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Databases · head to head

Apache Airflow vs Teable

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Teable logo

Teable

Spreadsheets

Spreadsheet interface over real PostgreSQL tables, so the data stays queryable by anything that speaks SQL

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; Teable the automation builder is far less capable than the established commercial alternatives, so multi step workflows usually end up in an external tool that must be paid for and maintained separately.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Teable covers PostgreSQL native storage.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Airflow and Teable differ
AttributeApache AirflowTeable
Pricing modelOpen source, no licence fee; managed services billed separatelyOpen source, no licence fee
PlatformsLinux, Docker, Kubernetes, Self-hostedWeb, Linux
CategoryDatabasesSpreadsheets

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 Teable

  • PostgreSQL native storage
  • Multiple views
  • Linked records and rollups
  • Generated REST API
  • Real time collaboration
  • Self hosting via Docker
  • Field level permissions

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

Teable

  • A team that has hit the record ceiling of a hosted spreadsheet database and does not want to move to raw SQLnot Apache Airflow
  • Operational data that a business intelligence tool must also read directly, without an export or a sync jobnot Apache Airflow
  • A regulated or data resident organisation that needs the underlying database inside its own infrastructurenot Apache Airflow
  • An internal tool where a grid interface and a REST API over the same table are both requirednot 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

Teable

  • The automation builder is far less capable than the established commercial alternatives, so multi step workflows usually end up in an external tool that must be paid for and maintained separately.
  • Self hosting moves PostgreSQL backups, version upgrades, connection pooling and capacity planning onto your team, and the licence saving disappears if that time is costed honestly.
  • The integration catalogue is small, so connecting to a common business system often means writing against the REST API rather than installing a connector.
  • The project is young relative to the products it replaces, and interface and API changes still arrive at a pace that requires reading release notes before upgrading.
  • Storing every table as a real PostgreSQL table means schema changes are real migrations, so a careless field type change on a large table can lock it far longer than a spreadsheet user would expect.

Pricing, plan by plan

Apache Airflow

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

Teable

Free
  • Self hosted open sourceFree
    • No licence fee
    • Unlimited rows subject to your PostgreSQL capacity
    • Docker deployment

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 Teable if

  • You need postgresql native storage.
  • You want to start without paying.
  • You work on Web, Linux.
  • You also want multiple views.
Teable review

Questions people ask

Is Apache Airflow or Teable better?
Neither clearly leads. Apache Airflow starts at Free and Teable at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Teable?
Apache Airflow starts at Free and Teable at Free.
Does Apache Airflow or Teable run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Teable runs on Web, Linux.
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 Teable is typically brought in for.
What can Apache Airflow do that Teable cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Teable covers PostgreSQL native storage, Multiple views, Linked records and rollups, Generated REST API.

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.

Teable: How many rows can it actually hold?

As many as your PostgreSQL instance can serve. There is no product imposed record limit on the self hosted edition, which is the main reason to choose it.

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.

Teable: Can I query the data with SQL directly?

Yes. Tables are real PostgreSQL tables, so any SQL client or reporting tool can read them.

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.

Teable: Is self hosting genuinely free?

The licence is. The database server, backups and the engineer maintaining them are not.

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.

Teable: Does it replace Airtable feature for feature?

No. Views and field types are close; automations, apps and the integration catalogue are not.

Teable: What happens to my data if the project stops?

It remains in a standard PostgreSQL database that you control, which is a materially better exit than a proprietary export format.

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