Apache Airflow itself stays the default for most Python teams because the software costs nothing in licence fees and runs anywhere, while Astronomer Astro is the closest managed version of the same tool. Dagster suits teams that think in data assets, and Prefect Cloud suits small teams that want a free Hobby tier. Managed Airflow is metered by the hour, so a Small Amazon MWAA environment at $0.49 per hour runs about $358 a month even when no DAG runs, while Astronomer workers scale to zero when idle and only the deployment fee keeps running. Prefect announced in July 2026 that it is acquiring Dagster Labs, so two of the leading alternatives now sit under one owner, while Dagster+ keeps running as before and the open source project continues.
Airflow won the Python orchestration market because workflows are plain Python code, and every serious alternative answers the same question in a different way. Some keep your DAGs and host them for you, some replace DAGs with assets or YAML flows, and some bill by the hour, by the seat, or by the credit. The right pick depends on where your data lives, how many people run pipelines, and which billing meter you can live with.
On this page
Python airflow alternatives: comparison table
Swipe the table sideways on a phone to see every column.
| # | Name | Best for | Starting price | Free plan / trial | Link |
|---|---|---|---|---|---|
| 1 | Apache Airflow | Python teams that want full control and no licence fee | $0 for open source software | Yes | Visit |
| 2 | Astronomer Astro | Teams that want managed Airflow without running it | $0.35 per hour | Free trial (14 days) | Visit |
| 3 | Dagster | Data teams that plan around assets and lineage | $10 per month | Free trial (30 days) | Visit |
| 4 | Prefect Cloud | Small Python teams that want a free start | $0 for Hobby plan | Yes | Visit |
| 5 | Kestra | Engineers who want YAML flows on any cloud | $0 for open source edition | Yes | Visit |
| 6 | Amazon MWAA | AWS shops that want managed Airflow inside their account | $0.49 per hour | No | Visit |
| 7 | Google Cloud Composer | Google Cloud teams running Airflow next to BigQuery | $0.06 per DCU-hour | No | Visit |
The 7 best python airflow alternatives, in detail
Apache Airflow
Best for: Python teams that want full control and no licence fee
Airflow is the baseline every other tool on this list is measured against, and for many Python teams it is also the right answer. It started at Airbnb in October 2014, joined the Apache Software Foundation incubator in March 2016, and became a top-level Apache project in January 2019. Workflows are defined entirely in Python, which means loops, branching, and parameters are ordinary code rather than configuration, and the provider packages cover Google Cloud, Amazon Web Services, Microsoft Azure, and many other services.
- The current release line is Airflow 3.4.0, announced on 6 November 2026 with task loops, DAGs in any language, and an AI agents provider.
- The web interface lets you view, trigger, and debug runs, while the scheduler, DAG processor, triggerer, and workers split the load across machines.
- The trade-off is operational: the licence is free, but servers, a metadata database, upgrades, and on-call time are not, and a team that cannot staff
Pros
- No licence fee under Apache License 2.0
- DAGs are plain Python code with a large provider library
Cons
- You run the scheduler, web server, and database yourself
- upgrades and scaling are your job
Astronomer Astro
Best for: Teams that want managed Airflow without running it
Astro is the managed Airflow service from Astronomer, the company behind much of the Airflow ecosystem, and it is the closest thing to Airflow without the operations work. A deployment is one Airflow environment, and the Developer plan prices a Small deployment at $0.35 per hour with 1 vCPU and 2 GiB of memory for the scheduler. Medium deployments run $0.57, $0.77, or $1.54 per hour depending on scheduler and DAG processor size, and dedicated single-tenant clusters start at $2.40 per hour on the Team plan.
- Workers bill separately, from $0.13 per hour for an A5 worker with 1 vCPU and 2 GiB of memory up to $4.16 per hour for an A160 worker with 32 vCPU and
- Workers only bill while tasks are running and scale to zero when idle, which is the main cost control Astro offers.
- A Small deployment left up all month costs about $255.50 before any worker time, and the 14-day trial includes $20 in credits.
- Choose Astro when your team already writes DAGs and wants to keep them.
Pros
- Workers scale to zero when idle
- standard multi-tenant clusters are included on all plans
Cons
- Deployment fee runs every hour even when no tasks run
- Astro Observe costs a percentage of compute with no published rate
Dagster
Best for: Data teams that plan around assets and lineage
Dagster changes the unit of orchestration from the task to the data asset. You define software-defined assets, declare what each asset depends on, and Dagster works out execution order, tracks lineage, and shows data quality next to execution status. That asset-first model is the main reason teams switch from Airflow, and it makes stale or missing tables visible in a way a task list does not.
