CI/CD refers to Continuous Integration and Continuous Delivery, a DevOps approach that automates code testing and deployment. CI/CD tools help teams release software faster, improve reliability and reduce errors. This guide reviews the best CI/CD tools of 2025 that offer advanced features for scalability, collaboration and security.
Selecting the right CI/CD tool depends on your team's size, workflow complexity, existing infrastructure, and long-term DevOps goals. The best CI/CD tools support automation, scalability, and seamless integration with your tech stack. To make an informed decision, evaluate core features and licensing models that match your operational and compliance requirements.
A robust CI/CD platform should provide more than just pipeline automation. Look for the following core features to ensure performance, flexibility, and team productivity:
Example:
GitHub Actions offers native Git integration and reusable workflows, while CircleCI excels in speed and parallelism.
The choice between open-source and commercial CI/CD tools hinges on control, support, and operational priorities.
Open-source tools (e.g. Jenkins, Drone, Buildkite):
Commercial platforms (e.g. CircleCI, GitLab, Spacelift):
Key Consideration:
If security, uptime, and regulatory compliance are priorities, a commercial CI/CD solution with managed infrastructure may provide faster time to value and reduced operational overhead.
The most effective CI/CD tools in 2025 strike a balance between speed, automation, flexibility, and security. Your choice should depend on your team’s DevOps maturity, infrastructure preferences, and integration needs. Below is a curated selection of the best platforms, with in-depth context to help you decide.
Jenkins is a long-standing, open-source CI/CD tool known for its flexibility and customisability. Built in Java, it supports thousands of plugins for integrating with virtually any development, test, or deployment tool.
Key features:
GitHub Actions is a cloud-native CI/CD platform embedded directly into GitHub, making it ideal for teams already using GitHub for version control.
.github/workflows/Key features:
CircleCI offers fast, flexible pipelines optimised for container-based deployments. Known for its performance at scale, it supports both cloud and on-prem environments.
.circleci/config.ymlKey features:
GitLab offers a unified DevOps platform with native CI/CD tightly integrated into its version control system. It enables code management, testing, security scanning and deployment within a single interface.
.gitlab-ci.ymlKey features:
Spacelift is a specialised CI/CD platform built for infrastructure-as-code (IaC) workflows. It focuses on security, policy-as-code and deep Terraform integration.
Key features:
Tip: When comparing tools, prioritise compatibility with your current stack, pipeline visibility, and the availability of vendor support.
The best CI/CD tool is the one that fits your team’s structure, regulatory needs and development environment. Whether you're a lean startup, a large enterprise or part of a regulated industry, tool selection should reflect the unique constraints and goals of your workflow.
Startups need speed, ease of setup and minimal overhead. They often favour cloud-native tools that require little infrastructure management.
Recommended tools:
Why:
Larger teams need fine-grained access controls, high concurrency, and compliance features.
Recommended tools:
Why:
Distributed teams benefit from tools that support visibility, async collaboration and decentralised infrastructure.
Recommended tools:
Why:
Machine learning workflows introduce distinct challenges to software delivery, including model versioning, experiment tracking, handling large datasets, and multi-step training pipelines. Traditional CI/CD tools are increasingly being extended to support MLOps: the practice of operationalising machine learning in production environments.
Here are some of the most widely used CI/CD tools and frameworks that support ML pipelines:
Kubeflow Pipelines is an open-source platform for building and deploying portable, scalable ML workflows on Kubernetes. It supports orchestration of training, validation and deployment steps using containerised components.
Case Study: CERN
CERN uses Kubeflow Pipelines to process and analyse particle physics data at scale. They implemented containerised ML workflows to detect anomalies and retrain models using real-time sensor input.
GitHub Actions can automate machine learning (ML) tasks, such as triggering training jobs, logging metrics, or promoting models through environments using MLflow.
Case Study: Shopify
Shopify integrates GitHub Actions and MLflow to automate forecasting models for demand planning. Engineers push changes to GitHub, triggering retraining jobs that log performance metrics and register deployment models.
Jenkins X provides Kubernetes-native CI/CD pipelines and supports containerised ML applications using Helm, Kustomize or KServe for model deployment.
