Apache Airflow, Astro, and Temporal: Choosing the Right Orchestrator
Compare Apache Airflow and Temporal on data pipelines, AI agents, and event-driven workflows, and explore where the two fit together in the same architecture.
Apache Airflow and Temporal are both open-source orchestration platforms, but they orchestrate different things.
- Airflow orchestrates data: pipelines, analytics, ML training, and increasingly AI agents that work across enterprise data.
- Temporal orchestrates application state: microservices, transactions, and long-running business processes.
For most of their history that made the choice straightforward. AI and event-driven workloads have since blurred the line, so this guide covers where each is the stronger fit, where they now overlap, and how engineering teams increasingly run both in the same architecture.
At a Glance: Comparing Airflow and Temporal
| Apache Airflow (on Astro) | Temporal | |
|---|---|---|
| Orchestration model | Data pipeline and AI workflow orchestration, built on with asset-aware Dags and event-driven scheduling | Event-sourced, stateful workflows with durable execution and replay |
| Primary domain | Analytics, ML training, and agentic data workflow grounded in enterprise data | Microservices, transactions, and long-running business processes |
| Durable execution | A failed task reconnects to the job already in progress, whether that's a long running analytics job or a model fine-tune, instead of resubmitting it and paying for the compute or GPU time twice over | Whole-workflow event-sourced replay of arbitrary application state, including in-memory variables |
| AI & agents | Native agent and LLM tasks inside the pipeline; model-agnostic; human-in-the-loop review | Durable execution layer for agents built with frameworks like the OpenAI Agents SDK and Google ADK |
| Ecosystem | 2,000+ connectors across warehouses, lakes, data platforms, and AI/ML frameworks and harnesses | Custom integration via SDKs; no data-specific connector library |
| Language support | Python-first authoring, with experimental support for writing individual tasks in Java, JavaScript and Go | First-class SDKs for Go, Java, TypeScript, Python, and .NET, with Rust in preview |
| Deployment | Managed multi-cloud (Astro), Remote Execution for hybrid setups, and self-hosted/air-gapped via Astro Private Cloud, self-hosted open source | Self-hosted open source or Temporal Cloud |
| Governance | Apache Software Foundation project; no single company controls the roadmap | Commercial product from Temporal Technologies |
| Best fit | Data and AI pipelines, typically owned by a data or platform team | Application workflows, typically owned by an application engineering team |
How do Airflow and Temporal differ?
Airflow orchestrates data workflows. The core unit is the Dag: a pipeline that moves and transforms data across source and target systems, from warehouses and lakes to APIs, ML models, and AI agents, triggered on a schedule, when data arrives, or via an API call. Individual tasks handle retries and idempotency; the scheduler handles timing and dependencies between them. A large ecosystem of pre-built connectors means most data platforms are a few lines of configuration away.
Temporal orchestrates application state. The core unit is the workflow: durable, resumable code that models a business process, waiting on a signal, calling external services, surviving a crash or deploy without losing its place. Activities, Temporal's equivalent of a task, hold anything with a side effect, while the workflow itself replays deterministically from recorded history. Temporal has no scheduler, no data connectors, and no concept of a data pipeline; those are things a team builds on top of it, by design.
Neither model is "better" in the abstract. A pipeline that transforms a table doesn't need whole-workflow replay. A payment saga that has to survive a service crash mid-transaction doesn't need a connector library. The two were built for different jobs.
Where do Airflow and Temporal overlap?
Both platforms have expanded into territory that used to be the domain of the other, and the result is a real but partial overlap across three areas.
- AI and agentic workflows. Airflow added native agent and LLM tasks that run inside the pipeline, model-agnostic and with human-in-the-loop review. Temporal added durable execution support for agents built with frameworks like the OpenAI Agents SDK and Google ADK. Both offer valuable capabilities for AI workloads, but are aimed at different layers: Airflow at agents working with data pipelines, Temporal at durable execution underneath an application-embedded agent.
- Event-driven and API-triggered pipelines. Airflow 3 added event-driven and asset-aware scheduling, along with sub-second, API-triggered dispatch (preview) making it ideal for latency-sensitive, high concurrency data workflows. Temporal remains a stronger fit for processes that stay open indefinitely, waiting on a signal that could arrive at any time.
