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Databases · head to head

Apache Airflow vs Nango

Apache Airflow logo

Apache Airflow

Databases

Programmatically author, schedule and monitor data workflows in Python

From
Free
Rated
-
Full reviewWebsite
Nango logo

Nango

Automation Integration

Open source unified API and OAuth infrastructure for product integrations, licensed under Elastic License 2.0

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; Nango the licence is Elastic License 2.0, which is source available rather than OSI open source, and it forbids offering Nango to third parties as a managed service, so anyone planning to resell or embed it in a platform for their own customers has a genuine legal problem.
  • They diverge on capability: Apache Airflow covers Pipelines as Python, Nango covers Managed OAuth.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Apache Airflow and Nango differ
AttributeApache AirflowNango
Pricing modelOpen source, no licence fee; managed services billed separatelyPer connection per month
PlatformsLinux, Docker, Kubernetes, Self-hostedWeb, Linux, Docker
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 Nango

  • Managed OAuth
  • Pre-built integrations
  • Custom syncs and actions
  • Incremental sync
  • Rate limit and retry handling
  • Webhooks
  • Self-hosting
  • Unified models

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

Nango

  • A SaaS product that needs to ship twenty customer-facing integrations without hiring a team to maintain OAuth and token refresh for eachnot Apache Airflow
  • A team that needs a niche or internal API integrated, which closed unified API vendors will not build for themnot Apache Airflow
  • A company with data residency or security constraints that must self-host the integration layer rather than send customer tokens to a vendornot Apache Airflow
  • An engineering team replacing a homegrown integration service whose main cost is silent token expiry and rate limit failures in productionnot 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

Nango

  • The licence is Elastic License 2.0, which is source available rather than OSI open source, and it forbids offering Nango to third parties as a managed service, so anyone planning to resell or embed it in a platform for their own customers has a genuine legal problem.
  • Pricing is per connection where a connection is one authorised end-user account, so cost scales linearly with your customer base and a product where each user links several services multiplies quickly beyond what a headline plan price suggests.
  • Pre-built integrations vary in depth, and a connection that exists is not the same as a connection that covers the endpoints and objects your feature needs, so each one must be verified before it is designed into a roadmap.
  • Custom syncs are written in TypeScript and run in Nango model, which means integration logic lives in a vendor runtime and migrating away later requires rewriting it rather than lifting it out.
  • Self-hosting removes the vendor from the data path but transfers operational responsibility for a component that holds customer OAuth tokens, and few teams appreciate the security burden that comes with running that themselves.

Pricing, plan by plan

Apache Airflow

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

Nango

Free
  • FreeFree
    • 10 connections
    • Pre-built integrations
    • Managed OAuth
  • Starter$50/month
    • 20 connections included
    • 1 USD per additional connection
    • Custom syncs and actions
  • Growth$500/month
    • 100 connections included
    • 1 USD per additional connection
    • Higher limits
  • Enterprise$undefined/month
    • Quoted
    • Custom connection volumes
    • Security review and SLA

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 Nango if

  • You need managed oauth.
  • You want to start without paying.
  • You work on Web, Linux, Docker.
  • You also want pre-built integrations.
Nango review

Questions people ask

Is Apache Airflow or Nango better?
Neither clearly leads. Apache Airflow starts at Free and Nango at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Airflow or Nango?
Apache Airflow starts at Free and Nango at Free.
Does Apache Airflow or Nango run on more platforms?
Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Nango runs on Web, Linux, Docker.
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 Nango is typically brought in for.
What can Apache Airflow do that Nango cannot?
Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating. Nango covers Managed OAuth, Pre-built integrations, Custom syncs and actions, Incremental sync.

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.

Nango: Is Nango open source?

It is source available under Elastic License 2.0. You can read, modify and self-host it, but you cannot offer it to third parties as a managed service. That is not the same as an OSI approved open source licence.

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.

Nango: How is it priced?

Per connection per month, where a connection is one authorised end-user account. Free to 10 connections, 50 dollars a month for Starter with 20, 500 for Growth with 100, and one dollar per additional connection.

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.

Nango: Can we integrate an API Nango does not support?

Yes. Custom syncs and actions in TypeScript cover any API including internal ones, which is the main advantage over closed unified API products.

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.

Nango: Can we self-host it?

Yes, under the Elastic License 2.0 terms, which permit self-hosting for your own use but not resale as a service.

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