For AWS-based teams, SageMaker Pipelines integrates with AWS CodePipeline to automate training, model approval, and deployment workflows.
Case Study: Siemens
Siemens Healthineers uses SageMaker Pipelines and CodePipeline to automate ML-driven diagnostics. Pipelines include differential testing, data drift detection and staged approvals before model promotion to production.
Azure ML enables end-to-end machine learning lifecycle management, with Azure DevOps facilitating CI/CD for model deployment, retraining, and rollback.
MLOps integration ensures machine learning models follow the same discipline as software releases, making them more robust, reproducible and scalable. By using CI/CD tools that support ML workflows, teams can:
Are some tools better for regulated industries like finance or healthcare?
Yes, compliance-heavy sectors require tools that prioritise security, traceability and policy enforcement by design.
Recommended tools:
Key capabilities to look for:
Successful CI/CD implementation requires more than just selecting the right tool. It involves aligning processes, infrastructure and culture around continuous delivery. Leading teams apply CI/CD to reduce release friction, enforce code quality, and accelerate innovation without sacrificing control or security.
Netflix operates at a massive scale, pushing thousands of production changes daily across a globally distributed architecture. To meet this demand, Netflix developed and implemented a custom CI/CD pipeline built on open-source tools.
Implementation highlights:
Key outcomes:
Top-performing teams track the following DevOps metrics to measure the effectiveness of their CI/CD implementation:
Tracking these metrics helps teams detect bottlenecks, optimise performance and justify further DevOps investment.
Even the best CI/CD tools can fail to deliver results if implementation is rushed, misaligned with team workflows, or overly complex. Avoiding common pitfalls ensures a smoother rollout, faster value realisation, and better long-term scalability.
Choosing a CI/CD platform involves evaluating how well a tool aligns with your team's workflows, infrastructure, and delivery goals. Industry practitioners emphasise compatibility, simplicity, and scalability as key success factors.
Choosing the right CI/CD tool is both a technical and a strategic decision. The best solution is the one that aligns with your existing workflows, scales with your infrastructure, and enables your team to deliver quality software faster and more reliably.
Prioritise ease of integration, support for your tech stack, and long-term scalability when making your final choice.
Need expert guidance on choosing the right CI/CD solution? Our team specialises in helping development teams streamline their software delivery pipelines. Get in touch with us today to discuss your requirements and explore the best-fit tools for your infrastructure, team size and compliance needs.
There are many tools used for CI/CD depending on your tech stack and requirements. Popular choices include Jenkins, GitHub Actions, GitLab CI/CD, CircleCI, and Spacelift. These platforms automate build, test and deployment processes to support continuous software delivery.
Jenkins remains one of the most widely adopted CI/CD tools due to its open-source flexibility and extensive plugin ecosystem. However, GitHub Actions and GitLab CI/CD have gained popularity for their native integration with source control platforms and ease of use.
No. Kubernetes is not a CI/CD tool. It is a container orchestration platform. However, many CI/CD pipelines are designed to deploy applications directly into Kubernetes clusters using integrations with tools like Argo CD, Flux, or Spinnaker.
Yes. Azure DevOps includes a fully featured CI/CD service called Azure Pipelines, which supports multi-platform build, test, and deployment automation. It works with GitHub, Bitbucket and other repos, and integrates natively with Microsoft Azure cloud services.
Yes. GitHub Actions is free for public repositories and offers generous limits for private projects. It’s ideal for beginners due to its native GitHub integration, simple YAML configuration, and the absence of infrastructure management requirements.
Yes. Most modern CI/CD tools support both stages within a single pipeline. GitLab CI/CD, CircleCI, and Spacelift allow teams to build, test, and deploy code using a single, streamlined configuration.
No. CI/CD is equally valuable for small teams and solo developers. Automation reduces manual errors, speeds up testing, and improves deployment reliability, regardless of team size. Tools like GitHub Actions and CircleCI scale flexibly.
Check the tool’s documentation for official integrations with languages, frameworks, cloud providers and version control systems. Most CI/CD platforms support Docker, Kubernetes, Terraform, and popular programming languages like Python, Java, and Node.js.
Both build on Continuous Integration and aim to speed up release cycles.
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