integrated Performance Analysis: The platform’s integrated performance analysis enables users to monitor and improve the performance of applications.
• Strong Deployment: Outsystems generates app code that can run in the Outsystems cloud or any private or public cloud deployment, and provides a simplified approach to package and submit mobile applications to Google Play and the Apple App Store.

Pros

• Intuitive flow editor
• Strong integration
• Flexible platform

Cons

• Expensive platform
• Unreliable management
• Limited customization

Best For Oracle Database Users: Oracle Application Express (APEX)

For organizations already using Oracle’s database and cloud technologies, APEX is an obvious fit, enabling low-code application development for data-driven applications and use cases.

Pricing

Oracle APEX does not list prices online. Users can request a free workspace to try out the platform.

Features

• Charts Data: Charting capabilities for data visualization within apps is an area of differentiation for APEX.
• Unique REST API: Beyond just data applications, APEX enables users to build their own REST APIs that can be used to enable other applications and services.
• Universal Applications: Universal theme user interface development in APEX lets citizen developers easily generate applications that will work across different types of desktop and mobile screens.

Pros

• Simple deployment
• Easy configuration
• Rapid development

Cons

• Limited CSS (Cascading Style Sheets) editing
• Limited documentation
• Oracle Database dependency

Best Low-Code Platform For Analytics And Reporting: Salesforce Lightning

Salesforce Lightning provides a low-code environment as part of the Salesforce Platform. It’s a great option for existing Salesforce CRM users and can also benefit those just looking for an easy-to-use subscription-based service. Salesforce Lightning’s additional tools help companies with analytics and reporting data for app development.

Pricing

Salesforce Lightning has two plans. The Platform Starter plan costs $25 per user, per month, billed annually. The Platform Plus plan costs $100 per user, per month, billed annually. Both plans have a free trial.

Features

• Lightning Flow: Business logic capabilities are built-in via the Lightning Flow feature for BPM.
• Mobile Deployment: All apps built with Salesforce Lighting are by default also enabled for mobile device deployment.
• Salesforce’s Heroku: For more complex application needs, low-code can be complemented with Salesforce’s Heroku Platform-as-a-Service, which provides traditional programming language support.

Pros

• Ability to view the status of sales
• Strong analytic features
• Positive integration

Cons

• Difficult API integration
• Not for beginners
• Limited customization

Best Low-Code Platform For User Accessibility: Zoho Creator

Zoho Creator provides an easy-to-use interface for building both simple and complex applications and can be particularly useful for building multi-device applications. Zoho Creator’s drag-and-drop elements help users easily create apps for iOS and Android.

Pricing

Zoho Creator has many pricing options, all based on an annual subscription. The yearly standard subscription costs $8 per user, per month; the yearly professional subscription costs $20 per user, per month; the yearly enterprise subscription costs $25 per user, per month. A monthly plan is also available, and costs $12-$37 per user, per month, based on features. Zoho also offers a 15-day free trial to test the platform.

Features

• User Access Controls: A key differentiator for Zoho Creator is its well-defined user access controls, which include audit trails as well as single sign-on capabilities.
• Converge Data: The ability to simply converge data from different sources to create a visual dashboard is a core use case.
• Data Integration: Form digitization is another key feature of the platform, with templates that help users to build different types of digital forms that can integrate multiple forms of data including barcodes.

Pros

• Live data tracking
• Simple reporting and analytics
• Workflow and automated notification

Cons

• Limited customization
• Limited documentation
• Difficult search feature

Key Features Of Low-Code Platforms

Low-code platforms help companies by allowing business professionals and IT professionals to collaborate during the coding and application development process. Non-technical professionals can provide insight and business requirements, while IT can look over the coding to help support the development process. This feature saves companies time and increases productivity and efficiency.

