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Databases · head to head

Apache Airflow vs Presto

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Presto logo

Presto

Databases

The Meta-lineage distributed SQL query engine, distinct from the Trino fork

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; Presto the original creators and most of the active contributor base left for Trino in 2020, so Presto has the smaller community, fewer connectors and slower feature delivery of the two branches.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Presto covers Federated querying.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Airflow and Presto differ
AttributeApache AirflowPresto
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 Presto

  • Federated querying
  • In-memory execution
  • Open table format support
  • Presto C++ workers
  • ANSI SQL
  • Pluggable connectors

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

Presto

  • An existing PrestoDB estate that needs continued upgrades rather than a migration to Trinonot Apache Airflow
  • A team buying IBM watsonx.data, where Presto is the underlying query enginenot Apache Airflow
  • Joining a Hive or Iceberg lake to an operational PostgreSQL database in one query without an ETL stepnot Apache Airflow
  • Very large scale interactive SQL where the Meta-tested branch is a specific requirementnot 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

Presto

  • The original creators and most of the active contributor base left for Trino in 2020, so Presto has the smaller community, fewer connectors and slower feature delivery of the two branches.
  • Documentation, tutorials and Stack Overflow answers for the two projects are frequently mixed up, and a solution written for Trino often does not apply, which costs real debugging time.
  • It is a query engine with no storage of its own, so query performance is dictated by your file layout, partitioning and statistics, and a badly organised lake makes Presto look slow.
  • Memory-bound execution means a single large join can fail the whole query rather than spilling gracefully, and tuning cluster memory settings is a persistent operational chore.
  • Commercial support has consolidated into IBM since the Ahana acquisition, so the independent vendor market that once existed around Presto is largely gone.

Pricing, plan by plan

Apache Airflow

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

Presto

Free
  • PrestoFree
    • Apache 2.0 licence
    • Presto Foundation governance under the Linux Foundation
    • No node or query 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.
Apache Airflow review

Choose Presto if

  • You need federated querying.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes.
  • You also want in-memory execution.
Presto review

Questions people ask

Is Apache Airflow or Presto better?
Neither clearly leads. Apache Airflow starts at Free and Presto at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Presto?
Apache Airflow starts at Free and Presto at Free.
Does Apache Airflow or Presto run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Presto 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 Presto is typically brought in for.
What can Apache Airflow do that Presto cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Presto covers Federated querying, In-memory execution, Open table format support, Presto C++ workers.

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.

Presto: Is this Presto or Trino?

This is PrestoDB, the branch that stayed at Facebook and moved to the Linux Foundation. Trino is the 2020 fork by the original creators.

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.

Presto: Which should I choose for a new project?

Trino, in most cases. It has the larger community, more connectors and more commercial options.

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.

Presto: Who maintains Presto now?

Principally Meta, Uber and IBM, which acquired the Presto vendor Ahana in 2023.

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.

Presto: Is it still actively released?

Yes, releases continue on a regular cadence under the Presto Foundation.

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