Temporal vs Airflow

What it's for^1,13

Temporal: Durable execution of application code: workflows keep their state and progress through failures, crashes or server outages.

Airflow: Airflow's own docs call it a platform for orchestrating batch workflows with a defined start and end that run on a schedule.

Execution model^2,14

Temporal: Workflow code is recorded as an event history and replayed to recover state, so workflow code must be deterministic. Side effects run in activities on your own workers.

Airflow: A scheduler submits DAG tasks to an executor such as Celery or Kubernetes. In Airflow 3, tasks report state through the API server instead of touching the metadata database.

Failure handling^2,15

Temporal: Activities retry by policy and a workflow resumes from its last recorded event after a worker or server failure.

Airflow: Tasks retry by count, and Airflow 3.3 adds pluggable retry policies plus a state store so tasks can keep checkpoints across retries and runs.

Long-running and human-in-the-loop^2,3,16

Temporal: Executions can last seconds or years. Signals, queries and Workflow Update (GA since January 2025) let people and systems interact with running workflows.

Airflow: Airflow 3.1 added human-in-the-loop operators that pause a task in a deferred state and show a form in the Airflow UI.

Scheduling^4,17,18

Temporal: Schedules start workflows by interval, calendar or cron spec, with overlap policies, backfill and pause.

Airflow: Cron and time-based schedules, asset-triggered runs, and event-driven scheduling from message queues via AssetWatcher, added in 3.0.

Languages^5,15,19

Temporal: Official SDKs for Go, Java, Python, TypeScript, .NET, Ruby, PHP and Rust.

Airflow: DAGs are always Python. Since 3.3, individual tasks can be written in Java or Go through an experimental language SDK.

Scale model^6,2,20

Temporal: You scale workers horizontally; on Temporal Cloud a namespace starts at a 500 actions-per-second limit, and one execution's history is capped at 51,200 events or 50 MB, so very long workflows use Continue-As-New, which restarts the workflow with a fresh history.

Airflow: You scale workers through executors, and Airflow can run several executors at once, for example Local and Celery, routing tasks per DAG or task.

Observability^7,17,16

Temporal: The Web UI lists executions and shows each one's full event history, pending activities and current call stack.

Airflow: The React-based UI added in Airflow 3 shows DAG runs, task logs and versions, and Airflow 3.1 added deadline alerts for runs that overrun.

Versioning^2,17,21

Temporal: Because history is replayed, code changes to running workflows must stay deterministic, which Temporal handles through its versioning features.

Airflow: Airflow 3 added DAG versioning. With a Git DAG bundle, each run records the commit it started on and finishes on that version.

Data lineage and assets^1,17,15,22

Temporal: No data asset or lineage model. Temporal tracks executions, not datasets.

Airflow: Assets are first-class, with partitioning added in 3.2, and the OpenLineage provider emits lineage events from tasks and DAGs.

Hosting and pricing^{8,9,10,23,24,25}

Temporal: Self-host the MIT-licensed server with your own database, or use Temporal Cloud on AWS or Google Cloud, billed per action and storage.

Airflow: Apache License 2.0 and free to self-host. Managed services include Astro, Google Cloud Composer, Amazon MWAA and Apache Airflow jobs in Microsoft Fabric.