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Databases · head to head

Apache Airflow vs dbt

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
dbt logo

dbt

Databases

SQL transformation framework enabling analytics engineers to version, test and deploy models

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; dbt free tier severely limited: 3,000 models/month cap and single project maximum restricts team and production use

Where they differ

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

Attributes where Apache Airflow and dbt differ
AttributeApache Airflowdbt
Pricing modelOpen source, no licence fee; managed services billed separatelysubscription|free
PlatformsLinux, Docker, Kubernetes, Self-hostedCloud, Self-hosted, IDE integration (VS Code, Cursor, Claude Code, Windsurf)

Identical on both: starting price (Free), free tier (Yes), 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 dbt

Nothing recorded that Apache Airflow does not also cover.

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

dbt

  • Data warehouse transformation and ELT pipelinesnot Apache Airflow
  • Analytics engineering for reporting and business intelligencenot Apache Airflow
  • Data quality testing and validation at scalenot Apache Airflow
  • Cross-functional data collaboration with version controlnot Apache Airflow
  • Cost optimisation of warehouse usage through intelligent schedulingnot 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

dbt

  • Free tier severely limited: 3,000 models/month cap and single project maximum restricts team and production use
  • Starter at $1,200/year per seat: minimum 5-seat team costs $6,000/year baseline; no single-seat or 2-seat paid option
  • Enterprise pricing opaque: 'custom pricing' with no budget range for startups vs. enterprises; requires sales consultation
  • Model volume metering unclear: 'successful models/month' as a limit is ambiguous; unclear if this counts transformation runs, test runs, or deployment attempts

Pricing, plan by plan

Apache Airflow

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

dbt

Free
  • Developer$Free/mo
  • Starter$$100/seat/month/mo
  • Enterprise$Custom pricing/mo
  • Enterprise+$Custom pricing/mo

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 dbt if

  • You want to start without paying.
  • You work on Cloud, Self-hosted, IDE integration (VS Code, Cursor, Claude Code, Windsurf).
dbt review

Questions people ask

Is Apache Airflow or dbt better?
Neither clearly leads. Apache Airflow starts at Free and dbt at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or dbt?
Apache Airflow starts at Free and dbt at Free.
Does Apache Airflow or dbt run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. dbt runs on Cloud, Self-hosted, IDE integration (VS Code, Cursor, Claude Code, Windsurf).
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 dbt is typically brought in for.
What can Apache Airflow do that dbt cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating.

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.

dbt: How much does dbt cost?

dbt Developer is free. dbt Starter is $100/seat/month with a 5-seat minimum (or custom annual billing). Enterprise and Enterprise+ have custom pricing and require contacting sales.

Source
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.

dbt: What is included in the free Developer plan?

The Developer plan is free and includes 1 developer seat, 3,000 successful models/month limit, 1 project, browser-based IDE, MFA, and job scheduling.

Source
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.

dbt: What is the model limit on each plan?

Developer tier allows 3,000 successful models/month. Starter allows 15,000/month. Enterprise and Enterprise+ allow 100,000/month.

Source
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.

dbt: Can I upgrade or downgrade my dbt plan?

Yes. dbt allows you to 'upgrade or downgrade at any time.' Starter plans bill monthly by credit card based on seat count; Enterprise plans are annual invoicing.

Source
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