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Workflow Orchestration🟡Low Code
K

Kestra

Open-source event-driven orchestration platform with declarative YAML flows, AI/LLM plugins, and a full web UI — an Airflow alternative for the modern data + AI stack.

Starting atFree (Apache 2.0)
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In Plain English

Open-source event-driven orchestration platform with declarative YAML flows, AI/LLM plugins, and a full web UI — an Airflow alternative for the modern data + AI stack.

OverviewFeaturesPricingUse CasesFAQ

Overview

Kestra is an open-source orchestration platform used to run scheduled jobs, event-triggered pipelines, and AI/agent workflows at scale. Flows are declared in YAML — so they live in Git and are reviewable in pull requests — but the platform ships a rich web UI where non-engineers can run, monitor, and edit flows visually. Where legacy orchestrators like Airflow feel weighed down by Python operators and DAG boilerplate, Kestra separates orchestration from execution: any language, container, script, dbt project, Spark job, or LLM call can run inside a task, and the engine handles retries, backfills, dependencies, and parallel fan-out declaratively. Its 600+ plugins cover cloud storage, databases, warehouses (Snowflake, BigQuery, Redshift), messaging (Kafka, Pub/Sub), and — increasingly — LLM providers (OpenAI, Anthropic, Bedrock, Ollama) plus AI-specific plugins for embeddings, RAG, and prompt chaining. Kestra also acts as an MCP server so agent hosts like Claude Desktop and Cursor can inspect flows, trigger executions, and query state. Deployable via Docker or Kubernetes with a Postgres backend, Kestra has an active OSS community and a commercial Enterprise Edition offering SSO, RBAC, secrets managers, worker groups, and audit logs. Popular use cases include AI content pipelines, ETL with LLM enrichment, event-driven agent orchestration, and CI/CD for ML models.

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Key Features

YAML + UI: flows are declarative YAML, so they diff cleanly in Git and are safe to code-review, but the web UI lets analysts trigger, monitor, and even edit flows without touching a repo — bridging the divide between engineering and business ops teams that Airflow forces you to choose between.+
Language-agnostic execution: tasks can be Python, Node, shell, dbt runs, Spark jobs, or arbitrary Docker containers. The engine handles retries, backfills, timeouts, and parallel fan-out declaratively — no more scattering conditional Python across a DAG.+
AI-native plugin surface: dedicated plugins for OpenAI, Anthropic, Bedrock, Vertex, Ollama, embedding models, and vector stores mean LLM steps are first-class citizens rather than 'call this Python function' escape hatches. Good fit for RAG ingestion pipelines and evaluation loops.+
MCP server: agent hosts can inspect flow definitions, trigger executions, and query run state, so agents can use Kestra as a durable execution layer instead of hand-rolling their own retry logic.+

Pricing Plans

Open Source

Free (Apache 2.0)

  • ✓Self-host on Docker or Kubernetes
  • ✓All 600+ plugins
  • ✓Full web UI + REST API
  • ✓Community support

Kestra Cloud

Contact sales

  • ✓Fully-managed control plane
  • ✓Autoscaling execution
  • ✓SSO / SAML
  • ✓Storage isolation
  • ✓Higher availability guarantees

Enterprise Edition

Contact sales

  • ✓Self-host or Cloud
  • ✓SSO/SAML + advanced RBAC
  • ✓Worker groups + namespaces
  • ✓Multi-tenant + isolation
  • ✓Audit logs
  • ✓Dedicated support + SLA
See Full Pricing →Free vs Paid →Is it worth it? →

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Best Use Cases

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AI content generation pipelines with LLM enrichment

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Data warehousing ETL with dbt + LLM classification

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Event-driven agent orchestration

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MLOps: retraining, evaluation, deployment

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Teams migrating off Airflow for a modern UI and lower boilerplate

Pros & Cons

✓ Pros

  • ✓Open-source and generous — no per-task or per-user fees on the OSS edition
  • ✓YAML-plus-UI split serves both engineers (Git) and analysts (visual editor) cleanly
  • ✓600+ plugins mean fewer custom operators to write vs. Airflow
  • ✓First-class LLM plugins make AI pipelines idiomatic instead of hacky
  • ✓MCP support lets agents use Kestra as a reliable execution backbone

✗ Cons

  • ✗Java-based engine has a heavier baseline resource footprint than lightweight Python schedulers
  • ✗Enterprise pricing not published — requires sales conversation for RBAC/audit features
  • ✗Smaller ecosystem than Airflow — some third-party providers still need custom plugins
  • ✗Learning curve for teams coming from Airflow (DAG-vs-Flow mental model differs)

Frequently Asked Questions

How much does Kestra cost?+

Kestra pricing starts at Free (Apache 2.0). They offer 3 pricing tiers.

What are the main features of Kestra?+

Kestra includes Declarative YAML flows checked into Git, 600+ plugins including LLM providers, embeddings, dbt, Spark, Snowflake, Kafka, Event-driven and cron-scheduled triggers and 4 other features. Open-source event-driven orchestration platform with declarative YAML flows, AI/LLM plugins, and a full web UI — an Airflow alternative for the modern...

What are alternatives to Kestra?+

Popular alternatives to Kestra include [object Object], [object Object], [object Object], [object Object]. Each offers different features and pricing models.
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Quick Info

Category

Workflow Orchestration

Website

kestra.io
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