Other key features to look for in low-code platforms:

• Pre-Built Templates: The ability to use pre-built templates will benefit companies across their entire infrastructure..
• Drag-and-Drop Interfaces: Drag-and-drop interfaces can simplify development and save time.
• Accessibility: Building an application using low-code can improve accessibility by designing apps that can be accessed on mobile devices, the cloud and on-premises platforms.
• Scalability: A company may be growing while using a low-code platform, and if they are, scalability is necessary to the platform.
• Visual Modeling: Low-code platforms often have visual modeling features and use built-in modules to help anyone understand the platform, whether a beginner or expert developer.

For more information: Trends in Low-Code/No-Code

How To Choose What Low-Code Platform Is Best For You

There are several criteria to consider when evaluating low-code platforms.

Fit For Purpose

As is the case with any development tool, low-code or otherwise, different tools are sometimes optimized for specific use cases and even specific platforms. The first step should be to figure out what the company aims to do with the platform.

Explore Templates 

Many tools provide pre-built templates for different types of applications. Make sure the platform provides what is needed to help accelerate the specific type of development a given project requires.

Extensibility With ‘Full’ Code

While low-code tools can be used to build full applications, there are some capabilities that might require traditional coding extension. If a larger application is the goal, ensure the platform can generate code that can be used in other tools.

Cost And Licensing

Increasingly all forms of software are sold on a subscription model, while some are still sold on more traditional licensed models. Determine which model is the best fit for the use case. It might be more effective to pay for a limited subscription for a small project rather than pay for a lifetime license.

Try It Out

Many platforms offer limited trials. It can be helpful to see how a low-code platform works with specific needs before committing to it.

Bottom Line: Using Low-Code Platforms

When considering providers and solutions of low-code platforms, it’s important to weigh features and cost against a company’s primary goals, including deployment, analytic needs, and customization. While each provider and solution offers a variety of features, identifying a company’s own use case can help better evaluate them against specific needs.

For more information on Low-Code: The Future of Low Code No Code

The need for enterprise-grade data storage has propelled the global public cloud market skyward. It is expected to almost double from $445 billion to $988 billion between 2022 and 2027. The richness and diversity of the market can make it daunting for organizations looking to upscale and upgrade their services.

Here’s a brief guide to some of the leading providers of public cloud solutions and how to choose the right provider for specific business needs.

Best Public Cloud Providers:

Amazon Web Services (AWS)

Amazon subsidiary Amazon Web Service (AWS) emerged in 2006, revolutionizing how organizations access cloud computing technology and remote resources. It offers a vast array of resources, allowing it to design and execute new solutions at a rapid pace to keep up with the global market’s evolution.

AWS’s services range from Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) to the simplified and easy-to-access and use, Software as a Service (SaaS) cloud models. Key offerings include:

Amazon EC2

Amazon Elastic Compute Cloud (EC2) is a web service that delivers secure and scalable computing capacity based in the cloud designed to facilitate web-centric computing for developers. This allows them to obtain and configure capacity with minimal friction with the infrastructure.

The services are available in a wide selection of instance types, from public to private and hybrid, that can be optimized to fit different use cases.

Amazon S3

Amazon Simple Storage Service (S3) is an object-based storage service known for its industry-leading scalability, security, performance and reliable data availability. Organizations of various sizes and industries can use it to store and retrieve any amount of data at any time, providing easy-to-use management features in order to organize data and configure it finely-tuned access control.

Amazon RDS

Amazon Relational Database Service (RDS) simplifies the setup and operations of relational databases in the cloud. AWS is responsible for automating all the redundant and time-consuming administrative tasks, such as hardware provisioning, database setup and data backup and recovery. This is best used to free up developers’ time, allowing them to focus on more pressing tasks like application development and design.

Use Cases and Industries

As a multinational corporation, AWS is able to cater to a wide variety of industries at different stages of development, from startups to established enterprises, as well as the public sector.

Use cases include:

• Application hosting
• Data processing
• Data warehousing
• Backup and restoration

This makes AWS’s service particularly useful for data-intensive industries such as healthcare, telecommunications, financial services, retail, and manufacturing.

Microsoft Azure

Microsoft launched Azure in 2010 as a comprehensive suite of cloud-based services designed to help businesses and organizations navigate the challenges that come with digital adoption. Azure was built on Microsoft’s decades-long specialty—software design—allowing its public cloud solutions to integrate seamlessly with other Microsoft products.

