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Databases · head to head

Apache Airflow vs Steampipe

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Steampipe logo

Steampipe

Developer Tools

Query cloud APIs, SaaS tools and code with SQL, with no extract or load step

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; Steampipe aGPL-3.0 across all four engines is a procurement blocker at organisations that ban the licence outright, and the network clause reaches any internal portal or service that puts a web interface in front of it.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Steampipe covers SQL over live APIs.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Airflow and Steampipe differ
AttributeApache AirflowSteampipe
Pricing modelOpen source, no licence fee; managed services billed separatelyOpen source, with paid hosting through Turbot Pipes
PlatformsLinux, Docker, Kubernetes, Self-hostedmacOS, Linux, Windows, Docker, Web
CategoryDatabasesDeveloper Tools

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 Steampipe

  • SQL over live APIs
  • Wide plugin set
  • Embedded Postgres
  • Compliance benchmarks
  • Joins across providers
  • Hosted option

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 Steampipe
  • Coordinating machine learning training and evaluation runsnot Steampipe
  • Orchestrating dbt runs alongside extraction and loadingnot Steampipe
  • Replacing a sprawl of cron jobs with dependencies and visible run historynot Steampipe

Steampipe

  • Security teams answering posture questions against live cloud accounts rather than a nightly exportnot Apache Airflow
  • Compliance evidence gathering where the answer must reflect the account at the moment it is askednot Apache Airflow
  • Inventory and drift questions spanning several cloud providers in one querynot Apache Airflow
  • Engineers who would rather write SQL than learn each provider command line toolnot 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

Steampipe

  • AGPL-3.0 across all four engines is a procurement blocker at organisations that ban the licence outright, and the network clause reaches any internal portal or service that puts a web interface in front of it.
  • Dashboards, benchmarks and mods were removed from Steampipe entirely at version 1.0 in October 2024 and now live in a separate product, so pre-2024 documentation and tutorials describe commands that no longer exist.
  • Live querying is bound by cloud provider API rate limits and keeps no persistent store by default, which is why a separate DuckDB-backed product exists for log volumes and why large accounts return slowly.
  • The company is fifteen people and bootstrapped, maintaining four command line tools plus a hosted service plus two further products, and the newer tools have thin community traction relative to that surface area.
  • Hosted tiers include only three users regardless of tier, with additional Enterprise users charged separately, so a team of thirty costs an order of magnitude more than the headline figure before compute and storage are counted.

Pricing, plan by plan

Apache Airflow

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

Steampipe

Free
  • Steampipe CLIFree
    • AGPL-3.0
    • All plugins
    • No user or query limits
  • Pipes DeveloperFree
    • One user
    • 400 compute minutes
    • 3GB storage
  • Pipes Team$49/month
    • Three users
    • 2,000 compute minutes
    • 20GB storage
  • Pipes Enterprise$249/month
    • Three users
    • 10,000 compute minutes
    • 100GB storage

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 Steampipe if

  • You need sql over live apis.
  • You want to start without paying.
  • You work on macOS, Linux, Windows, Docker, Web.
  • You also want wide plugin set.
Steampipe review

Questions people ask

Is Apache Airflow or Steampipe better?
Neither clearly leads. Apache Airflow starts at Free and Steampipe at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Steampipe?
Apache Airflow starts at Free and Steampipe at Free.
Does Apache Airflow or Steampipe run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Steampipe runs on macOS, Linux, Windows, Docker, Web.
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 Steampipe is typically brought in for.
What can Apache Airflow do that Steampipe cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Steampipe covers SQL over live APIs, Wide plugin set, Embedded Postgres, Compliance benchmarks.

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.

Steampipe: When did Steampipe become AGPL?

May 2021, about four months after the project went public. It is a settled licence rather than a recent change, and predates the Business Source Licence wave it is often confused with.

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.

Steampipe: Where did the dashboards and benchmarks go?

Into Powerpipe. They were deprecated in March 2024 and removed from Steampipe at version 1.0 in October 2024, so the check, dashboard, mod and variable commands are gone.

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.

Steampipe: Is it fast on a large cloud estate?

Not always. Queries call provider APIs at request time, so rate limits rather than query planning set the pace, and there is no persistent store by default.

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.

Steampipe: Does the AGPL affect internal use?

Running it internally for your own analysis is fine. Putting a web interface in front of it that other people use is where the network clause becomes a question for your legal team.

Steampipe: Is a bootstrapped vendor a risk?

It cuts both ways. There is no investor pressure toward a licence change or an exit, and there are also fifteen people supporting a large product surface.

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