- Dagster+ is the managed version.
- Solo costs $10 per month plus $0.040 per credit and covers 1 user, 1 code location, and 1 deployment, with serverless compute at $0.010 per minute.
- Starter costs $100 per month plus $0.035 per credit and covers up to 3 users, 5 code locations, and 1 deployment, adding catalog search and role-based
- Pro is quoted by sales with unlimited code locations and deployments, uptime SLAs, and a private Slack channel.
Pros
- Open source core is free under Apache 2.0
- Hybrid deployments carry no Dagster compute charge
Cons
- Every asset materialization and op execution costs 1 credit
- SSO, SAML, and audit logs sit in the Pro plan
Prefect Cloud
Best for: Small Python teams that want a free start
Prefect is the most Python-native of the Airflow alternatives, with flows written as decorated Python functions and an open source server you can self-host. Prefect Cloud adds hosting, scheduling, and observability, and its pricing is flat and seat-based rather than metered by runs, which makes the monthly bill easy to predict. The Hobby tier is free forever and includes 2 users, up to 5 deployments, 500 minutes of Prefect Serverless, and 7 days of run retention.
- Starter costs $100 per month for 3 users, up to 20 deployments, 75 hours of Prefect Serverless, webhooks, and a 1,250 API requests per minute limit.
- Team costs $100 per user per month for 4 to 8 users, up to 100 deployments, 225 hours of Prefect Serverless, service accounts, and a 24 hour audit log
- Enterprise is custom priced with SAML and OIDC single sign-on, role-based access control, SCIM directory sync, IP allowlisting, PrivateLink, unlimited
- Pick Prefect when your team is small, your flows are Python, and you want a free tier that stays free.
Pros
- Hobby is free forever with 2 users and 5 deployments
- pricing is by seat, not by run volume
Cons
- Hobby caps at 5 deployments and 7 days of run retention
- SSO and audit logs need Enterprise
Kestra
Best for: Engineers who want YAML flows on any cloud
Kestra is an open source orchestration platform where workflows are declarative YAML stored in Git, and tasks can run SQL, Python, shell, Terraform, dbt, or Spark in the same flow. The open source edition is free forever under Apache 2.0, self-hosted on Docker or Kubernetes, with unlimited flows and executions and more than 2,600 plugins. The limit of the free edition is governance.
- A single basic-auth username and password is the only credential in open source, while API tokens, service accounts, role-based access control, SSO, a
- Kestra Cloud is the fully managed version with a 14-day free trial and no card, billed on task runs and Cloud runner time at rates that are not publis
- Choose Kestra when you want cloud-neutral YAML orchestration and you can live without enterprise access controls, or when you are ready to pay for the
Pros
- Open source edition has unlimited flows and executions with 2,600+ plugins
- runs on Docker or Kubernetes on any cloud
Cons
- RBAC, SSO, audit logs, and secrets manager need Enterprise
- Cloud has no published rate and needs an access request
Amazon MWAA
Best for: AWS shops that want managed Airflow inside their account
Amazon Managed Workflows for Apache Airflow, known as MWAA, runs Airflow inside your AWS account with the scheduler, web server, and metadata database managed for you. A Small environment costs $0.49 per hour in US East (N. Virginia), and a Large environment costs $0.99 per hour, billed per second with no minimum fee.
- Additional small workers cost $0.055 per hour each, and metadata database storage costs $0.10 per GB-month.
- AWS publishes a worked example for a Small environment that spikes to 50 concurrent workers for an hour a day: $364.56 for the environment, $83.55 for
- MWAA Serverless, launched in November 2025, bills per task instead, at $0.080 per hour of AWS Managed Tasks usage with a one-minute minimum, so short
- Pick MWAA when your data and identity already live in AWS and you want Airflow without leaving that account.
Pros
- No minimum fees or upfront commitments
- serverless option bills per task from $0.080 per hour of managed task compute
Cons
- A Small environment left running costs about $358 a month before workers
- extra small workers cost $0.055 per hour each
Google Cloud Composer
Best for: Google Cloud teams running Airflow next to BigQuery
Google Cloud Composer, now called Managed Service for Apache Airflow, is Google’s managed Airflow service, and Gen 3 is the current generation. It bills in Data Compute Units, a single meter that covers the CPU, memory, and storage used by the environment’s schedulers, DAG processors, triggerers, workers, and web server. The standard rate in Iowa (us-central1) is $0.06 per DCU-hour, measured in 1,000 milli DCU-hours, with a 1-year committed-use discount at $0.054 and a 3-year discount at $0.048.
