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Databases · head to head

Apache Airflow vs FaunaDB

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
FaunaDB logo

FaunaDB

Databases

Document-relational database whose hosted service closed in 2025 and whose core is now unmaintained Apache 2.0 code.

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; FaunaDB the hosted service was wound down in 2025, so there is no managed Fauna to buy; every remaining user either operates a JVM cluster themselves or migrates, and both are projects rather than tasks.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, FaunaDB covers Document-relational model.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Airflow and FaunaDB differ
AttributeApache AirflowFaunaDB
Pricing modelOpen source, no licence fee; managed services billed separatelyfreemium
PlatformsLinux, Docker, Kubernetes, Self-hostedWeb
FoundedUnknown2012

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 FaunaDB

  • Document-relational model
  • FQL v10
  • Distributed ACID transactions
  • HTTPS access
  • User-defined functions
  • Attribute-based access control
  • Document history
  • Event streaming

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

FaunaDB

  • Keeping an existing Fauna-backed application alive on self-hosted infrastructure while a migration is planned and fundednot Apache Airflow
  • Extracting historical data from a Fauna dataset that can no longer be reached through the hosted APInot Apache Airflow
  • Studying a production implementation of deterministic distributed transactions, since the full server source is now readable under Apache 2.0not Apache Airflow
  • Forking the engine deliberately, where an organisation has JVM and distributed-systems staff and wants a document-relational store it fully controlsnot 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

FaunaDB

  • The hosted service was wound down in 2025, so there is no managed Fauna to buy; every remaining user either operates a JVM cluster themselves or migrates, and both are projects rather than tasks.
  • The open-sourced repository has had no substantive activity since May 2025 and the drivers were frozen alongside it, so you inherit responsibility for security patches in a Scala distributed database that almost nobody else is running.
  • FQL has no wire or dialect compatibility with anything else, so migrating off is a rewrite of every query, index and access rule in the application rather than a data export.
  • No BI tool, ORM or CDC connector speaks FQL, so reporting and analytics always required exporting the data first, and that export tooling is now also unmaintained.
  • The community was small before the shutdown and has dispersed since, so operational answers, tuning advice and people who have run a Fauna cluster in anger are all scarce when something breaks.

Pricing, plan by plan

Apache Airflow

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

FaunaDB

Free
  • FreeFree
    • 100K read ops
    • 50K write ops
    • 1GB storage
  • Pro$25/month
    • Pay per use
    • Priority support
    • Advanced features

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 FaunaDB if

  • You need document-relational model.
  • You want to start without paying.
  • You also want fql v10.
FaunaDB review

Questions people ask

Is Apache Airflow or FaunaDB better?
Neither clearly leads. Apache Airflow starts at Free and FaunaDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or FaunaDB?
Apache Airflow starts at Free and FaunaDB at Free.
Does Apache Airflow or FaunaDB run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. FaunaDB runs on 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 FaunaDB is typically brought in for.
What can Apache Airflow do that FaunaDB cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. FaunaDB covers Document-relational model, FQL v10, Distributed ACID transactions, HTTPS access.

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.

FaunaDB: Can I still sign up for Fauna as a service?

No. Fauna Inc. wound down the hosted service in 2025 and the company website is no longer serving. The only way to run Fauna now is to build and operate the open-sourced server yourself.

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.

FaunaDB: What licence is the open-sourced code under?

Apache 2.0, with the copyright held by a FaunaDB Foundation. That is a permissive OSI licence with no competing-use clause, so you may run it, modify it and even offer it as a service.

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.

FaunaDB: Is the open source version the same software that ran the cloud?

It is the core database engine. The control plane, billing, dashboard and multi-tenant operational tooling that made it a service are not part of the release, so you are running the engine, not the product.

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.

FaunaDB: What should I migrate to?

There is no drop-in target. Teams that valued the document model with relationships usually land on Postgres with JSONB, and teams that valued the serverless HTTP access pattern usually land on DynamoDB or a managed Postgres with an HTTP driver. Either way the query layer is rewritten.

FaunaDB: How hard is it to self-host?

It builds as a fat JAR and runs as a multi-node JVM cluster. There is an OPERATING.md, but no supported packaging, no operator, no upstream releases and no support contract, so budget for a distributed-systems engineer, not a container.

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