Kestra has streamlined our data processes, reduced costs, and significantly enhanced our scalability and efficiency. It has truly been a critical asset in our digital transformation journey.
Data Orchestration
Ship Data Pipelines in 5 Minutes.
Run Them at Enterprise Scale
One platform for data, AI, and business-critical workflows.
Used by teams at 
Data Orchestration Shouldn’t Be Hard
The challenge
- Airflow gives you dependency conflicts, executor tuning, infra complexity instead of building pipelines.
- Analysts use SQL. ML engineers use Python. Ops use Bash. But your orchestrator only speak Python.
- Weeks learning DSL patterns before shipping one pipeline.
How Kestra solves it
- Docker install and you’re running. No dependency management, no executor configuration. Just workflows.
- Write tasks in any language. YAML orchestrates, your code stays native. No wrappers, no refactoring required.
- Know YAML? You’re ready. Pick a Blueprint and ship in under 5 minutes. Zero proprietary abstractions.
+900%in data production
+250active users
+5,000workflows created
Built For How Data Teams Actually Work
Any Language, Native Execution
Define pipelines in declarative YAML. Run tasks in Python, SQL, R, Bash, or any script. Execute in isolated containers on Docker, Kubernetes, or custom workers.
Start From Blueprints, Ship in Minutes
Pick from 200+ production-ready blueprints for dbt, Airbyte, Spark, and ML. Customize in a live YAML editor with real-time DAG preview. Ship your first pipeline in minutes.
Enterprise-Grade Observability
Built-in monitoring and distributed tracing. Real-time logs, Gantt views, and alerts. SLA tracking included. Integrates with Datadog, Prometheus, and Grafana.
Built For The Stack You Run in Production
Native integrations for dbt, Airbyte, Spark, Snowflake, BigQuery, Databricks, and 1200+ integrationsfrom event-driven triggers for files, APIs, databases, and queues.
Version Control Native + CI/CD Ready
YAML lives in Git. Code review, branch-based development, automated testing, and rollbacks all work with your existing workflow. Deploy via CLI, API, or Terraform. Supports GitOps patterns.
Scale From Prototype to Enterprise
Handles 100,000+ concurrent tasks with built-in multi-tenancy. High availability with automatic failover. Start with data pipelines, expand to AI, and business critical workflows.
Cut Orchestration Costs By 80% *
Reported by teams switching from Airflow to Kestra in production.*
Infrastructure costs
Self-hosted Kestra runs on minimal infrastructure. No overprovisioned workers or complex component scaling. Deploy on your existing Kubernetes cluster or a single VM.
Engineering time
Eliminate weeks of operational overhead. No upgrades breaking plugins, no dependency conflicts, no executor tuning. Ship pipelines, not infrastructure.
Licensing and managed services
Many orchestrators lock enterprise features behind expensive managed plans. Kestra delivers them in open source or flexible commercial licensing.
From Prompt to Production in 60 Seconds
Kestra Copilot turns natural language into working pipelines using dbt, Airbyte, Spark, Snowflake, BigQuery, Databricks, and 1200+ plugins.
AI Copilot
Add
Start From Proven Data Patterns
Pick from our production-ready Blueprints for common data workflows. Copy, customize, deploy in minutes.
1200+ Plugins To Works Automate
Fivetran
Snowflake
Databricks
Azure
DBT
Airbyte
MongoDB
Cassandra
PostgreSQL
MySQL
Redshift
BigQuery
ClickHouse
Redis
Elasticsearch
Kafka
Pulsar
RabbitMQ
Fivetran
Snowflake
Databricks
Azure
DBT
Airbyte
MongoDB
Cassandra
PostgreSQL
MySQL
Redshift
BigQuery
ClickHouse
Redis
Elasticsearch
Kafka
Pulsar
RabbitMQ
Kestra is Not Just Another Orchestrator
| | Legacy orchestrators (e.g. Airflow) | Modern alternatives | Why this matters | |
|---|---|---|---|---|
| Workflow definition | Declarative YAML | Python DAGs | Python SDKs with custom abstractions | Your existing scripts work as-is. Zero migration tax. |
| Languages supported | Any (Python, SQL, R, Bash, etc.) | Python only | Python only | Analysts use SQL, engineers use Python, ops use Bash. |
| Time to first pipeline | < 5 minutes | 1-2 hours | 30-60 minutes | Ship 10x faster. Deliver value day one. |
| Learning curve | YAML (immediate) | Framework + infrastructure setup | Framework concepts and abstractions | Junior engineers ship pipelines their first day. |
| Live DAG preview | Yes, real-time | No (deploy to see) | No (deploy to see) | See changes instantly. No deploy-to-preview cycles. |
| Event-driven triggers | Native, unlimited | Limited sensors (polling-based) | Yes, native support | React in seconds, not minutes, with real-time workflows. |
| Execution isolation | Containers per task | Shared workers | Configurable | No dependency conflicts. Ship fearlessly. |
| Built-in observability | Metrics, logs, tracing included | Requires manual setup | Setup required or cloud-dependent | Debug in minutes with full visibility out of the box. |
| Multi-language team support | SQL, Python, Bash all native | Python wrappers required | Python wrappers required | Everyone contributes using their native tools. |
| Scope beyond data | AI, infra, business workflows | Data pipelines only | Data pipelines primary focus | Future-proof your platform with one orchestrator for everything. |
| Enterprise deployment | Self-hosted or cloud | Self-managed infrastructure | Self-hosted or cloud-first | 50%+ lower TCO. Less infrastructure, less ops time. |
| Migration path | Incremental (run Airflow + Kestra in parallel) | N/A | Requires full rewrite | Move pipelines one by one, no big-bang cutover. |
Start With Data. Grow Without Limits.
Join 500+ data teams who’ve modernized their orchestration with Kestra.
Frequently asked questions
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