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Databases · head to head

Apache Airflow vs Apache Pulsar

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Apache Pulsar logo

Apache Pulsar

Databases

Cloud-native messaging and streaming with separated storage

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Apache Airflow self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job; Apache Pulsar more components than Kafka: brokers, BookKeeper and ZooKeeper each need operating
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Apache Pulsar covers Separated storage.
  • Prices and features above were last checked on 29 August 2026.

Where they differ

Only the attributes on which Apache Airflow and Apache Pulsar actually diverge.

Attributes where Apache Airflow and Apache Pulsar differ
AttributeApache AirflowApache Pulsar
Pricing modelOpen source, no licence fee; managed services billed separatelyOpen source, no licence fee

Identical on both: starting price (Free), free tier (Yes), platforms (Linux, Docker, Kubernetes, Self-hosted), user rating (Not yet rated), category (Databases).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Apache Airflow

  • Pipelines as Python
  • Web UI
  • Cloud provider packages
  • Jinja templating
  • Retries and dependencies
  • Extensible operators

Only in Apache Pulsar

  • Separated storage
  • Queuing and streaming
  • Built-in multi-tenancy
  • Geo-replication

What people use each for

The jobs each tool is most often brought in to do.

Apache Airflow

  • Scheduling nightly ETL where step order and retries matternot Apache Pulsar
  • Coordinating machine learning training and evaluation runsnot Apache Pulsar
  • Orchestrating dbt runs alongside extraction and loadingnot Apache Pulsar
  • Replacing a sprawl of cron jobs with dependencies and visible run historynot Apache Pulsar

Apache Pulsar

  • Platforms needing both work queues and replayable streams without running two systemsnot Apache Airflow
  • Multi-tenant messaging where isolation between teams is a requirementnot Apache Airflow
  • Deployments where storage and traffic grow at genuinely different ratesnot Apache Airflow

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Apache Airflow

  • Self-hosting is genuinely heavy: scheduler, metadata database, workers and executor choice are a standing operational job
  • Built for scheduled batch work, and a poor fit for event-driven or sub-minute latency pipelines
  • Because DAGs are Python that the scheduler parses continuously, expensive top-level code in a DAG file slows the whole scheduler
  • Local development and testing of DAGs is awkward compared with newer orchestrators designed with it in mind

Apache Pulsar

  • More components than Kafka: brokers, BookKeeper and ZooKeeper each need operating
  • Correspondingly harder to run well, and the expertise is rarer than Kafka expertise
  • A much smaller ecosystem of connectors, tooling and hiring pool than Kafka
  • The architectural advantages only pay off at a scale most deployments never reach

Pricing, plan by plan

Apache Airflow

Free
  • Apache AirflowFree
    • Full scheduler and web UI
    • All provider packages
    • No task or DAG limits

Apache Pulsar

Free
  • Apache PulsarFree
    • Full functionality
    • No usage limits
    • Community support

Which should you pick?

Choose Apache Airflow if

  • You need pipelines as python.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want web ui.
Apache Airflow review

Choose Apache Pulsar if

  • You need separated storage.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want queuing and streaming.
Apache Pulsar review

Questions people ask

Is Apache Airflow or Apache Pulsar better?
Neither clearly leads. Apache Airflow starts at Free and Apache Pulsar at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Apache Pulsar?
Apache Airflow starts at Free and Apache Pulsar at Free.
Does Apache Airflow or Apache Pulsar run on more platforms?
Both run on Linux, Docker, Kubernetes, Self-hosted, so platform support will not decide this one for you.
Can I use Apache Airflow for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Airflow best used for?
Apache Airflow is most often used for scheduling nightly etl where step order and retries matter, coordinating machine learning training and evaluation runs, orchestrating dbt runs alongside extraction and loading, replacing a sprawl of cron jobs with dependencies and visible run history. Of those, scheduling nightly etl where step order and retries matter and coordinating machine learning training and evaluation runs are not what Apache Pulsar is typically brought in for.
What can Apache Airflow do that Apache Pulsar cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Apache Pulsar covers Separated storage, Queuing and streaming, Built-in multi-tenancy, Geo-replication.

Answered from the vendors’ own pages

Apache Airflow: Is Apache Airflow free?

Yes. Airflow is open source under the Apache Software Foundation with no licence fee. Costs are the infrastructure to run it, or a managed service such as Google Cloud Composer or Amazon MWAA.

Apache Pulsar: Is Apache Pulsar free?

Yes, open source under the Apache Software Foundation.

Apache Airflow: What language are Airflow workflows written in?

Python. A workflow is a Python file, so standard language features including loops and datetime handling can generate tasks dynamically, with no XML or command-line configuration.

Apache Pulsar: Pulsar or Kafka?

Pulsar separates storage from compute and covers queuing and streaming in one system. Kafka has a far larger ecosystem and hiring pool. Most teams should have a specific reason before choosing Pulsar.

Apache Airflow: Is Airflow suitable for real-time pipelines?

Not really. Airflow is designed for scheduled batch orchestration. Event-driven or sub-minute work is better served by a streaming platform such as Kafka or a purpose-built streaming engine.

Apache Pulsar: Why does separated storage matter?

Brokers hold no data, so adding or replacing one requires no rebalancing, and storage can grow without adding serving capacity.

Apache Airflow: What are the main alternatives to Airflow?

Dagster and Prefect are the two most commonly weighed against it, both newer and both designed around the local development and testing experience Airflow is criticised for.

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