Azure also includes a multitude of services that range from computing and database management to storage and machine learning, including the following:

Azure Blob Storage

Azure Blob Storage is an object-based and scalable storage platform used for data lakes, warehouses and analytics as well as backup and recovery. It’s optimized for massive amounts of unstructured data, like text or binary values.

Azure Cosmos DB

Azure Cosmos DB is a database management service that’s multi-modeled, globally distributed and highly scalable, ensuring low latency that supports various APIs to facilitate access. It supports data models including SQL, MongoDB, Tables, Gremlin and Cassandra.

Azure Virtual Machines

Azure’s Virtual Machines are on-demand, scalable resources that provide users the flexibility of virtualization without the need to invest in or maintain the infrastructure that runs it. They also run on several Microsoft software platforms, supporting numerous Linux distributions for a more versatile experience.

Use Cases and Industries

When combined with Microsoft’s software and enterprise-focused approach to the public cloud, Microsoft Azure’s comprehensive services make it the ideal solution for numerous use cases, such as:

• Big data and analytics
• Application hosting
• Disaster and backup recovery
• IoT applications

Azure’s services are used by businesses and organizations in a number of industries such as e-commerce, healthcare, insurance and financial institutions.

Google Cloud Platform (GCP)

First launched in 2011 as a cloud-based subsidiary of Google, Google Cloud Platform (GCP) is a suite of cloud computing services that uses the same infrastructure as Google’s software products. Its industry-leading creations from TensorFlow and Kubernetes are some of the greatest examples of Google’s sophisticated solutions, and include the following:

Google Cloud Engine

Also known as Google Kubernetes Engine (GKE), Cloud Engine is a fully managed, user-ready environment used to deploy containerized applications and web services. Based on the open-source Kubernetes system, it’s developed by Google for managing workloads, enabling developers to flexibly and efficiently develop apps and deploy applications.

Google Cloud Storage

Google Cloud Storage is a fully managed and scalable object-oriented storage service. It includes many services ranging from serving website content to storing data for archival purposes and disaster recovery.

Google Compute Engine

Google Compute Engine is a cloud-based virtual machine solution that’s scalable and flexible. It allows users to tailor their computing environment, meeting specific requirements, and offering flexible pricing and cost savings.

Use Cases and Industries

GCP is used by organizations and businesses in IT, healthcare and retail, as well as the financial industry. Use cases include:

• Data analytics and machine learning
• Application development
• Storage and database management

IBM Cloud

IBM launched IBM Cloud in 2011 as a collection of cloud-based computing services. It leverages IBM’s vast experience, offering a robust approach to enterprise-grade public cloud platforms with an emphasis on open-source technologies and supporting a diverse set of computing models, including the following:

IBM Cloud Functions

IBM Cloud Functions is IBM’s Function as a Service (FaaS) solution built on Apache OpenWhisk. It enables developers to execute code in response to events as well as direct HTTP calls without having to manage their own hardware infrastructure.

IBM Cloud Virtual Servers

These flexible and scalable cloud computing solutions support both public and dedicated virtual servers. They’re the right balance of computing power to cost, allowing companies to deploy the servers globally and reach their customers.

IBM Cloud Databases

IBM Cloud Databases is a family of managed, public databases that support a wide variety of data models that include relational, key-value, document, and time-series applications.

Use Cases and Industries

IBM Cloud services a wide range of industries with its diverse offerings, such as IT and technology companies, healthcare organizations, financial institutions and retail providers, as well as the public sector. Use cases include:

• Public and hybrid cloud implementation
• Blockchain development
• Data analytics and management
• AI and machine learning

Oracle Cloud Infrastructure

The Oracle Cloud Infrastructure is a part of Oracle’s comprehensive cloud offering, first launched in 2012. The public cloud solution leverages Oracle’s long history in enterprise computing and data processing, enabling the company to provide robust, scalable and secure services, including the following:

Oracle Cloud Storage

Oracle Cloud Storage is a high-performance, scalable and reliable object storage service. It’s capable of storing an unlimited amount of data of any content type, including analytic data and rich content like images and video.

