Matillion and Apache Airflow are often compared, but they do different jobs. Matillion, now branded Maia, is a low-code platform that builds pipelines and runs transformations inside your warehouse, billed in credits per task hour. Airflow is a free, open-source orchestrator that schedules and chains Python tasks you write yourself.
Matillion vs Airflow at a glance
| Matillion (Maia) | Apache Airflow |
|---|
| Category | Low-code ELT and in-warehouse transformation platform | Open-source workflow orchestration framework |
| Core job | Build, transform and schedule pipelines on a visual canvas | Schedule, chain and monitor tasks written in Python |
| Connectors | 150+ pre-built, plus no-code custom connectors | Provider operators and hooks; extract logic, pagination and schema handling are yours to write |
| Transformation | Visual canvas, SQL and Python, pushed down to the warehouse; Maia AI agents | None built in; runs whatever you call (SQL, dbt, Spark, Python) |
| Who builds pipelines | Analysts and engineers in a low-code UI | Python engineers |
| Destinations | Snowflake, Databricks, Amazon Redshift, Google BigQuery | Anything your code or providers can reach |
| Pricing model | Credits per task hour: 1 for orchestration, 3 for transformation, 1–2 for CDC streams | Free (Apache 2.0); you pay for infrastructure and engineering time, or a managed provider |
| Warehouse compute | Not included; every transformation runs on your warehouse | Not included; depends on what your tasks run |
| Deployment | Full SaaS, or hybrid runner in your cloud on the Scale plan | Self-managed, or managed via Astronomer, Amazon MWAA or Google Cloud Composer |
| Current version | Maia Foundation (Matillion ETL being retired) | Airflow 3.x (Airflow 2 reached end of life in April 2026) |
| Open source | No | Yes |
About Matillion
Matillion is a cloud data integration platform founded in 2011 that runs transformations as SQL inside your own warehouse. Its Data Productivity Cloud has been renamed Maia, made up of Maia Foundation (the pipeline execution layer), Maia Team (AI agents that build and fix pipelines) and a Context Engine. The older Matillion ETL product is being retired, with version 1.78 LTS reaching end of support in November 2026.
About Apache Airflow
Apache Airflow is an open-source workflow orchestrator, originally built at Airbnb and now an Apache Software Foundation project. You define workflows as Python DAGs, and Airflow schedules, runs and monitors each task. Airflow 3 is the current major line; Airflow 2 reached end of life on April 22, 2026.
Different jobs, often compared
Matillion and Airflow overlap only at scheduling. Matillion is a pipeline builder: it connects to sources, transforms data in your warehouse, and runs those pipelines on a schedule. Airflow is an orchestrator: it decides when tasks run and in what order, but every task is code you write or a tool you call.
Teams usually compare them when deciding between two approaches: buy a low-code platform that does the work, or build pipelines in Python and orchestrate them with Airflow. Many end up with both, using Airflow to trigger Matillion jobs through its API alongside dbt and other tasks.
Pricing: credits per task hour vs. free software and your time
Matillion bills in credits, consumed per task hour in two-second increments. An orchestration pipeline uses 1 credit per task hour, a transformation pipeline 3, and a CDC stream 1 or 2. Extra developer users cost 70 credits each per month. Matillion does not publish a price per credit; third-party listings cite about $2.00. Transformations also run on your warehouse, so that compute is a second bill.
Airflow is free under the Apache 2.0 license. The cost is in running it: schedulers, workers and a metadata database, plus upgrades such as the move from Airflow 2 to 3. Managed services like Astronomer, Amazon MWAA and Google Cloud Composer take on that work for a fee.
In short, Matillion's cost scales with how much transformation and streaming you run. Airflow's scales with infrastructure and the engineering hours your team spends building and maintaining DAGs.
Data movement: the gap in both
Neither tool is built around a large library of maintained ingestion connectors.
Matillion offers 150+ pre-built connectors and a no-code builder for REST APIs, but it only loads to Snowflake, Databricks, Redshift and BigQuery.
Airflow's provider packages give you operators and hooks, but a hook is a client, not a pipeline. For every source, your team still writes and maintains:
- Authentication, pagination and rate-limit handling
- Incremental logic, cursors and state
- Schema change detection and type mapping
- Retries, backfills and deduplication
Multiply that by 20 or 50 sources and you've built a private connector library that breaks every time a vendor changes its API.
Matillion is built around transformation. Teams design pipelines on a visual canvas, drop into SQL or Python where needed, and push the work down to the warehouse. Maia agents can now plan, build and troubleshoot pipelines from natural-language prompts.
Airflow doesn't transform data itself. It runs whatever you point it at, most often dbt models, SQL scripts or Spark jobs. That keeps your transformation layer open, but someone has to write and test it.
Deployment and who runs it
Matillion runs as full SaaS, or with a hybrid runner in your own AWS, Azure, Google Cloud or Snowflake account on the Scale plan. Matillion hosts the control plane either way.
Airflow runs anywhere you can run Python: on Kubernetes, on VMs or on a laptop. You can self-host it fully on-prem, or hand operations to a managed provider. Production Airflow needs people who can run and upgrade it.
Where each one leads
Matillion leads for teams that want a low-code way to build warehouse transformations quickly, with analysts and engineers in the same tool.
Airflow leads for engineering teams that need to orchestrate many tools in one place, with complex dependencies, retries and full control in open-source code.
When to choose Matillion, Airflow or Airbyte
| Your situation | Better fit |
|---|
| Analysts and engineers need to build warehouse transformations in a low-code UI | Matillion |
| You're on Snowflake, Databricks, Redshift or BigQuery and want transformation pushed down | Matillion |
| You orchestrate multi-step workflows across many tools | Airflow |
| You need open-source, self-hosted orchestration with full control in code | Airflow |
| Your Airflow DAGs are mostly custom extract-and-load code | Airbyte, orchestrated by Airflow |
| You need connectors beyond Matillion's 150+ or destinations beyond the big four warehouses | Airbyte |
| You need CDC from production databases without paying for every hour a stream runs | Airbyte |
Where Airbyte fits
Matillion is strongest at transformation and Airflow at orchestration. Neither is built to maintain hundreds of ingestion connectors, which is the job Airbyte does.
- 700+ maintained connectors. Authentication, pagination, incremental syncs, schema changes and CDC are built in. The Connector Builder generates new connectors from API docs with AI assist.
- Native Airflow integration. The official Airbyte provider lets a DAG trigger a sync and wait for it to finish before dbt or other tasks run. Airbyte also works with Dagster and Prefect.
- Open source, like Airflow. Airbyte Core is free to self-host. Cloud plans start at $20/month, and Pro and Enterprise Flex price on Data Workers, so volume growth doesn't raise the bill.
- Any destination. Warehouses, lakehouses, databases and AI context stores, not just four warehouses.
A common modern stack is Airbyte for data movement, dbt or Matillion for transformation, and Airflow to orchestrate both.
Related comparisons: Airbyte vs Airflow, Airbyte vs Matillion, Fivetran vs Matillion, Talend vs Airflow.
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