CI/CD tools
Continuous Integration Tools: 10 CI Tools Compared
Continuous integration tools run your build, test and check steps every time code changes, and report the result back to the change. This guide explains what CI does, how to choose between hosted and self-hosted options, and compares ten tools, from GitHub Actions to Argo Workflows.
By Jasdeep Bains, SeaGit
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Key takeaways
- Continuous integration runs builds, tests and checks on every change and reports pass or fail to the pull request.
- The first decision is hosting: a SaaS service that runs jobs for you, or a self-hosted tool on machines or a cluster you operate.
- Pick the tool your code host and team already use. Moving CI later costs more than most feature differences.
- Kubernetes-native tools such as Tekton and Argo Workflows run each step as a container in your cluster, but you assemble triggers and reporting yourself.
- Pricing follows one of a few models: metered compute, seats or tiers, a self-hosted license, or open source plus your own infrastructure. Check the vendor page for current figures.
What continuous integration is
Continuous integration is the practice of merging changes to a shared branch often and verifying each change automatically. A CI system watches a repository, starts a job when a trigger fires (a push, a pull request, a schedule or an API call), runs the steps the team defined, and reports pass or fail back to the change.
A typical pipeline checks out the code, restores dependencies from a cache, runs linters and unit tests, builds an artifact such as a binary, a package or a container image, runs integration tests, and publishes the artifact or the result. Continuous delivery and deployment take that artifact further toward production. CI covers the build and verification half of that path.
Keep CI and CD apart in your head, even when one tool does both. GitHub Actions and GitLab CI/CD run both halves in one pipeline. Argo CD does only the deployment half: it watches a Git repository and syncs a cluster to match. It is a CD controller, not a CI tool, so it is not in the roundup below.
How to choose a CI tool
Compare tools on the criteria below, roughly in this order. Most teams settle hosting and config format before they look at feature lists, because those two decisions are the hardest to reverse.
Hosting model
SaaS tools run the control plane and often the build machines. Self-hosted tools run the server and agents on infrastructure you operate. Hybrid tools host the control plane but run jobs on agents you own. Hosting decides who patches the server, where secrets sit, and whether a build can reach a private network.
Config as code
Look for pipelines stored in the repository as YAML or code. A pipeline in the repository gets code review, history and branch specific changes. Tools configured mainly through a web UI are harder to audit and to reproduce.
Runners and executors
A runner or agent is the machine that executes a job. Check which operating systems are available, whether you can bring your own machines, how autoscaling works, and whether a job runs in a container, a VM or directly on the host. Build isolation depends on this choice.
Container and Kubernetes support
If your deploy target is Kubernetes, check whether each job can run as a pod, whether images can be built without a privileged Docker daemon (Kaniko and BuildKit are common choices), and whether the CI system can schedule work onto your cluster. Kubernetes-native tools treat the cluster as the scheduler. Hosted tools treat it as an external deploy target.
Caching and speed
Dependency and build caches decide most of the time a pipeline takes. Check how caches are keyed and shared between branches, and whether the tool can run steps in parallel or split a test suite across jobs.
Secrets
Check how credentials are stored, scoped and rotated. Look for scoping by branch, environment or protected branch, and for support for an external secret store or OIDC federation to your cloud account, so long-lived keys do not sit in CI settings.
Pricing model
Most tools fall into one of four pricing shapes: metered compute, such as minutes or credits on hosted runners; seats or tiers; a license for a self-hosted server; or open source, where you pay for the infrastructure and the people who run it. Compare the shape against your expected job volume and team size, not against a headline figure. Read the vendor pricing page on the day you decide.
Ecosystem
Count the integrations that matter to you: your source host, your registry, your cloud, and your chat and ticketing tools. Large catalogues of reusable steps save time, but every third-party step runs with your secrets, so review what you import.
Ten continuous integration tools
Each entry gives what the tool is, where it runs, how pipelines are written, who it suits, and one real limitation. The order follows how often teams meet each tool, not a ranking of quality.
GitHub Actions
- What it is
- The CI and automation service built into GitHub. Workflows start on repository events such as push, pull_request or a schedule, and run as jobs made of steps. Reusable actions come from the GitHub Marketplace and from other repositories.
- Hosting
- SaaS, with GitHub-hosted runners on Linux, Windows and macOS, and self-hosted runners you operate.
- Config format
- YAML workflow files in .github/workflows.
- Best fit
- Teams whose code already lives on GitHub and who want CI next to pull requests, with no separate system to run.
- Limitation
- Workflows are written against GitHub events and its runner model, so moving them elsewhere means rewriting them. Long workflows repeat YAML unless you factor them into reusable workflows.
