Configuration as Code
Build steps, triggers, and agent requirements can be stored as Kotlin DSL files and kept in the same repository as the application, so pipeline changes go through review and can be reverted like any other commit.

JetBrains TeamCity is a CI/CD server for automating builds, tests, and deployments across Windows, Linux, and macOS. Teams use it to catch problems early, parallelize test suites, and release software through repeatable, auditable pipelines.
JetBrains TeamCity is a continuous integration and delivery server that takes a project from source commit to tested, deployable artifact. You connect a version control repository, define a build configuration with the steps, dependencies, and triggers you need, and TeamCity watches for changes and queues a build automatically. Work runs on build agents that can sit on your own hardware or in the cloud, so long or resource-heavy jobs run without blocking another team's pipeline. Results appear in the web interface as soon as a build finishes, with logs, artifacts, and test reports attached to each run. That feedback loop is what teams buy into: developers hear about a broken commit within minutes instead of days.
Deployment is where JetBrains TeamCity goes beyond a plain build runner. Configurations chain together, so a commit triggers compilation, then tests, then a staging deployment, then a gated production release. Approval steps let a person sign off before anything ships, and the record of every promotion stays visible. The same flexibility extends to the wider toolchain: repositories, chat platforms, and notification channels connect through integrations, just as separate services are linked in tools like Adobe Connect. Both the cloud-hosted and on-premises editions keep that workflow intact, so a small team and a large organisation configure the same concepts in the same way.
The clearest benefit is earlier feedback: a defect found minutes after a commit costs far less to fix than one discovered during a release scramble. Parallel test execution and automatic reordering of previously failed tests shorten the wait further, so a large suite that once took an hour may finish in a fraction of that time. Repeatability matters just as much, because build steps that live in version-controlled configuration can be recreated rather than rebuilt by hand. Audit trails, approval gates, and role-based permissions give managers the evidence they need for compliance reviews without slowing engineers down. The practical result is a process that scales with the team, from a modest self-hosted setup to a large installation running many build agents across platforms. Because the same configuration model applies regardless of where agents run, teams can start small and grow the infrastructure without redesigning their pipelines.
Key capabilities and highlights.
Build steps, triggers, and agent requirements can be stored as Kotlin DSL files and kept in the same repository as the application, so pipeline changes go through review and can be reverted like any other commit.
Related configurations can depend on one another, so a single commit moves through compilation, unit tests, integration tests, and deployment without manual handoffs. Artifacts produced by one stage are passed automatically to the next, and each chain reports status per step, which makes it easy to see exactly where a pipeline stalled and how far a change actually progressed.
Test suites are split across the available agents and reordered so recently failed tests run first, cutting the waiting time on large projects without hand-written splitting scripts. Aggregated results show totals, timings, and per-test history, so a suite that once ran for an hour can return a verdict in a fraction of that time.
Reports separate genuine regressions from unstable tests, show the history behind each result, and allow a build step to be re-run from the point of failure instead of starting over. Investigations can be assigned to individuals and tests re-executed in a fresh environment, turning a vague red build into a specific and actionable problem.
Agents can be grouped into pools, tagged by capability, and matched only to compatible jobs, so a Windows-only step never lands on the wrong machine. JetBrains TeamCity can also start additional cloud agents on demand when the build queue grows and shut them down afterwards, keeping capacity aligned with demand instead of paid for around the clock.
Developers can send changes for a remote build before pushing and read the outcome inside their development environment, so only code that already passed the build reaches the shared branch. Reviewers then see a verified commit, the queue stays clean, and integration problems surface while the author still has full context in the editor.
Release steps are modelled as ordinary configurations, optionally with manual or scheduled approval, so every promotion has a recorded and repeatable path from a staging environment to production. Environments, parameters, and credentials can differ per stage, and a rejected release can be investigated or re-run without rebuilding what was already tested.
Role-based access, project-level permissions, directory or single sign-on authentication, and retained audit history control who may view, change, or trigger builds. Because every action is attributable and permissions can be scoped per project, teams can answer compliance and security questions without exposing pipeline settings to everyone.
Browse historical releases and previous builds.
Common questions about JetBrains TeamCity.
It is a continuous integration and delivery server. It builds and tests code automatically whenever changes are committed, runs the work on dedicated agents, and can promote validated builds toward staging or production, giving teams fast feedback on every change.
Yes, a free tier covers smaller installations, with paid editions for larger teams. It can be self-hosted on your own servers or used as a JetBrains-hosted cloud service. Plan limits and prices are revised periodically, so confirm current terms before choosing an edition.
Jenkins is an open-source automation server extended mainly through community plugins. TeamCity ships with many capabilities built in, including test reporting, build chains, configuration as code, and dashboards, and is a commercially supported product with a vendor responsible for updates and assistance.
Yes. Builds are commonly run inside containers for consistent toolchains, and pipelines can pull base images as a step, publish freshly built images to a registry, and deploy containers from the same configuration.
Yes. Configuration can be written in Kotlin DSL and synchronised with a repository, so pipeline definitions are versioned, reviewable, and portable between projects. Settings edited in the web interface can also be committed back to the repository.
It supports Git and hosted Git services, Mercurial, Subversion, Perforce, and TFS or Azure DevOps repositories, among others. IDE plugins cover the IntelliJ-based family plus Visual Studio and VS Code for remote runs.
Yes. Besides installing it on infrastructure you control, there is a cloud edition hosted by JetBrains where server maintenance falls to the vendor. Both use the same configuration concepts, which keeps moving between them manageable.
It keeps a per-test history, distinguishes unstable tests from consistent failures, can retry tests automatically, and groups results so a team can judge whether a failure is new or recurring before deciding to block a build.
JetBrains TeamCity is a CI/CD server for automating builds, tests, and deployments across Windows, Linux, and macOS. Teams use it to catch problems early, parallelize test suites, and release software through repeatable, auditable pipelines.
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