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Databases · head to head

Apache Airflow vs OpenSearch

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
OpenSearch logo

OpenSearch

Databases

Open-source search and analytics suite forked from Elasticsearch

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; OpenSearch diverged from Elasticsearch since 7.10, so clients, plugins and features no longer map one to one
  • They diverge on capability: Apache Airflow covers Pipelines as Python, OpenSearch covers Full-text search.

Where they differ

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

Attributes where Apache Airflow and OpenSearch differ
AttributeApache AirflowOpenSearch

Identical on both: starting price (Free), pricing model (Open source, no licence fee; managed services billed separately), free tier (Yes), platforms (Linux, Docker, Kubernetes, Self-hosted), 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 OpenSearch

  • Full-text search
  • OpenSearch Dashboards
  • Log analytics
  • Vector search

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

OpenSearch

  • Log and observability storage where an Apache-2.0 licence is a requirementnot Apache Airflow
  • Replacing Elasticsearch after the licence change without changing architecturenot Apache Airflow
  • Search plus analytics on one cluster rather than two systemsnot 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

OpenSearch

  • Diverged from Elasticsearch since 7.10, so clients, plugins and features no longer map one to one
  • Operationally heavy in the way Elasticsearch is: cluster sizing, shard strategy and JVM tuning are ongoing work
  • Smaller ecosystem of third-party tooling than Elasticsearch, which most integrations still target first
  • Overkill for plain application search, where a dedicated search engine is far simpler

Pricing, plan by plan

Apache Airflow

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

OpenSearch

Free
  • OpenSearchFree
    • 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.
Apache Airflow review

Choose OpenSearch if

  • You need full-text search.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want opensearch dashboards.
OpenSearch review

Questions people ask

Is Apache Airflow or OpenSearch better?
Neither clearly leads. Apache Airflow starts at Free and OpenSearch at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or OpenSearch?
Apache Airflow starts at Free and OpenSearch at Free.
Does Apache Airflow or OpenSearch run on more platforms?
Both run on Linux, Docker, Kubernetes, Self-hosted, so platform support will not decide this one for you.
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 OpenSearch is typically brought in for.
What can Apache Airflow do that OpenSearch cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. OpenSearch covers Full-text search, OpenSearch Dashboards, Log analytics, Vector search.

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.

OpenSearch: Is OpenSearch free?

Yes, Apache 2.0 licensed under the Linux Foundation. Amazon OpenSearch Service is a paid managed option.

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.

OpenSearch: Why does OpenSearch exist?

Elastic moved Elasticsearch off the Apache 2.0 licence in 2021. AWS forked the last Apache-licensed version, and the project now sits under the Linux Foundation.

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.

OpenSearch: Is OpenSearch compatible with Elasticsearch?

It was at the 7.10 fork point. Both have developed independently since, so compatibility weakens with every release and should be verified for the features you use.

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.

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