For the Data Engineers Who Make It All Work
Apache Airflow® orchestrates the world’s data, ML, and AI pipelines. Astro is the best way to build, run, and observe them at scale.
Trusted by companies winning with data and AI
Data engineers deserve better.
For years, data engineers were the people who kept the lights on. Essential but invisible. Today, data engineers are building the systems that provide the context to make AI work in production systems. In the AI era, that architecture is your moat. We give you the tools to build it.
Designed for data engineers who build systems. And the agents they work with.
The last mile is where the value of AI becomes real. Agents need access to your pipelines, context about your data, and the ability to fix what breaks. Demo-ready is easy, working in production isn’t.
Tools
Airflow MCP server grants agents programmatic access to Airflow to control and manage your pipelines.
Skills
Help agents understand Airflow internals, best practices, and safe change patterns so they don't break production workflows.
Data Platform Context
Lineage, schemas, and metadata from your entire stack give agents full visibility into cross-system dependencies.
RCA Agent
AI that pinpoints root causes of pipeline failures by analyzing task logs, worker metrics, and execution context.
Raise the ceiling of data engineering with Airflow you never have to babysit
You write DAGs, Astro runs them reliably at any scale. No Kubernetes expertise required. No ops burden. Just Airflow that works the way it should.
- ✓ Created project directory
- ✓ Generated example DAG
- ✓ Project ready!
- Building image...
- Deploying to Astro...
- ✓ Deployment complete!
Architecture & Performance
The Astro Engine
Three components purpose-built for Airflow: a hardened runtime, an agent-based executor, and an optimization layer that manages health and resources across deployments on any major cloud. The Astro Executor handles higher concurrency, recovers from failures automatically, and stays efficient across a wide range of workloads without constant tuning.
How we benchmarked performance
Same infrastructure, same workloads, same rules. We ran 5,400 DAGs across Astro, MWAA, and GCP Composer using a mix of CPU, memory, and I/O tasks that mirror real production patterns.
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