Databases · head to head
Apache Airflow vs Apache Flink
Apache Airflow
Databases
Programmatically author, schedule and monitor data workflows in Python
- From
- Free
- Rated
- -
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 Flink genuinely difficult: event time, watermarks and state backends are a real conceptual load before anything works
- They diverge on capability: Apache Airflow covers Pipelines as Python, Apache Flink covers Event-time processing.
Where they differ
Only the attributes on which Apache Airflow and Apache Flink actually diverge.
| Attribute | Apache Airflow | Apache Flink |
|---|---|---|
| Platforms | Linux, Docker, Kubernetes, Self-hosted | Linux, Kubernetes, Docker, Self-hosted |
Identical on both: starting price (Free), pricing model (Open source, no licence fee; managed services billed separately), 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 Apache Flink
- Event-time processing
- Exactly-once state
- Batch and stream
- SQL interface
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 Flink
- Coordinating machine learning training and evaluation runsnot Apache Flink
- Orchestrating dbt runs alongside extraction and loadingnot Apache Flink
- Replacing a sprawl of cron jobs with dependencies and visible run historynot Apache Flink
Apache Flink
- Real-time aggregations and dashboards computed over an event streamnot Apache Airflow
- Fraud and anomaly detection where patterns span a time windownot Apache Airflow
- Joining two live streams where events arrive out of ordernot 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 Flink
- Genuinely difficult: event time, watermarks and state backends are a real conceptual load before anything works
- Operationally heavy — job managers, task managers, checkpoint storage and state size are all yours to run and tune
- State grows with the workload, and large state changes recovery time and cost significantly
- Overkill where a scheduled batch job would answer the same question
Pricing, plan by plan
Apache Airflow
Free- Apache AirflowFree
- Full scheduler and web UI
- All provider packages
- No task or DAG limits
Apache Flink
Free- Apache FlinkFree
- Full functionality
- Self-hosted
- No usage limits
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.
Choose Apache Flink if
- You need event-time processing.
- You want to start without paying.
- You work on Linux, Kubernetes, Docker, Self-hosted.
- You also want exactly-once state.
Questions people ask
- Is Apache Airflow or Apache Flink better?
- Neither clearly leads. Apache Airflow starts at Free and Apache Flink at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Airflow or Apache Flink?
- Apache Airflow starts at Free and Apache Flink at Free.
- Does Apache Airflow or Apache Flink run on more platforms?
- Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Apache Flink runs on Linux, Kubernetes, Docker, Self-hosted.
- 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 Flink is typically brought in for.
- What can Apache Airflow do that Apache Flink cannot?
- Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Apache Flink covers Event-time processing, Exactly-once state, Batch and stream, SQL interface.
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 Flink: Is Apache Flink free?
Yes, open source under the Apache Software Foundation. Managed services such as Amazon Managed Service for Apache Flink are billed separately.
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 Flink: Flink or Kafka?
They are complementary rather than alternatives. Kafka moves and stores events; Flink computes over them with windowing, joins and durable state.
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 Flink: What is event-time processing?
Computing based on when an event actually occurred rather than when it arrived. It is what makes results correct when data is late or out of order, and it is the main reason Flink is harder than it looks.
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.
Related pages
More on Apache Airflow
More on Apache Flink
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