# Temporal vs Airflow

> Pick Airflow for scheduled batch data pipelines. It has a long list of provider packages, asset-aware scheduling, lineage through OpenLineage, and several managed services. Pick Temporal when the workflow is application logic, such as orders, payments, provisioning or AI agents. It suits work that must survive failures, wait for signals or people, run for days or years, or be written in languages other than Python.

Canonical: https://www.techfabric.com/compare/temporal-vs-airflow
Written by TechFabric, a Databricks partner. Checked against the vendors' own pages on 2026-10-10.

## Temporal

An open-source durable execution platform that runs application code as workflows which keep their state through crashes and outages, self-hosted or on Temporal Cloud.

## Airflow

An open-source platform for developing, scheduling and monitoring batch workflows defined as Python DAGs.

## Choose Temporal when

- Business processes inside your product, like order fulfillment, payments or account provisioning, where the process has to pick up from the last completed step after a crash.
- Long-running or human-in-the-loop work that waits for days or months on signals, updates or timers, since a workflow has no imposed time limit.
- Teams writing workflows in Go, Java, TypeScript, .NET, Ruby, PHP or Rust as well as Python.
- AI agent loops that call models and tools and need retries, state and resumption without hand-built checkpointing.

## Choose Airflow when

- Nightly or hourly ETL and ELT jobs that move data between warehouses, lakes and SaaS tools, using ready-made provider operators.
- Pipelines that should run when upstream data assets update, using asset-aware and event-driven scheduling.
- Data teams that work in Python and want a UI built around DAG runs, backfills and data lineage.
- Organizations that want a managed service from their cloud provider, such as Google Cloud Composer or Amazon MWAA.

## Differences

| | Temporal | Airflow |
| --- | --- | --- |
| What it's for | Durable execution of application code: workflows keep their state and progress through failures, crashes or server outages. | Airflow's own docs call it a platform for orchestrating batch workflows with a defined start and end that run on a schedule. [1, 13] |
| Execution model | 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. | 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. [2, 14] |
| Failure handling | Activities retry by policy and a workflow resumes from its last recorded event after a worker or server failure. | 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. [2, 15] |
| Long-running and human-in-the-loop | Executions can last seconds or years. Signals, queries and Workflow Update (GA since January 2025) let people and systems interact with running workflows. | 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. [2, 3, 16] |
| Scheduling | Schedules start workflows by interval, calendar or cron spec, with overlap policies, backfill and pause. | Cron and time-based schedules, asset-triggered runs, and event-driven scheduling from message queues via AssetWatcher, added in 3.0. [4, 17, 18] |
| Languages | Official SDKs for Go, Java, Python, TypeScript, .NET, Ruby, PHP and Rust. | DAGs are always Python. Since 3.3, individual tasks can be written in Java or Go through an experimental language SDK. [5, 15, 19] |
| Scale model | 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. | You scale workers through executors, and Airflow can run several executors at once, for example Local and Celery, routing tasks per DAG or task. [6, 2, 20] |
| Observability | The Web UI lists executions and shows each one's full event history, pending activities and current call stack. | 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. [7, 17, 16] |
| Versioning | Because history is replayed, code changes to running workflows must stay deterministic, which Temporal handles through its versioning features. | Airflow 3 added DAG versioning. With a Git DAG bundle, each run records the commit it started on and finishes on that version. [2, 17, 21] |
| Data lineage and assets | No data asset or lineage model. Temporal tracks executions, not datasets. | Assets are first-class, with partitioning added in 3.2, and the OpenLineage provider emits lineage events from tasks and DAGs. [1, 17, 15, 22] |
| Hosting and pricing | Self-host the MIT-licensed server with your own database, or use Temporal Cloud on AWS or Google Cloud, billed per action and storage. | 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. [8, 9, 10, 23, 24, 25] |

## Pricing

Temporal: The Temporal server is MIT-licensed, so self-hosting costs only your infrastructure. Temporal Cloud bills actions, such as starting a workflow or activity, timers, signals, queries and updates, at $50 per million for the first 5 million each month, falling in tiers to $25 per million between 100 and 200 million; above 200 million, pricing comes from Temporal's sales team. Active storage costs $0.042 per GB-hour and retained storage $0.00105 per GB-hour. The Business plan fee is the greater of $500 a month or 10% of usage spend, and Enterprise and Mission Critical are priced annually.

Airflow: Airflow is free under the Apache License 2.0; self-hosting costs the infrastructure for the scheduler, API server, workers and metadata database. Managed services such as Astro, Cloud Composer and Amazon MWAA set their own prices on their own pages. [8, 9, 10, 11, 23, 24]

## Using both

Airflow can run the scheduled data pipelines while Temporal runs the application workflows, and each can start the other. Because a Temporal client is an ordinary SDK call (for example start_workflow in Python), an Airflow task can start a Temporal workflow, and a Temporal activity can call Airflow's REST API to trigger a DAG run. [12]

## Moving application DAGs from Airflow to Temporal

1. **Sort the DAGs first.** Sort DAGs first: keep scheduled batch ETL with heavy operator use in Airflow, and move only the ones that are really application processes, long waits or human approvals.
2. **Map DAGs to workflows.** Map each DAG to one workflow and each task to an activity, and move side effects such as API and database calls into activities so workflow code stays deterministic.
3. **Replace retries, sensors and schedules.** Replace Airflow retries and sensors with activity retry policies, timers and signals; replace the DAG schedule with a Temporal Schedule and pick an overlap policy.
4. **Replace XCom.** Replace XCom hand-offs with ordinary return values, and keep large payloads in storage because a request payload is capped at 2 MB on Temporal Cloud.
5. **Run both side by side.** Run both side by side for a period, comparing outputs, then switch the trigger over and retire the DAG.
6. **Leave data pipelines where they are.** Don't move if the DAG's value is its provider operators, asset-driven scheduling or lineage, since Temporal has no equivalent of those.

