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Databases · head to head

Apache Airflow vs Orkes

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Orkes logo

Orkes

Automation Integration

Commercial Conductor, now the maintainer of the workflow engine Netflix archived

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; Orkes netflix archived the Conductor repository in December 2023 and Orkes took over maintenance, so the large-company backing that justified many original adoptions no longer exists.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Orkes covers JSON workflow definitions.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Airflow and Orkes differ
AttributeApache AirflowOrkes
Pricing modelOpen source, no licence fee; managed services billed separatelyquote
PlatformsLinux, Docker, Kubernetes, Self-hostedLinux, Docker, Kubernetes, Cloud
CategoryDatabasesAutomation Integration

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

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 Orkes

  • JSON workflow definitions
  • Polyglot workers
  • Visual workflow builder
  • AI and LLM tasks
  • Human tasks
  • Event handlers
  • RBAC and secrets
  • Hosted or self-hosted

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

Orkes

  • A microservices estate that needs orchestration across services written in different languages without a shared SDKnot Apache Airflow
  • A team already running Conductor OSS that wants a supported distribution now that Netflix has stopped maintaining itnot Apache Airflow
  • An AI platform composing agent steps, model calls and vector lookups as versioned workflow definitionsnot Apache Airflow
  • A business process needing human approval steps interleaved with automated service callsnot 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

Orkes

  • Netflix archived the Conductor repository in December 2023 and Orkes took over maintenance, so the large-company backing that justified many original adoptions no longer exists.
  • Enterprise pricing is entirely unpublished, and the only free option is explicitly a prototyping sandbox whose limits can change at any time, so there is no supported low-cost entry point.
  • Self-hosting Conductor OSS in production means operating the engine plus its persistence and indexing dependencies, typically Postgres or Cassandra with Elasticsearch, which is a substantial platform commitment.
  • JSON workflow definitions are language agnostic but verbose and awkward to test, and teams accustomed to defining workflows in code find them a step backwards in developer experience.
  • Orkes is a small company whose commercial future is tied to a single open source project it also maintains, which concentrates both vendor and project risk in the same place.

Pricing, plan by plan

Apache Airflow

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

Orkes

Free
  • Developer EditionFree
    • All Conductor OSS features plus enterprise capabilities
    • Sandbox for prototyping only
    • Rate limits and resources may change without notice
  • Enterprise$undefined/year
    • Availability SLA up to 99.99%
    • Orkes-hosted or customer-hosted deployment
    • Dedicated technical account manager

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 Orkes if

  • You need json workflow definitions.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Cloud.
  • You also want polyglot workers.
Orkes review

Questions people ask

Is Apache Airflow or Orkes better?
Neither clearly leads. Apache Airflow starts at Free and Orkes at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Orkes?
Apache Airflow starts at Free and Orkes at Free.
Does Apache Airflow or Orkes run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Orkes runs on Linux, Docker, Kubernetes, Cloud.
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 Orkes is typically brought in for.
What can Apache Airflow do that Orkes cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Orkes covers JSON workflow definitions, Polyglot workers, Visual workflow builder, AI and LLM tasks.

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.

Orkes: Does Netflix still maintain Conductor?

No. Netflix archived its repository in December 2023. The open source project continues at conductor-oss under Apache 2.0, maintained by Orkes.

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.

Orkes: Is there a free production tier?

No. The Developer Edition is a sandbox that the vendor states is unsuitable for production. Self-hosting Conductor OSS is the free production route.

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.

Orkes: What does Enterprise cost?

Nothing is published; it is quoted by deployment and support requirements.

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.

Orkes: How does it compare with Temporal?

Conductor uses declarative JSON workflow definitions and external workers; Temporal defines workflows in code. Temporal has the larger community.

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