Oracle Cloud Compute

Oracle Cloud Compute encompasses a variety of cloud computing options set to meet the needs of small-scale applications to enterprise-grade workloads. It’s available as both bare metal and virtual machine instances, giving users a flexible, scalable environment for running applications.

Oracle Cloud Functions

Oracle’s Function as a Service (FaaS) offering lets developers write and deploy code without worrying about underlying infrastructure. It’s based on the open-source Fn Project and allows developers to build, run, and scale applications in a fully managed serverless environment.

Use Cases and Industries

With its versatile offerings, Oracle Cloud Infrastructure is able to serve a wide range of industries such as application development, insurance, healthcare and e-commerce in both the private and public sectors. Use cases include:

• High-performance computing (HPC)
• Enterprise resource planning (ERP)
• Data backup and recovery
• Data analytics

Alibaba Cloud

Launched in 2009, Alibaba Cloud is the cloud computing faction of the Alibaba Group. As the leading cloud provider in China and among the top global providers, Alibaba Cloud capitalizes on Alibaba’s massive scale and experience with e-commerce and data processing. Services include the following:

ApsaraDB

ApsaraDB is a suite of managed database services that cover a wide range of database types including relational, NoSQL and in-memory databases. These services handle database administration tasks, allowing developers to focus on their applications rather than database management.

Alibaba Object Storage Service

Alibaba Object Storage Service (OSS) is an easy-to-use service that enables users to store, backup and archive large amounts of data in the cloud. It is highly scalable, secure, and designed to store exabytes of data, making it ideal for big data scenarios.

Alibaba Elastic Compute Service

Alibaba Elastic Compute Service (ECS) provides fast memory and flexible cloud servers, allowing users to build reliable and efficient applications with ease. ECS instances come in a variety of types, each optimized for certain workloads, making them versatile for different application scenarios.

Use Cases and Industries

In essence, Alibaba Cloud’s extensive services, coupled with its strong presence in Asia, make it a compelling choice in the public cloud market. It also serves a multitude of data-heavy industries such as technology companies, media and entertainment, financial services and education. Use cases include:

• E-commerce platforms
• Big data analytics and processing
• AI and machine learning models

Emerging Public Cloud Providers

The booming market and demand for public cloud have opened the doors for numerous technology companies to start offering their own cloud computing and storage solutions. The focus of emerging cloud providers tends to be on providing straightforward, scalable, and affordable cloud services to small and midsize businesses, and key players in addition to the ones covered in this article include DigitalOcean, Linode and Vultr. All offer developer-friendly features at affordable rates alongside high-quality customer service and support.

Factors to Consider When Choosing a Public Cloud Provider

When choosing a provider of public cloud solutions, there are several factors to consider.

Scalability and performance

The cloud service provider must be able to handle workloads and be able to accommodate growth and changes as business grows.

Security

Providers must be compliant with local and federal data security and privacy regulations. Additionally, they should be able to protect data against attacks, leaks and breaches.

Pricing flexibility

Cloud services are most known for their flexible, pay-as-you-go pricing models. Multiple tiers at varying costs allow businesses to access only the resources they need.

Integration and customer service

A public cloud solution should be compatible with existing and legacy systems, ensuring seamless integration, and should include reliable customer support and service to ensure access to solutions and assistance.

Bottom Line: Public Cloud Providers

The public cloud market offers a diverse range of options, each with its own strengths and trade-offs. AWS, Microsoft Azure, GCP, IBM Cloud, Oracle Cloud Infrastructure and Alibaba Cloud are major players, each serving a multitude of industries with a broad array of services. Simultaneously, emerging providers offer compelling alternatives, especially for certain use cases or customer profiles.

When choosing a provider, considerations over scalability, performance, security, cost, integration and support are key. By understanding these factors, businesses can make informed decisions and choose the public cloud provider that best meets their specific needs.

