Airflow 2 end of life and the reliability gap

Airflow 2 reached end of life on April 22, 2026. This leaves 60% of the industry on unpatched software. Airflow 3 downloads doubled since January 2026. This growth brings the share to nearly 40% of the total market share. Airflow 3.3.2 is the current version. It supports 80,000 organizations and 30 million monthly downloads. Reliability defines the divide. Airflow runs a scheduler loop that parses Python files. The system uses Python-based Directed Acyclic Graphs with @dag and @task decorators. Failed workers trigger retries from the top. In contrast, Temporal runs an event-sourced log where the history is the source of truth. When a worker dies mid-task in Airflow, the task fails and retries from the top, but Temporal replays event history to rebuild the state of local variables on a healthy worker so the process resumes exactly where it stopped.

Operational footprints and pricing

Teams evaluating orchestration engines must account for operational footprints and scaling bottlenecks. Airflow requires a metadata database, scheduler, API server, triggerer, and workers to run. The infrastructure needs at least 4GB of RAM for modest workloads. Airflow 3.3.0, which released on July 6, 2026, added the experimental Language Task SDK to support Java and Go binaries. This update helps narrow the gap for polyglot teams.

Cadence, the predecessor to Temporal, remains a direct alternative for teams that want full control over deployment. Cadence is a CNCF Sandbox project originally built at Uber. It uses domains to give teams isolated namespaces with separate quotas and rate limits to prevent noisy-neighbor problems. Migrating between Cadence and Temporal still requires mapping SDK packages and concept renames by hand. This is a manual process for engineers. Cadence supports self-hosting on SQLite, Docker, or Kubernetes. Teams migrating from Temporal Cloud to a self-hosted cluster use a dual-client architecture. This allows services to instantiate both a new client for Temporal Cloud and a self-hosted client. Feature flags at the infrastructure level control the connection to the self-hosted cluster. For on-demand workflows, a feature flag per tenant determines which cluster executes the workflow. For schedules, a dual-writer pattern creates and updates schedules in both clusters. The schedule remains in a paused state on the target cluster until migration is complete.

Managed Airflow via Amazon MWAA costs $365 per month for a small environment in US East. Large environments charge $0.99 per hour. Temporal Cloud bills by actions, starting at $50 per million, and charges $0.042 per GB-hour for active storage. Astronomer Astro starts at $0.35 per hour per deployment on the Developer plan. You should know that Airflow debugging remains frustrating when finding a root cause after a failure.

Workflow requirements and stateful execution

The decision between Airflow and Cadence rests on the specific requirements of the workload. Airflow serves data engineering teams building ETL/ELT pipelines and batch data workloads. It uses Python-based Directed Acyclic Graphs. Airflow 3.3 introduced AIP-103, a first-class task and asset state store that allows tasks to persist key-value state. This allows tasks to survive retries, but it is not the same as full durable execution. In Airflow 3.3, the SparkSubmitOperator defaults to "durable=True" to reconnect to an existing Spark job instead of resubmitting after a worker dies. Temporal and Cadence serve application developers who manage stateful, long-running processes. These tools handle payment flows, order fulfillment, and multi-step provisioning.

Temporal uses a History service to maintain the state of all workflow executions. This service is the most resource-intensive component and typically becomes the scaling bottleneck. The Matching service then dispatches tasks to polling Workers. Frontend handles all client requests, including starting workflows and querying state. The persistence store, such as PostgreSQL, MySQL, or Cassandra, holds the workflow state and task queues. If you need a tool that handles complex branching logic via a graph, will Airflow satisfy your needs?

Pick Airflow if you need to schedule nightly warehouse loads. Pick Cadence or Temporal if you need to manage application logic where losing progress is unacceptable. For teams building stateful multi-step flows, Temporal is the correct choice.