Temporal vs Airflow: Which Should You Self-Host?

Quick Verdict

These tools solve different problems. Airflow orchestrates scheduled batch data pipelines — ETL jobs, ML training, report generation. Temporal provides durable execution for long-running, stateful application workflows — payment processing, order fulfillment, user onboarding flows. If you’re scheduling data pipelines, use Airflow. If you’re building reliable distributed application logic, use Temporal. They can even coexist — Airflow for batch, Temporal for real-time.

Overview

Apache Airflow is the industry standard for scheduling and monitoring data pipelines. You define DAGs (Directed Acyclic Graphs) in Python, and Airflow’s scheduler executes tasks on a configurable interval, managing dependencies between them. It’s batch-oriented — “run this set of tasks on this schedule.”

Temporal is a durable execution platform. You write workflow logic in Go, Java, Python, TypeScript, or .NET, and Temporal guarantees that code runs to completion even through process crashes, server failures, and infrastructure restarts. It’s event-driven — “run this code reliably whenever it’s triggered, and survive any failure along the way.”

Feature Comparison

Feature	Temporal	Apache Airflow
Primary model	Durable execution — code that survives failures	DAG scheduling — batch tasks on a schedule
Workflow definition	Code (Go, Java, Python, TypeScript, .NET)	Python DAGs
Execution model	Event-driven, real-time	Batch-scheduled
Scheduling	Timer-based, cron, signal-driven	Cron, dataset triggers, timetables
Durability	Built-in — workflow state persisted through any failure	Retry policies per task, but no cross-failure state persistence
Long-running workflows	Native — can run for months or years	Not designed for it — DAG runs are expected to complete
Human-in-the-loop	Native signals and queries for external interaction	Sensors (poll for external conditions), but clunky
Task dependencies	Implicit via code flow (sequential, parallel, conditional)	Explicit DAG graph definition
Backfill	Re-run specific workflow IDs	Native date-range backfill
Versioning	Deterministic replay with workflow versioning	DAG versioning via source control
Web UI	Workflow search, execution history, task details (read-only)	DAG graph, tree, Gantt views, task logs, connections
Provider ecosystem	SDKs only — no pre-built connectors	70+ provider packages
Minimum containers	4+ (server, database, worker, UI)	5+ (API server, scheduler, DAG processor, worker, database, Redis)
Minimum RAM	4+ GB	4+ GB
License	MIT	Apache 2.0

Installation Complexity

Both require multi-container deployments. Neither is trivial to self-host.

Temporal needs a server (with database), a web UI, and at least one worker process that you build and deploy yourself. The temporalio/auto-setup image handles database schema setup automatically for development.

# Temporal development setup
services:
  temporal:
    image: temporalio/auto-setup:1.29.3
    ports:
      - "7233:7233"
    depends_on:
      - postgresql
    environment:
      - DB=postgresql
      - DB_PORT=5432
      - POSTGRES_USER=temporal
      - POSTGRES_PWD=temporal
      - POSTGRES_SEEDS=postgresql
      - DYNAMIC_CONFIG_FILE_PATH=config/dynamicconfig/development-sql.yaml
    restart: unless-stopped

  temporal-ui:
    image: temporalio/ui:2.36.2
    ports:
      - "8080:8080"
    environment:
      - TEMPORAL_ADDRESS=temporal:7233
      - TEMPORAL_CORS_ORIGINS=http://localhost:3000
    depends_on:
      - temporal
    restart: unless-stopped

  postgresql:
    image: postgres:16-alpine
    environment:
      POSTGRES_USER: temporal
      POSTGRES_PASSWORD: temporal
    volumes:
      - temporal_db:/var/lib/postgresql/data
    restart: unless-stopped

volumes:
  temporal_db:

Airflow has an even more complex architecture. The official Docker Compose defines 5+ services (API server, scheduler, DAG processor, Celery worker, triggerer) plus PostgreSQL and Redis. You must also manage a DAGs directory, set up the admin user via an init container, and configure the executor.