The marketplace of data science tools approach tasks in remarkably different ways and use different methods to aggregate and process data and generate actionable reports, graphics or simulations.

Here’s a look at 15 of the most popular tools and what sets them apart.

Data Science Tools Comparison Chart

15 Data Science Tools for 2023

Data Cleaning and Preprocessing Tools

Trifacta

Trifacta is a cloud-based, self-service data platform for data scientists looking to clean, transform and enrich raw data and turn it into structured, analysis-ready datasets.

Pros:

• Intuitive and user-friendly
• Machine Learning-based
• Integrates with data storage and analysis platforms

Cons:

• Costly for smaller projects
• Limited support for programming languages

Pricing
There isn’t a free option of Trifacta. However, there’s a Starter option at $80 per user, per month for basic functionality. The Professional option costs $4,950 per user, per year for added functionality, but requires a minimum of three licenses. There’s also the option for a desktop-based or a cloud-based free trial.

OpenRefine

OpenRefine is a desktop-based, open-source data cleaning tool that helps make data more structured and easier to work with. It offers a broad range of functions, data transformation, normalizations and deduplication.

Pros:

• Open-source and free to use
• Supports multiple data formats: CVS, XML and TSV
• Supports complex data transformation

Cons:

• No built-in ML or automation features
• Limited integration with data storage and visualization tools
• Steep learning curve

Pricing
100 percent free to use.

DataWrangler

DataWrangler is a web-based data cleaning and transforming tool developed by the Stanford Visualization Group, now available on Amazon SageMaker. It allows users to explore data sets, apply transformations and prepare data for downstream analysis.

Pros:

• Web-based with no need for installation
• Built-in data manipulation operations
• Automatic suggestions for  appropriate data-cleaning actions

Cons:

• Limited integration with data storage and visualization tools
• Limited support of large datasets
• Limited updates and customer support

Pricing
The use of DataWrangler on the Amazon SageMaker cloud is charged by the hour, starting at $0.922 per hour at 64 GiB of memory for standard instances, and at $1.21 at 124 GiB of memory for optimized memory.

AI/ML-Based Frameworks

Scikit-learn

Scikit-learn is a Python-based and open-source library that encompasses a wide range of tools for data classification and clustering using AI/ML.

Pros:

• Comprehensive documentation
• Reliable and consistent API
• Wide range of algorithms

Cons:

• Limited support for neural networks and deep learning frameworks
• Not optimized for GPU-usage

Pricing
100 percent free to use.

TensorFlow

Developed by Google, TensorFlow is an open-source machine learning and deep learning library. It enables users to deploy various models across several platforms, supporting both CPU and GPU computation.

Pros:

• Scalable and suitable for large-scale projects
• Allows for on-device machine learning
• Includes an ecosystem of visualizations and management tools
• Open-source and free to use

Cons:

• Steep learning curve
• Dynamic data modeling can be challenging

Pricing
The library is 100 percent free to use, but when deployed on the AWS cloud, the typical price starts at $0.071 per hour.

PyTorch

PyTorch is an open-source ML library developed by Meta’s AI research team and based on the Torch library. It’s known for its dynamic computation graphs, computer vision and natural language processing.

Pros:

• Simplifies the implementation of neural networks
• Easy integration with Python
• Open-source and free to use
• Strong community support and documentation

Cons:

• Few built-in tools and components
• Limited support for mobile and embedded devices

Pricing
The library is 100 percent free to use, but when deployed on the AWS cloud, the typical price starts at $0.253 per hour.

Deep Learning Libraries

Keras

Keras is a high-level neural network library and Application Programming Interface (API) written in Python. It’s capable of running on top of numerous frameworks, such as TensorFlow, Theano and PlaidML. It allows users to simplify the process of building, training and deploying data-based deep learning models.

Pros:

• User-friendly and easy to use
• Extensive documentations
• Pre-made layers and components

Cons:

• Limited compatibility with low-level frameworks
• Complex models may suffer from performance issues

Pricing
100 percent free to use.