- Long-running, resumable work. Airflow added task-level state that survives retries, so a task can reconnect to a long-running external job instead of resubmitting it. Temporal's whole-workflow replay is designed for arbitrary application state spanning weeks or months.
Because the overlap is partial, most engineering teams don't pick one platform for every workload. They draw the line based on what a given piece of work actually is. The clearest way to see that line is inside a single business process, where both platforms are often present at once.
Where do Temporal and Airflow fit together?
| Business process | Temporal typically owns | Airflow/Astro typically owns |
|---|---|---|
| Payments | Capturing payment exactly once, refunding if a downstream step fails | Settlement and reconciliation pipelines, fraud model training, chargeback analytics, revenue reporting |
| Order fulfillment | Rolling back multiple services if a step in the fulfillment chain fails | Inventory and demand forecasting, supplier feed ingestion, delivery estimation models, returns analysis |
| Checkout | Cancelling an unpaid order after a timeout | Cart abandonment analysis, pricing models, recommendation data, conversion reporting |
| Approvals | Resuming a workflow when a user approves a request | Document extraction, AI-assisted classification and summarization, audit and SLA reporting |
| Tenant provisioning | Provisioning infrastructure exactly once, without duplicating it | Usage and telemetry pipelines, cost attribution, capacity forecasting, adoption scoring |
| Third-party APIs | Retrying a flaky call inside a single request | Bulk data ingestion, enrichment, quality checks, freshness monitoring across downstream tables |
Across every row, the same pattern applies: Temporal keeps one transaction or one customer's state correct. Airflow runs continuously across all of them, turning that activity into the models, reporting, and AI your business runs on.
When to choose Astro and when to choose Temporal
| Choose Astro when you need | Choose Temporal when you need |
|---|---|
| Data and AI pipeline orchestration | Microservices orchestration and service coordination |
| Batch, scheduled, and event- or API-triggered pipelines | Long-running business workflows measured in days, weeks, or months |
| ML training, inference, and agentic AI grounded in enterprise data | Exactly-once execution semantics for transactional systems |
| Native integration with warehouses, lakes, and data quality tools | Whole-workflow, event-sourced replay of arbitrary application state |
| Data-centric observability, lineage, and data product SLAs | Application-level fault tolerance owned by a software engineering team |
No. They're built for different kinds of work. Airflow orchestrates data pipelines and AI agents that work with data. Temporal orchestrates application workflows and microservices. Most organizations that use both keep them on separate sides of that line rather than replacing one with the other. Yes, and many engineering teams do. A common pattern is Temporal managing application-level state, such as a payment or approval workflow, while Airflow handles the data pipelines around it: ingestion, transformation, analytics, and AI tasks that consume or produce that data. It depends on what the agent works with. Temporal is a strong fit for agents embedded in an application, running as a product feature. Airflow is built for agents that operate on enterprise data — querying warehouses, generating SQL, processing documents — because it puts them in the same orchestrator as the data they need, with the connector ecosystem already in place. Airflow supports task-level durability: a task can persist state across retries so it reconnects to a long-running job instead of restarting it. Temporal's model goes further for application state, using whole-workflow event sourcing to replay arbitrary in-memory state over days or weeks. The two solve durability at different scopes. Airflow supports event-driven and asset-aware scheduling natively, with sub-second, API-triggered execution in active development. Temporal remains the stronger choice for processes that stay open indefinitely, waiting on a signal that could arrive at any time. For most teams, neither. If application workflows are already running well on Temporal, they don't need to move to add data orchestration. If data pipelines are already running on Airflow, they don't need Temporal to add AI agents or event-driven triggers, since Airflow now supports both directly. Migration only makes sense when a workload was placed on the wrong platform for what it actually does.Frequently asked questions
Is Temporal a replacement for Apache Airflow?
Can Airflow and Temporal be used together?
Is Temporal better than Airflow for building AI agents?
Does Airflow support durable execution?
Which is better for event-driven, low-latency pipelines?
Should I migrate from Temporal to Airflow, or the other way around?
