Databases · head to head
Apache Airflow vs Ninox

Apache Airflow
Databases
Programmatically author, schedule and monitor data workflows in Python
- From
- Free
- Rated
- -
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; Ninox free plan limited to 5 users and 5,000 records
- Prices and features above were last checked on 29 August 2026.
Where they differ
Only the attributes on which Apache Airflow and Ninox actually diverge.
| Attribute | Apache Airflow | Ninox |
|---|---|---|
| Pricing model | Open source, no licence fee; managed services billed separately | freemium |
| Platforms | Linux, Docker, Kubernetes, Self-hosted | Web |
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 Ninox
Nothing recorded that Apache Airflow does not also cover.
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 Ninox
- Coordinating machine learning training and evaluation runsnot Ninox
- Orchestrating dbt runs alongside extraction and loadingnot Ninox
- Replacing a sprawl of cron jobs with dependencies and visible run historynot Ninox
Ninox
- No-code database solutions for small to medium teamsnot Apache Airflow
- Workflow automation and process managementnot Apache Airflow
- Customer data and project tracking with custom viewsnot 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
Ninox
- Free plan limited to 5 users and 5,000 records
- API call limits restrict automation on lower tiers (1,000/month free, 10,000/month team)
- Only 7-day history on free plan vs. unlimited on Business
Pricing, plan by plan
Apache Airflow
Free- Apache AirflowFree
- Full scheduler and web UI
- All provider packages
- No task or DAG limits
Ninox
Free- FreeFree
- Up to 5 users
- 100 MB storage
- Up to 5,000 records
- Team$25/month
- 5 GB storage per user
- 50,000 records per user
- 10,000 API calls per user per month
- Business$40/month
- 10 GB storage per user
- 250,000 records per user
- 50,000 API calls per user per month
- Enterprise$null/custom
- Custom pricing available
- Dedicated support
- Custom solutions and governance
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.
Questions people ask
- Is Apache Airflow or Ninox better?
- Neither clearly leads. Apache Airflow starts at Free and Ninox at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Apache Airflow or Ninox?
- Apache Airflow starts at Free and Ninox at Free.
- Does Apache Airflow or Ninox run on more platforms?
- Apache Airflow runs on Linux, Docker, Kubernetes, Self-hosted. Ninox runs on Web.
- 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 Ninox is typically brought in for.
- What can Apache Airflow do that Ninox cannot?
- Apache Airflow covers Pipelines as Python, Web UI, Cloud provider packages, Jinja templating.
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.
Ninox: How much does Ninox cost?
Ninox is free for individuals with up to 5 users. Team plan is 25 EUR per user per month (billed annually), and Business plan is 40 EUR per user per month (billed annually). Enterprise plans have custom pricing.
SourceApache 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.
Ninox: Is Ninox free?
Yes, Ninox offers a free plan for up to 5 users with 100 MB storage and 5,000 records. No payment required to start, and includes all core views: table, form, chart, kanban, maps, and calendar.
SourceApache 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.
Ninox: What is Ninox Team plan pricing?
The Team plan costs 25 EUR per user per month (billed annually). It includes 5 GB storage per user, 50,000 records, 10,000 API calls per month per user, and 30-day history. Annual billing is required.
SourceApache 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.
Related pages
More on Apache Airflow
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- Ninox vs PostgreSQL
- Ninox vs RabbitMQ
- Ninox vs NATS
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- Ninox vs MariaDB
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- Ninox vs Aiven
- Ninox vs Memcached
- Ninox vs OpenSearch
- Ninox vs Knack
- Ninox vs LanceDB
- Ninox vs Marqo
- Ninox vs Nile
- Ninox vs Presto
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- Ninox vs Cockroach Labs
- Ninox vs Amazon Aurora
- Ninox vs FaunaDB
- Ninox vs Readyset
- Ninox vs Elasticsearch
- Ninox vs MotherDuck
- Ninox vs Neo4j
- Ninox vs Qdrant
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- Ninox vs Cassandra
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