Fast.ai

Fast.ai is an open-source deep-learning library built on top of Meta’s PyTorch and designed to simplify the training of neural networks using minimal code.

Pros:

• User-friendly interface
• Built-in optimization for deep learning tasks
• Extensive documentation and educational resources

Cons:

• Limited customization options
• Smaller active community

Pricing
100 percent free to use.

Hugging Face Transformers

Hugging Face Transformers is an open-source, deep-learning library that focuses on natural languages processing models, such as GPT, BERT and RoBERTa. It offers pre-trained models along with the tools needed to fine-tune them.

Pros:

• Large repository of ready-use models
• Supports Python and TensorFlow
• Active online community

Cons:

• Limited open natural language processing tasks
• Steep learning curve

Pricing
The library is 100 percent free to use, but when combined with AWS Cloud and AWS Inferentia2, pricing starts at $0.76 per hour.

Big Data Processing Tools

Apache Spark

Apache Spark is a distributed and open-source computing system designed to simplify and speed up data processing. It supports a wide range of tasks including data transformers, ML and graph processing.

Pros:

• In-memory data processing for higher performance
• Built-in ML and graph processing libraries
• Integrates seamlessly with Hadoop ecosystems and various data sources

Cons:

• Processing is resource-intensive
• Requires pre-existing programming knowledge

Pricing
The system is 100 percent free to use, but when deployed on the AWS cloud, typical pricing starts at $0.117 per hour.

Apache Hadoop

Apache Hadoop is an open-source, distributed computing framework that processes large volumes of data across clusters of servers and databases. It consists of Hadoop Distributed File System (HDFS) for storage and MapReduce for processing.

Pros:

• Highly-scalable and fault-tolerant
• Supports a wide variety of tools such as Apache Hive and HBase for data processing
• Cost-effective

Cons:

• Disk-based storage leads to slower processing
• Limited support for real-time data processing
• MapReduce as a steep learning curve

Pricing
The framework is 100 percent free to use, but when deployed on the AWS cloud, typical pricing starts at $0.076 per hour.

Dask

Dask is a flexible, parallel computing library for Python that enables users to scale numerous well-known workflows using APIs such as Scikit-learn and NumPy. It’s designed specifically for multi-core processing and distributed computing.

Pros:

• Interface similar to Python
• Support for dynamic, real-time computation
• Lightweight and compatible with Python workflows

Cons:

• Limited support for languages other than Python
• Not ideal for processing large datasets

Pricing
100 percent free to use.

Cloud-based Data Science Platforms

Google Colab

Google Colab is a cloud-based Jupyter Notebook environment in which users are able to write and execute Python code directly in their web browsers. It’s a collaborative platform for both data science and machine learning tasks with accelerated computations.

Pros:

• No setup or installation required
• Online access to GPUs and TPUs
• Supports real-time collaboration and data sharing

Cons:

• Limited computing resources available
• Lack of built-in support for third-party integration

Pricing
With a free version available, Google Colab pricing plans start at $9.99 per month for the Colab Pro plan and $49.99 per month for the Colab Pro+ plan; a pay-as-you-go option starts at $9.99 per 100 compute units, or $49.99 per 500 compute units.

Databricks

Databricks is a unified data analytics platform that combines ML with big data processing and collaborative workspaces, all in a managed cloud environment. It’s a comprehensive solution for data engineers, scientists and ML experts.

Pros:

• Seamless integration with Apache Spark
• Supports high-performance data processing and analysis
• Built-in tools for version control, data visualization and model deployment

Cons:

• Cost ineffective for smaller projects
• Steep learning curve
• Vendor lock-in

Pricing
With a 14-day free trial available, Databricks can be deployed on the user’s choice of Azure, AWS or Google Cloud. There’s a price calculator, enabling customization of subscriptions.

Amazon SageMaker

Amazon SageMaker is a fully managed, ML platform that runs on Amazon Web Services. It allows data scientists and developers to build, train and deploy machine learning models in the cloud, providing end-to-end solutions for data processing, model training, tuning and deployment.