## Questions

### Is Temporal a replacement for Airflow?

Not usually. Airflow is built for scheduled batch data pipelines with assets and lineage, while Temporal is built for durable application workflows that must survive failures and wait on events. The two can run side by side, each triggering the other.

### Can Airflow handle human approvals and long waits now?

Partly. Airflow 3.1 added human-in-the-loop operators that pause a task and show a form in the UI. Temporal workflows can wait on signals, updates or timers for days or years, which suits processes that are mostly waiting.

### What does Temporal Cloud cost?

Two things set the bill. Every action counts, including starting a workflow or activity, timers, signals, queries and updates, at $50 per million for the first 5 million a month, falling in tiers to $25 per million between 100 and 200 million, with sales pricing above that. On top of that, the Business plan fee is at least $500 a month, so a small workload on that plan still pays $500. Self-hosted Temporal is free under the MIT license.

### What changed in Airflow 3?

Airflow 3.0, released April 2025, added DAG versioning, data assets, event-driven scheduling, a Task SDK with a client-server task execution interface, and a new UI. Later releases added human-in-the-loop tasks (3.1), asset partitioning (3.2), and a task state store with experimental Java and Go task SDKs (3.3, July 2026).

### Do I have to write Temporal workflows in Python?

No. Temporal has official SDKs for Go, Java, Python, TypeScript, .NET, Ruby, PHP and Rust. Airflow DAGs must be Python, though since 3.3 individual tasks can run Java or Go through an experimental SDK.

## Sources

1. [Understanding Temporal](https://docs.temporal.io/temporal), Temporal, read 2026-10-10
2. [Temporal Workflow Execution overview](https://docs.temporal.io/workflow-execution), Temporal, read 2026-10-10
3. [Announcing a new operation: Workflow Update](https://temporal.io/blog/announcing-a-new-operation-workflow-update), Temporal, read 2026-10-10
4. [Schedules](https://docs.temporal.io/schedule), Temporal, read 2026-10-10
5. [Temporal SDKs](https://docs.temporal.io/encyclopedia/temporal-sdks), Temporal, read 2026-10-10
6. [Temporal Cloud limits](https://docs.temporal.io/cloud/limits), Temporal, read 2026-10-10
7. [Temporal Web UI](https://docs.temporal.io/web-ui), Temporal, read 2026-10-10
8. [temporalio/temporal](https://github.com/temporalio/temporal), Temporal (GitHub), read 2026-10-10
9. [Temporal Cloud pricing](https://docs.temporal.io/cloud/pricing), Temporal, read 2026-10-10
10. [Service regions](https://docs.temporal.io/cloud/regions), Temporal, read 2026-10-10
11. [Temporal Cloud actions](https://docs.temporal.io/cloud/actions), Temporal, read 2026-10-10
12. [Temporal Python SDK: Temporal Client](https://docs.temporal.io/develop/python/temporal-client), Temporal, read 2026-10-10
13. [Apache Airflow documentation (3.3.2)](https://airflow.apache.org/docs/apache-airflow/stable/index.html), Apache Software Foundation, read 2026-10-10
14. [Architecture overview](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/overview.html), Apache Software Foundation, read 2026-10-10
15. [Apache Airflow 3.3.0: Stateful Tasks and Multi-Language Support](https://airflow.apache.org/blog/airflow-3.3.0/), Apache Software Foundation, read 2026-10-10
16. [Apache Airflow 3.1.0: Human-Centered Workflows](https://airflow.apache.org/blog/airflow-3.1.0/), Apache Software Foundation, read 2026-10-10
17. [Apache Airflow 3 is Generally Available!](https://airflow.apache.org/blog/airflow-three-point-oh-is-here/), Apache Software Foundation, read 2026-10-10
18. [Event-driven scheduling](https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/event-scheduling.html), Apache Software Foundation, read 2026-10-10
19. [Non-Python Task SDKs](https://airflow.apache.org/docs/apache-airflow/stable/authoring-and-scheduling/language-sdks/index.html), Apache Software Foundation, read 2026-10-10
20. [Executor](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/executor/index.html), Apache Software Foundation, read 2026-10-10
21. [Dag Bundles](https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/dag-bundles.html), Apache Software Foundation, read 2026-10-10
22. [OpenLineage Airflow integration](https://airflow.apache.org/docs/apache-airflow-providers-openlineage/stable/guides/structure.html), Apache Software Foundation, read 2026-10-10
23. [Apache License, Version 2.0](https://airflow.apache.org/docs/apache-airflow/stable/license.html), Apache Software Foundation, read 2026-10-10
24. [Ecosystem](https://airflow.apache.org/ecosystem/), Apache Software Foundation, read 2026-10-10
25. [What is an Apache Airflow job?](https://learn.microsoft.com/en-us/fabric/data-factory/apache-airflow-jobs-concepts), Microsoft, read 2026-10-10