Buying snapshot
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| Decision point | What Kestra states |
|---|---|
License | Apache 2.0 for the open-source code on GitHub. |
Self-hosting | Open Source runs on Docker, Kubernetes with Helm charts, or a single VM. The README also offers an AWS CloudFormation template and a Google Cloud Terraform module. |
Task languages | Python, Node.js, R, Go, Shell, SQL and others. Kestra says existing scripts work unchanged, and Docker is enabled by default. |
Queue and repository | Open Source runs both on PostgreSQL or MySQL. The Enterprise card also lists Kafka, Redis and AMQP for the queue. |
Automation | API coverage across the platform, an official Terraform provider, the kestractl CLI, Python and JavaScript SDKs and a VS Code extension. |
Support | Open Source: community Slack, GitHub discussions, docs and Blueprints. Enterprise: dedicated support, SLAs and training. |
Our take
Choose Kestra if your workflows mix schedules, events and scripts in several languages and you want them reviewed as YAML in Git. The free open-source edition lets you prove that fit on your own infrastructure before you talk to sales about Cloud or Enterprise.
Evaluate Kestra
Comparisons
Where it stands out
Code review without shutting out non-developers. The YAML editor, the no-code editor and the AI Copilot all edit the same flow and stay in sync, and the YAML updates itself when someone changes a flow from the UI or the API. Flows can be pushed to a Git branch from Kestra, and draft revisions let you stage changes while triggers keep running the last published revision.
Schedules and events in one engine. Flows can start on intervals, CRON or conditions, on webhooks and API calls, or on events such as file arrivals and messages from Kafka, Pulsar, MQTT, AWS SQS, Google Pub/Sub or Azure Event Hubs. Backfills catch up after an outage from the UI, without redeploying code. Kestra says event triggers are unlimited in Open Source.
Tasks can run where the work is. Workers talk to the control plane over gRPC, so they can run in another region or inside a restricted network. Tasks can also run on remote servers over SSH, in Docker containers or as Kubernetes jobs.
A full record of each run. Every run becomes an execution with status per task, logs, outputs and timing, and retries, timeouts, concurrency limits and conditional branching are built-in controls.
AI steps inside ordinary flows. AI agents and RAG pipelines are task types, one task interface calls OpenAI, Anthropic, Google Gemini, Amazon Bedrock, Ollama and other providers, and token usage is reported per execution. A built-in MCP server exposes flows as tools to MCP-compatible agents.
Product screenshot
Trade-offs to weigh
Identity controls set the edition. Kestra describes RBAC, SSO, audit logs and multi-tenancy as what Enterprise adds to Open Source, so a self-hosted team that needs them is buying Enterprise from the start. On Kestra Cloud, Kestra says SSO and compliance alignment depend on your plan; confirm both before you sign.
Upgrading to 2.0 takes migration work. Kestra 2.0 has breaking changes: a single Loop task replaces ForEach and ForEachItem, and trigger conditions are now written as
when. Kestra publishes a 2.0 migration guide, and its Terraform provider uses ~> 2.0 for Kestra 2.x.Open Source means running it yourself. You operate the servers and the PostgreSQL or MySQL database; Kestra Cloud is the fully managed version, where Kestra handles hosting, upgrades and backups. The quick-start command uses the slim image, which fetches each plugin from Maven Central the first time a flow needs it; the kestra/kestra:latest image bundles them up front.
Pricing and plan conditions
Enterprise is licensed per instance rather than by workload, so the number of Kestra instances you run decides what you license. Ask how separate development and production instances count; Enterprise multi-tenancy can isolate teams or environments inside one instance. Kestra describes Cloud as pay-as-you-scale, so ask which usage it meters before comparing it with an Enterprise quote.
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| Edition | How it is sold | What the edition lists |
|---|---|---|
Open Source | Free Forever. Self-host on Docker or Kubernetes. | Declarative workflows, 2,300+ plugins (most are in both Open Source and Enterprise), event-driven scheduling, business logic in any language, code and no-code editors, an MCP server for AI agents, and unlimited flows and executions. |
Kestra Cloud | Request Access. Kestra says you pay only for what you use. | Everything in Open Source, plus a fully managed platform (hosting, upgrades, monitoring), automatic scaling, built-in security and governance features, Enterprise Plugins and SOC2 compliance. |
Enterprise Edition | Contact Sales. Annual subscription on a per-instance model, with unlimited flows, tasks and executions. | SSO, LDAP and SCIM; role-based access control; Policies; Promote; audit logs and Cases; multi-tenancy and storage isolation; worker groups; task runners on containers or VMs; plugin versioning; a log shipper and external log data store; a secrets manager; Enterprise Plugins; an agentic AI Copilot on any LLM; apps and reusable inputs; a customer success program and enterprise support with SLA. Runs in cloud, on-premises or air-gapped environments. |
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| Enterprise support level | Channels | P0 SLA | Coverage | Expert Advisory Services |
|---|---|---|---|---|
Standard (included by default) | 24h | 8x5 | Available as add-on | |
Premium | Email and a dedicated Teams or Slack channel | 6h | 8x5 | Available as add-on |
Platinum | Email and a dedicated Teams or Slack channel | 1h | 24x7 | 6 hours per month |
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
2.0k GitHub commits 90d30.2k GitHub stars0 vulnerabilities across 1 packageOpenSSF score 6.3/10
See all signals from 8 sources
PyPI · kestra@2.0.0
Alternatives to Kestra
The reviewed substitutes for Kestra among the workflow orchestrators, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- Mage
- Kestra and Mage are open-source orchestration tools for building and scheduling data pipelines. Kestra defines workflows in declarative YAML with a low-code UI, Git sync and a large plugin library for tasks in any language, while Mage is Python-centered with a notebook-style editor for data integration and transformation.Applies to: Choosing between these two for the python data orchestration decision.
Other approaches
A different approach to the same problem. Each substitutes only for the workload named beside it.
- Apache Airflow
- Both schedule and monitor data workflows; Kestra is declarative YAML, Airflow is Python DAGs.Applies to: Scheduling and monitoring recurring data pipelines.
- Prefect
- Choose Prefect if you want the fastest path from a Python script to a production-grade workflow with retries, observability, and caching built in.Applies to: Open-source orchestration paired with a hosted control plane.
- Dagster
- Choose Dagster if your primary concern is data quality and observability across a complex analytics stack, and you want built-in lineage tracking without separate tooling.Applies to: Orchestrating data pipelines with a managed control plane.
Related technologies
Normally used together rather than chosen between, so these are not alternatives.
- Fivetran
- An ELT tool lands data; an orchestrator decides what runs, in what order, and what happens when a step fails. The documented pattern runs both — Fivetran ships an Airflow provider, and code-first movement libraries are written to run inside an orchestrator — so the pair is a pipeline-and-scheduler pairing rather than a substitution. The question readers actually ask is whether both are needed.Applies to: Whether a managed or code-first ingestion tool removes the need for an orchestrator, or runs inside one.