Apache Airflow™ is an open source platform for orchestrating batch workflows. In Airflow, pipelines are defined in Python code as directed acyclic graphs (DAGs), meaning you can generate workflows dynamically and connect them with virtually any technology, either through ready made packages from third party providers or your own extensions. A modular architecture of Airflow and a built in message queue ensures its high scalability. Airflow’s user interface provides overviews and in depth views of pipelines and tasks.
Define and version workflow logic in Python DAGs.
Run time-based or event-driven jobs with robust retry policies.
Connect to external systems with operators, hooks, and provider packages.
Monitor task execution, failures, and dependencies from the Airflow interface.
Additional Nebius infrastructure costs may apply. Use the Nebius Pricing Page to estimate your infrastructure costs.
Deploy Apache Airflow™ on Kubernetes.
Some applications are easier with a hand on the wheel. Talk to an architect who has deployed this in production.
Reliable AI infrastructure backed by top-tier NVIDIA GPUs, purpose-built for demanding inference workloads. Multiple deployment methods — virtual machines for full hardware control, Kubernetes for scalable cluster deployments, and managed serverless applications for teams that want inference running without infrastructure overhead
Learn about Nebius AI CloudNebius meets a broad set of security and compliance standards. Fine-grained IAM controls, audit logs, and encrypted storage are available out of the box — so teams can meet security requirements without additional tooling.
Explore the Trust centerApplication support
Provided by Apache. See the documentation and project links above.
Infrastructure support
Provided by Nebius for the underlying cloud infrastructure.