GitLab CI/CD
- What it is
- The CI and CD system built into GitLab. Pipelines run from a .gitlab-ci.yml file and link to merge requests, the container registry and deployment environments.
- Hosting
- SaaS on GitLab.com, or self-managed GitLab. Jobs run on GitLab Runner, which you install on your own machines or in a Kubernetes cluster.
- Config format
- .gitlab-ci.yml, with includes and templates for reuse.
- Best fit
- Teams that use GitLab for source control and want one application for code, CI, registry and environments.
- Limitation
- Most of the value depends on the rest of the GitLab platform. Compute on SaaS plans is quota-based, and self-managed installs need an upgrade plan.
Jenkins
- What it is
- An open-source automation server with a large plugin catalogue. Pipelines are written as a Jenkinsfile, either declarative or scripted in Groovy, and the controller hands work to agents.
- Hosting
- Self-hosted. You run the controller and the agents on VMs, containers or Kubernetes, with the Kubernetes plugin for pod-based agents.
- Config format
- Jenkinsfile (declarative or scripted Groovy), or jobs set up in the web UI.
- Best fit
- Organizations that need full control of the environment, rely on many existing plugins, or run builds on networks no hosted service can reach.
- Limitation
- You own upgrades, plugin compatibility and the security of the controller, which is ongoing work. Scripted Groovy pipelines can become hard to read.
CircleCI
- What it is
- A cloud CI platform that runs pipelines defined in a config file. It offers reusable packages called orbs and Docker, machine and macOS execution environments.
- Hosting
- SaaS, with self-hosted runners you run on your own infrastructure.
- Config format
- .circleci/config.yml, with orbs for shared jobs and commands.
- Best fit
- Teams that want a managed service for Docker-heavy builds, with caching and parallel test splitting, on GitHub or Bitbucket.
- Limitation
- Usage is metered on compute, so cost follows job volume and resource class. Orbs save effort but add a dependency you must review and pin.
Buildkite
- What it is
- A CI platform with a hosted control plane and agents that run on machines you own. Pipelines are defined as YAML steps, and a pipeline can generate more steps at run time.
- Hosting
- Hybrid. Buildkite hosts the web application and control plane. Agents run on infrastructure you control, including Kubernetes.
- Config format
- pipeline.yml steps, optionally generated by a script (dynamic pipelines).
- Best fit
- Teams with large monorepos, strict network or hardware needs, or an existing build farm, who want a hosted interface without moving compute out of their account.
- Limitation
- You run and scale the agent fleet, so that operations work is yours. The UI and scheduling still depend on the hosted control plane.
Azure Pipelines
- What it is
- The CI and CD service in Azure DevOps. Pipelines can be written as YAML or built in the classic editor, and can build code from Azure Repos, GitHub, Bitbucket or other Git sources.
- Hosting
- SaaS, with Microsoft-hosted agents and self-hosted agents you install.
- Config format
- azure-pipelines.yml, or the classic visual editor.
- Best fit
- Teams already on Microsoft tooling, and organizations that want work tracking, boards and pipelines in one place.
- Limitation
- Organizations, projects and parallel-job limits add structure to learn. Outside a Microsoft-centered setup, that structure is setup a Git-native CI does not need.
Bitbucket Pipelines
- What it is
- The CI service built into Bitbucket Cloud. Each step runs in a Docker container that the pipeline file names.
- Hosting
- SaaS, with self-hosted runners available for your own infrastructure.
- Config format
- bitbucket-pipelines.yml.
- Best fit
- Teams whose repositories are in Bitbucket Cloud and who want CI without a separate system.
- Limitation
- It is tied to Bitbucket Cloud repositories, and build minutes depend on the plan.
TeamCity
- What it is
- A CI server from JetBrains with build chains, managed build agents and detailed test reporting. Configuration can be set in the web UI or stored as code in a Kotlin DSL.
- Hosting
- Self-hosted on-premises server, or the JetBrains-hosted cloud option, with build agents on machines you choose.
- Config format
- UI settings, or a Kotlin DSL kept in the repository.
- Best fit
- Enterprise teams, especially on Java, .NET or mixed stacks, who want a supported server with fine-grained permissions and reporting.
- Limitation
- In the self-hosted model you license and operate the server yourself. Its third-party ecosystem is smaller than Jenkins or GitHub Actions.
Tekton
- What it is
- A Kubernetes-native framework for CI and CD. Pipelines are built from Kubernetes custom resources (Task, Pipeline, PipelineRun), and each step runs as a container in a pod. It is an open-source project under the CD Foundation.