Pros:

• Integrates seamlessly with the AWS ecosystem and tools
• Built-in algorithms for popular machine learning frameworks, such as MX Net, PyTorch and TensorFlow
• Wide range of tools for model optimization, monitoring, and versioning

Cons:

• Steep learning curve
• High-end pricing
• Vendor lock-in

Pricing
With a free tier available, Amazon SageMaker is available in an on-demand pricing model that allows customization of services and cloud capacity.

Factors to Consider When Choosing Data Science Tools

As the importance of data continues to grow and transform industries, selecting the right tools for your organization is more critical than ever. However, with the vast array of available options, both free and proprietary, it can be challenging to identify the ideal fit for specific needs.

There are a number of factors to consider when choosing data science tools, whether it’s data processing frameworks or ML libraries.

Scalability

Scalability is a crucial factor to consider early on in the decision-making process. That’s because data science projects often involve large volumes of data and computationally-intensive algorithms. Tools like Apache Spark, TensorFlow and Hadoop are designed with big data in mind, enabling users to scale operations across multiple machines.

It’s essential to ensure that a tool can efficiently manage the data size and processing demands of the project it is chosen for, both currently and in the future as needs evolve.

Integration With Existing Infrastructure

Seamless integration with an organization’s existing infrastructure and legacy software is vital for efficient data processing and analysis. This is where caution can prevent being locked into a specific vendor.

Many online tools and platforms, such as Amazon SageMaker and Databricks, are compatible with a number of legacy systems and data storage solutions. This enables them to complement an organization’s existing technology stack and greatly simplify the implementation process, allowing users to focus on deriving insights from data.

Community Support and Documentation

A strong online community and comprehensive documentation are particularly important when choosing data science tools to be used by smaller teams. After all, active user communities are able to provide troubleshooting assistance, share best practices, and even contribute to the ongoing development of the tools.

Tools like Keras and Scikit-learn boast extensive documentation in addition to a widespread and active online community. This makes them accessible to beginners and experts alike. When it comes to documentation, it’s crucial that the available documents include up-to-date information and are regularly updated with the latest advancements.

Customizability

The ability to flexibly customize tools is essential to accommodate unique project requirements, but to also optimize performance based on available resources. Tools like PyTorch and Dask offer some of the most useful customizability options compared to their counterparts. They allow users to tailor their data processing workflows and algorithms to their specific needs.

Determining the level of customization offered by a tool and how it aligns with a project is important to guarantee the desired level of control.

Learning Curve

While all tools have a learning curve, it’s important to find data science tools with complexity levels that match the expertise of the data science and analytics teams that will be using them.

Tools such as Google Colab and Fast.ai are known for their user-friendly and intuitive interface, but other programming-based tools, like Apache Spark and TensorFlow, may be harder to master without prior experience.

The Future of Data Science Tools

The rapid development and innovation in the fields of AI and ML are also driving the development of new algorithms, frameworks and platforms used for data science and analytics. In some instances, those advancements occur too fast, and staying informed about the latest trends ensures the ability to remain competitive in an economy reliant on deriving insights from raw data.

Automation is increasingly playing a prominent role in how data is gathered, prepared and processed. Using AI and ML, tools like AutoML and H2O.ai can be used to streamline data parsing by automating some of the numerous steps that go into the process. In fact, the growing role of automation in data science is likely to shape the industry’s landscape going forward, determining which tools and skill-set are more viable and in demand.

The same is likely to apply to quantum computing, as it holds great potential to revolutionize countless data processing and optimization problems, thanks to its ability to tackle complex and large-scale tasks. Its impact could potentially lead to new algorithms, frameworks and tools specifically designed for data processing in quantum environments.

Bottom Line: Data Science Tools

Choosing the right data science tools for an organization requires a careful evaluation of factors such as scalability, integration with existing infrastructure, community support, customizability and ease of use. As the data science landscape continues to evolve, staying informed about the latest trends and developments, including ongoing innovations in AI and ML, the role of automation and the impact of quantum computing will be essential for success in the data-driven economy.