- Google’s own pricing example runs an environment for 10 days at 12 to 15 DCUs, which comes to 3,240 DCU-hours and $194.40 for compute, plus $0.57 for
- Database storage is a separate SKU at $0.000232877 per GiB-hour with a 10 GiB minimum disk, and highly resilient environments roughly double the stora
- Choose Composer when your pipelines feed BigQuery, Dataflow, or Dataproc and you want Airflow that sits inside your Google Cloud project.
Pros
- Gen 3 hides the cluster and bills in DCU-hours
- committed-use discounts cut the rate to $0.054 for 1 year and $0.048 for 3 years
Cons
- Database storage bills separately from $0.000232877 per GiB-hour with a 10 GiB minimum
- baseline components accrue DCUs even with no DAGs running
How we chose
We picked tools that a Python team can realistically use to replace Airflow, either by hosting Airflow itself, by running Airflow as a managed service, or by offering a different orchestration model with a Python or YAML authoring experience. Every price and plan limit in this article comes from an official vendor pricing page or official documentation page opened on 2026-11-12, and each item cites that page in Sources. We did not test any tool hands-on. One detail most roundups skip is the billing meter: Airflow alternatives do not bill the same way, so an hourly deployment, a per-seat plan, and a per-credit plan can produce very different monthly totals for the same workload.
Which alternative fits your team and budget
| Situation | Pick | Entry cost |
|---|---|---|
| You have engineers to run it and want zero licence cost | Apache Airflow | $0 for the software, plus your own infrastructure |
| You already write DAGs and want them hosted | Astronomer Astro | $0.35 per hour for a Small deployment |
| You think in tables and models, not tasks | Dagster | $10 per month Solo, plus $0.040 per credit |
| You are a small Python team and want a free tier | Prefect Cloud | $0 for Hobby, 2 users and 5 deployments |
| You want YAML flows on any cloud | Kestra | $0 for open source, unlimited flows and executions |
| Your data lives in AWS | Amazon MWAA | $0.49 per hour for a Small environment |
| Your data lives in Google Cloud | Google Cloud Composer | $0.06 per DCU-hour for Gen 3 |
Frequently asked questions
What is the best Python Airflow alternative?
Apache Airflow itself is the best choice for most Python teams because the software is free under Apache 2.0 and DAGs are plain Python. If you want Airflow without the operations work, Astronomer Astro is the closest managed version at $0.35 per hour for a Small deployment. Dagster suits asset-first teams, and Prefect Cloud suits small teams that want a free Hobby tier.
How much does a managed Airflow alternative cost?
A Small Amazon MWAA environment costs $0.49 per hour in US East, or about $358 a month before workers, while Astronomer Astro starts at $0.35 per hour for a Small deployment plus $0.13 per hour per A5 worker. Google Cloud Composer Gen 3 costs $0.06 per DCU-hour. Dagster+ Solo starts at $10 per month plus credits, and Prefect Cloud Hobby is free.
Is there a free Airflow alternative?
Yes. Apache Airflow, Kestra open source, and the Dagster open source core are all free under Apache 2.0, though you pay for the servers that run them. Prefect Cloud Hobby is free forever for 2 users and 5 deployments with 500 minutes of Prefect Serverless. Kestra open source adds unlimited flows and executions with no licence fee at all.
What happened to Dagster after the Prefect acquisition?
Prefect announced in July 2026 that it is acquiring Dagster Labs, which puts Dagster and Prefect under one owner. Dagster+ continues to operate as before, with Solo at $10 per month and Starter at $100 per month, and the open source Dagster project continues. Buyers should still compare the two products on their own merits, since their pricing meters differ.
Can I move my Airflow DAGs to another tool?
Yes for the managed Airflow services, since Astronomer Astro, Amazon MWAA, and Google Cloud Composer all run Apache Airflow and accept your existing DAGs. Dagster, Prefect, and Kestra use different models, so moving means rewriting workflows as assets, Python flows, or YAML, which is a real migration cost to budget before you switch.
Sources
- https://airflow.apache.org/
- https://www.astronomer.io
- https://www.astronomer.io/pricing/compare/
- https://dagster.io
- https://dagster.io/pricing
- https://www.prefect.io
- https://www.prefect.io/pricing
- https://kestra.io
- https://kestra.io/pricing
- https://aws.amazon.com/managed-workflows-for-apache-airflow/
- https://aws.amazon.com/managed-workflows-for-apache-airflow/pricing/
- https://cloud.google.com/composer
- https://cloud.google.com/composer/pricing
Prices and features were read on each company’s own website in November 2026. They change often, so confirm the current offer on the official site before you sign up or buy.