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, and2,500+ plugins.
Pick from our production-ready Blueprints for common data workflows. Copy, customize, deploy in minutes.
Download a JSON export of embeddings, reshape it into Pinecone's vector format with Python, upsert the file in one call, and report the count to Slack.
Fetch the requested vector ids, delete them, fetch again to prove absence, and post a before-and-after audit message to Slack.
Scheduled DescribeIndexStats check that compares the total vector count against a threshold, alerts Slack, and fails the execution on a breach.
Clear every vector in a stale Pinecone namespace with one deleteAll call, verify with a stats readback, and confirm the purge in Discord.
Create a Pinecone serverless index with a fixed dimension, cloud, and region, verify it with a stats readback, and confirm the bootstrap in Slack.
Query a Pinecone index by an existing vector's id, pull the top five neighbors with metadata, and post the match count to Discord for retrieval QA.
Audit Apache Pinot real-time table consuming segments for stalled commits, recycle wedged partition workers, and verify ingestion.
Classify an Apache Pinot table HEALTHY, STALE, or EMPTY_TABLE from its own ingestion lag, and alert Slack when a streaming consumer stalls.
Move Apache Pinot query results into BigQuery as a clean, schema-typed CSV load.
Join features to labels as they were known at each label's cutoff with a DuckDB ASOF join, audit the set for future rows and correlation lift, then publish it or block it.
High-performance data transformation with Polars, SQL quality gating with DuckDB, and publication to AWS S3 Lakehouse.
Ingest raw files, run streaming LazyFrame transformations with Polars, validate schema rules, and sink partitioned Parquet with Slack alerts.
Join GeoJSON points to service zones, including boundaries, and export matched, unmatched and ambiguous assignments as CSV.
Repair polygon batches in PostGIS, quarantine unsuitable features and approve the exact report before saving a snapshot.
Find PostgreSQL tables with autovacuum switched off or a lax vacuum scale factor, and a server with autovacuum off, alert Slack with the dead rows, and optionally reset, vacuum and verify.
Monitors transaction freeze horizon in pg_database to prevent emergency read-only lockups.
Schedule pg_dump database archives to Amazon S3 and test-restore them into a scratch database to verify backup integrity.
Daily and on-demand PostgreSQL backup pipeline using pg_dump and gzip, streaming to Amazon S3 with size verification and Slack alerting.
Detect PostgreSQL lock conflicts and cascading uncommitted blockers via pg_locks, alerting DBAs on Slack before connections exhaust.
Audit disabled integer foreign-key triggers, count existing orphan rows then restore enforcement under bounded locks and approval.
Download a JSON export of embeddings, reshape it into Pinecone's vector format with Python, upsert the file in one call, and report the count to Slack.
Fetch the requested vector ids, delete them, fetch again to prove absence, and post a before-and-after audit message to Slack.
Scheduled DescribeIndexStats check that compares the total vector count against a threshold, alerts Slack, and fails the execution on a breach.
Clear every vector in a stale Pinecone namespace with one deleteAll call, verify with a stats readback, and confirm the purge in Discord.
Create a Pinecone serverless index with a fixed dimension, cloud, and region, verify it with a stats readback, and confirm the bootstrap in Slack.
Query a Pinecone index by an existing vector's id, pull the top five neighbors with metadata, and post the match count to Discord for retrieval QA.
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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