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Databases · head to head

Apache Airflow vs Apache Doris

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Apache Doris logo

Apache Doris

Databases

MPP analytical database with a MySQL wire protocol and sub-second aggregation on wide tables

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; Apache Doris two competing commercial vendors, VeloDB and SelectDB, were founded by overlapping core contributors, which makes the long-term governance and roadmap of the project harder to predict than a single-sponsor project.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Apache Doris covers MySQL wire protocol.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Airflow and Apache Doris differ
AttributeApache AirflowApache Doris
Pricing modelOpen source, no licence fee; managed services billed separatelyOpen source, no licence fee
PlatformsLinux, Docker, Kubernetes, Self-hostedLinux, Docker, Kubernetes

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 Apache Doris

  • MySQL wire protocol
  • Aggregate and unique key models
  • Materialised views
  • Multi-catalogue federation
  • Routine load from Kafka
  • Compute storage separation
  • Inverted indexes
  • Workload groups

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

Apache Doris

  • A team whose MySQL read replica can no longer serve reporting queries and wants an OLAP engine its existing drivers already speaknot Apache Airflow
  • A real-time dashboard backend needing sub-second aggregation over billions of rows with hundreds of concurrent usersnot Apache Airflow
  • An ad or ecommerce platform that needs updates and deletes on analytical tables, which append-only OLAP engines handle badlynot Apache Airflow
  • A data team that wants one SQL endpoint over both internal tables and existing Hive or Iceberg tables in the lakenot 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 Doris

  • Two competing commercial vendors, VeloDB and SelectDB, were founded by overlapping core contributors, which makes the long-term governance and roadmap of the project harder to predict than a single-sponsor project.
  • A large share of design discussion, issue reports and documentation detail originates in Chinese, so teams that do not read it get a thinner picture of known problems and workarounds.
  • Operating a cluster means managing frontend and backend node roles, tablet balancing and compaction tuning, and compaction backlogs under heavy upsert load are a recurring production complaint.
  • The MySQL protocol compatibility is at the wire level, not full MySQL semantics, so queries and functions still need porting and the familiarity can mislead.
  • Managed cloud availability outside China and major clouds is limited compared with ClickHouse or Snowflake, so many Western adopters end up self-hosting whether they wanted to or not.

Pricing, plan by plan

Apache Airflow

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

Apache Doris

Free
  • Apache DorisFree
    • Apache 2.0 licence with no usage restrictions
    • All engine features included
    • Community support via mailing list and Slack

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 Apache Doris if

  • You need mysql wire protocol.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes.
  • You also want aggregate and unique key models.
Apache Doris review

Questions people ask

Is Apache Airflow or Apache Doris better?
Neither clearly leads. Apache Airflow starts at Free and Apache Doris at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Apache Doris?
Apache Airflow starts at Free and Apache Doris at Free.
Does Apache Airflow or Apache Doris run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Apache Doris runs on Linux, Docker, Kubernetes.
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 Doris is typically brought in for.
What can Apache Airflow do that Apache Doris cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Apache Doris covers MySQL wire protocol, Aggregate and unique key models, Materialised views, Multi-catalogue federation.

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 Doris: Is Apache Doris really free?

Yes, it is Apache 2.0 with no usage restrictions. The commercial products are managed services from VeloDB and SelectDB.

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 Doris: Can I use my MySQL tools with it?

Yes, it implements the MySQL wire protocol, so clients and BI connectors attach without a new driver, though SQL semantics differ.

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 Doris: How does it compare with ClickHouse?

Doris handles updates and high concurrency more comfortably; ClickHouse is generally faster on raw single-query scan throughput and has far wider Western support.

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.

Apache Doris: Who maintains it?

The Apache Software Foundation project, with most committers employed by VeloDB or SelectDB.

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