Compare the Top DevOps Software as of June 2025

What is DevOps Software?

DevOps is a software development and delivery practice that emphasizes collaboration between software developers, IT operations professionals, and other stakeholders. It uses a set of tools and automation to help manage the various processes that go into developing software quickly, efficiently, and reliably. DevOps allows teams to streamline their software development activities into an automated pipeline for improved quality and faster time-to-market. Compare and read user reviews of the best DevOps software currently available using the table below. This list is updated regularly.

  • 1
    Cycloid

    Cycloid

    Cycloid

    Cycloid Sustainable Platform Engineering is a self-service portal that helps you streamline your software delivery, reduce the cognitive load of your engineering teams, and promote Green IT practices. Step 1. Modernize your infrastructure in sophisticated service catalog supported by Infra Import, a Terraform generator in a full GitOps-first approach. Step 2. Scale the adoption of your platform and lighten workload for end-users and DevOps with a UX-strong internal developer platform. Your tools, automation, and cloud will be accessible without expert knowledge while still keeping control and best practices. Step 3. Allow your teams to continuously optimize their projects with a 360° overview of CI/CD pipelines, automation, documentation, KPI’s, FinOps and GreenOps. With Cycloid, you will enable a future where technology and sustainability can coexist harmoniously, leaving a lasting positive legacy.
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  • 2
    Fairwinds Insights

    Fairwinds Insights

    Fairwinds Ops

    Protect and optimize your mission-critical Kubernetes applications. Fairwinds Insights is a Kubernetes configuration validation platform that proactively monitors your Kubernetes and container configurations and recommends improvements. The software combines trusted open source tools, toolchain integrations, and SRE expertise based on hundreds of successful Kubernetes deployments. Balancing the velocity of engineering with the reactionary pace of security can result in messy Kubernetes configurations and unnecessary risk. Trial-and-error efforts to adjust CPU and memory settings eats into engineering time and can result in over-provisioning data center capacity or cloud compute. Traditional monitoring tools are critical, but don’t provide everything needed to proactively identify changes to maintain reliable Kubernetes workloads.
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