Both tools have similar self-hosting complexity, but Temporal requires an additional step: you must build and deploy your own worker processes that contain your workflow code. With Airflow, the worker is generic — it runs whatever Python is in the DAGs folder.

Performance and Resource Usage

Metric	Temporal	Apache Airflow
Idle RAM	2-4 GB (server + DB + UI)	4-6 GB (all services)
CPU idle	Low	Medium (scheduler DAG parsing loop)
Throughput	10,000+ workflow starts/second	Hundreds of DAG runs per minute
Latency	Sub-second task dispatch	Seconds to minutes (scheduler parse interval)
State persistence	Full workflow state replayed from event history	Task state (success/fail/retry), not workflow state

Temporal has significantly higher throughput for individual workflow operations. Airflow’s scheduler introduces latency because it periodically parses DAG files and schedules task instances. For batch data pipelines, this latency is acceptable. For real-time application workflows, it’s not.

Community and Support

Aspect	Temporal	Apache Airflow
GitHub stars	13,000+	39,000+
License	MIT	Apache 2.0
Backing	Temporal Technologies (VC-funded)	Apache Software Foundation + Astronomer
Community	Slack community, enterprise-focused	Massive — Slack, mailing lists, Airflow Summit
Job market	Growing (distributed systems teams)	Industry standard for data engineering
Managed offerings	Temporal Cloud	Astronomer, AWS MWAA, GCP Cloud Composer
Documentation	Excellent SDK docs	Excellent ecosystem docs

Use Cases

Choose Temporal If…

• You’re building application workflows that must complete reliably (payments, orders, subscriptions)
• You need sub-second latency for workflow task dispatch
• You need workflows that run for hours, days, or months (with sleep/timer primitives)
• You’re implementing saga patterns with compensation logic for distributed transactions
• You need human-in-the-loop workflows (signals, queries, updates)
• Your team writes Go, Java, Python, or TypeScript and wants workflow-as-code

Choose Airflow If…

• You’re orchestrating scheduled data pipelines (ETL, ELT, ML training)
• You need date-range backfill for historical data reprocessing
• You rely on provider packages for Spark, dbt, Snowflake, BigQuery, etc.
• Your team are Python data engineers who know the DAG paradigm
• You need a proven, 10-year-old platform with massive community support
• You want multiple managed hosting options as an escape hatch

Final Verdict

Don’t choose between them — understand which problem you’re solving.

If you’re scheduling batch data pipelines on a cron — extracting data from APIs, transforming it, loading it into a data warehouse — Airflow is the established, well-supported choice. Its provider ecosystem for data infrastructure is unmatched.

If you’re building application logic that must execute reliably across failures — processing payments, managing long-running subscriptions, orchestrating microservice interactions — Temporal is purpose-built for this. Its durability guarantees and sub-second latency make it unsuitable for batch scheduling but ideal for application workflows.

For self-hosting specifically, both demand significant resources (4+ GB RAM each). If you only need one: Airflow covers the more common self-hosting use case (scheduled automation of data tasks). Temporal is the right tool when you’re building distributed applications and need an execution engine, not a scheduler.

FAQ

Can I use both Temporal and Airflow together?

Yes, and it’s a common pattern. Airflow handles scheduled batch orchestration (ETL pipelines running hourly/daily). Temporal handles real-time application workflows triggered by user actions or events. They serve complementary roles.

Is Temporal harder to learn than Airflow?

Different kind of hard. Airflow requires learning its DAG model, operators, hooks, sensors, and executor configuration. Temporal requires understanding durable execution, activities vs workflows, deterministic replay, and distributed systems concepts. If you already write backend code in Go/Python/Java, Temporal’s programming model may feel more natural than Airflow’s DAG boilerplate.

Which is more popular for self-hosting?

Airflow, by a significant margin. It’s been around since 2015, is used by thousands of companies, and has well-documented self-hosting guides. Temporal is growing rapidly but still more commonly deployed via Temporal Cloud than self-hosted.

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