One platform for data, AI, and business-critical workflows.
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.
Reported by teams switching from Airflow to Kestra in production.*
Self-hosted Kestra runs on minimal infrastructure. No overprovisioned workers or complex component scaling. Deploy on your existing Kubernetes cluster or a single VM.
Eliminate weeks of operational overhead. No upgrades breaking plugins, no dependency conflicts, no executor tuning. Ship pipelines, not infrastructure.
Many orchestrators lock enterprise features behind expensive managed plans. Kestra delivers them in open source or flexible commercial licensing.
Kestra Copilot turns natural language into working pipelines using dbt, Airbyte, Spark, Snowflake, BigQuery, Databricks, and2,600+ plugins.
Pick from our production-ready Blueprints for common data workflows. Copy, customize, deploy in minutes.
Extract JSON from a REST API, enrich it, and load it in parallel to Postgres and Amazon S3.
Extract data from a REST API, reshape it with Python and Polars, then run analytical SQL on it with DuckDB, all in one Kestra flow.
Runs parallel endpoint probes, aggregates batch metrics in DuckDB, scores drift in Python, and quarantines bad partitions.
Audit an Apify actor's most recent run status, alert Slack when it failed, timed out, or was aborted, and optionally launch a fresh retry run under a cost cap.
Run an Apify actor under hard cost and run caps, then branch on the returned item count so a degraded scrape pages the on-call instead of silently shipping bad data.
Run an Apify actor on a schedule, fetch its dataset, reshape the records, and bulk-load them into Postgres with cost caps and Slack alerts.
Run an Apify actor, pull its dataset, and publish the scraped results straight into a Notion page.
Search indexed YouTube transcripts and export timestamped passages, source links, and query details for research and video planning.
Audit Argo Workflows for workflows stuck Running far longer than reasonable, then safely terminate them with operator approval and verification.
Probe an Apache Arrow Flight SQL endpoint and alert Slack when it answers over an unencrypted connection in violation of your own TLS policy.
Fetch new arXiv papers for a query, skip duplicates, score relevance with Gemini against your team's interests and post the best to Slack.
Compare a candidate AsyncAPI document with the blessed baseline, alert Slack on removed channels or required fields, and publish the verdict.
Fetch customer feedback, analyze sentiment and categorize topics using OpenAI, aggregate results with DuckDB, and send a summary to Slack.
Audits Apache Avro data contracts, automatically infers schemas from raw streams, verifies bidirectional serialization, and alerts Slack on breaking drift.
Daily AWS Cost Explorer pull that compares yesterday's spend to your budget and alerts Slack on anomalies.
Audit Amazon DynamoDB ThrottledRequests via CloudWatch, detect provisioned capacity starvation, open GitHub issues, and alert Slack.
Watch a CloudWatch backlog metric and fire an EMR Serverless Spark job only when work is pending.
Run a Glue ETL job, validate the output with Athena, then cache the headline KPI in DynamoDB.
Audit an AWS Glue job run's status, alert Slack when it failed or has run far longer than expected, and optionally stop a stuck run with StopJobRun.
List every AWS MSK cluster in a region, alert Slack the moment one enters state FAILED, and classify API reachability separately from cluster health.
Extract JSON from a REST API, enrich it, and load it in parallel to Postgres and Amazon S3.
Extract data from a REST API, reshape it with Python and Polars, then run analytical SQL on it with DuckDB, all in one Kestra flow.
Runs parallel endpoint probes, aggregates batch metrics in DuckDB, scores drift in Python, and quarantines bad partitions.
Audit an Apify actor's most recent run status, alert Slack when it failed, timed out, or was aborted, and optionally launch a fresh retry run under a cost cap.
Run an Apify actor under hard cost and run caps, then branch on the returned item count so a degraded scrape pages the on-call instead of silently shipping bad data.
Run an Apify actor on a schedule, fetch its dataset, reshape the records, and bulk-load them into Postgres with cost caps and Slack alerts.
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
| 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. |
Join 500+ data teams who’ve modernized their orchestration with Kestra.
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