- Hosting
- Self-hosted in your Kubernetes cluster. The dashboard and extras such as Triggers and Chains are installed separately.
- Config format
- Kubernetes YAML for Tasks, Pipelines and runs.
- Best fit
- Platform teams that want CI to run as Kubernetes workloads, with reusable tasks and no separate CI server.
- Limitation
- There is no hosted service, Git UI or trigger model out of the box. You assemble triggers, dashboards and status reporting, and the YAML is verbose.
Argo Workflows
- What it is
- A Kubernetes-native workflow engine in which each step is a container and a workflow is a DAG or a sequence of steps. It is a CNCF project, used for CI pipelines, data jobs and batch work.
- Hosting
- Self-hosted in your Kubernetes cluster, with the Argo Workflows UI.
- Config format
- Kubernetes YAML (Workflow and WorkflowTemplate resources).
- Best fit
- Teams already running Argo in Kubernetes who want container-native pipelines with DAGs, retries and fan-out.
- Limitation
- It is a workflow engine, not a CI product. Git triggers, pull request status and reporting come from Argo Events and your own integrations.
Argo CD is not in the list. It is a GitOps deployment controller, so it pairs with a CI tool that builds the image rather than replacing one.
Side-by-side comparison
Pricing is given as a model only. Figures change, and each vendor publishes the current ones.
| Tool | Hosting | Config | Pricing model | Best fit |
|---|---|---|---|---|
| GitHub Actions | SaaS, plus self-hosted runners | YAML workflows | Metered hosted-runner minutes above the plan allowance | Repositories on GitHub |
| GitLab CI/CD | SaaS or self-managed, GitLab Runner | .gitlab-ci.yml | Plan-based, with compute quotas on SaaS | One platform for code, CI and registry |
| Jenkins | Self-hosted controller and agents | Jenkinsfile (Groovy) | Open source; you pay for infrastructure and operations | Full control and a large plugin set |
| CircleCI | SaaS, self-hosted runners | .circleci/config.yml | Usage-based compute | Managed, Docker-heavy builds |
| Buildkite | Hosted control plane, your agents | pipeline.yml | Subscription; you pay for your own compute | Large builds on your own machines |
| Azure Pipelines | SaaS, Microsoft-hosted or self-hosted agents | azure-pipelines.yml | Free tier, then paid add-ons such as users, parallel jobs and hosted minutes | Microsoft and Azure DevOps teams |
| Bitbucket Pipelines | SaaS, self-hosted runners | bitbucket-pipelines.yml | Plan-based build minutes | Repositories on Bitbucket Cloud |
| TeamCity | Self-hosted or cloud, your agents | UI or Kotlin DSL | Commercial license, with a free edition | Enterprise Java, .NET and mixed stacks |
| Tekton | Your Kubernetes cluster | Kubernetes YAML (Task, Pipeline) | Open source; you pay for cluster compute | Platform teams running CI as Kubernetes workloads |
| Argo Workflows | Your Kubernetes cluster | Kubernetes YAML (Workflow) | Open source; you pay for cluster compute | Container-native DAG pipelines |
Where SeaGit fits
SeaGit is not a general-purpose CI service. It builds and deploys apps from GitHub pushes onto Amazon EKS in your own AWS account. It fits when the part after the build is what you want handled: a running app with its own URL, a preview deployment for a branch, and promotion to the main deployment.
How the build runs depends on the route you pick. The build and deploy routes page describes four:
- Route A: SeaGit builds directly in your cluster. This is the shortest path.
- Route B: a multi-step pipeline on Argo Workflows, installed as the argoci add-on and triggered through the SeaGit GitHub App.
- Route C: your own argoci webhook. SeaGit is not in the trigger path, but it receives build signals and the deployment acknowledgement.
- Route D: your existing CI system, routed through SeaGit, which deploys the result once your CI reports back.
Argo-based builds need a few things in the cluster first: a node pool for builds, artifact storage (MinIO or an S3 bucket), and the argoci add-on. The Argo CI/CD setup guide walks through the order.
Pushes to a branch other than main can create or update a preview deployment with its own URL, and CI/CD does not overwrite the main deployment. On a pull request, SeaGit reports dispatch, build and deployment status checks, and a comment links to the preview.
If your team already runs tests and linting in GitHub Actions or GitLab CI, Route D lets that CI keep doing the building while SeaGit handles the deploy. The question to answer first is where the build should run and who should own the trigger. SeaGit runs on AWS only. Its clusters are Amazon EKS in your account.