Quick comparison
| # | App | Pricing | Verified price | Open source | Platforms | Verified facts | Score |
|---|
| 1 |  Apache Airflow | Open source | — | Yes | Linux, Self-hosted, Web | 5 of 5 | 68.8 |
| 2 |  Prefect | Open source | $100/month | Yes | Web, Linux, macOS +2 | 5 of 5 | 64.8 |
| 3 |  Flyte | Open source | — | Yes | Self-hosted | 4 of 4 | 59.4 |
| 4 |  Argo Workflows | Open source | — | Yes | Self-hosted | 4 of 4 | 57.5 |
1Best for: Data engineering teams that want a proven, free orchestrator with a huge integration ecosystem
Open sourceSelf-hostableLinux, Self-hosted, Web
Why switch from Dagster
The most widely deployed open-source orchestrator, with the largest pool of operators, providers and experienced engineers.
Pros
- Free and open source with a very large community
- Pipelines are plain Python, so they can be generated dynamically
- Hundreds of ready-made operators for clouds and data tools
- Managed versions available from several cloud providers
Cons
- Running scheduler, workers and metadata database yourself takes real ops effort
- Local development and testing are clunkier than in newer tools like Prefect or Dagster
- No official hosted service from the Apache project itself
How this score is calculated
Editorial fit ×0.3100
Reviews ×0.363
Clicks (90 days) ×0.150
Freshness ×0.15100
Votes ×0.150
0 counted reviews, 0 up and 0 down votes. Full formula
2Best for: Python teams who want orchestration without rewriting code into a separate DAG framework
Open sourceSelf-hostableWeb, Linux, macOS, Windows, Self-hosted
Why switch from Dagster
Plain Python flows without an asset model, lighter to adopt when you just want to schedule and retry scripts.
Pros
- Plain Python with a decorator, little new to learn
- Free hosted Hobby tier and a self-hostable open source server
- Runs on your own compute, Kubernetes or serverless
Cons
- Free cloud tier is limited to 2 users and 5 deployments
- Smaller integration ecosystem than Airflow
- Paid plans jump quickly from a flat fee to per-user pricing
How this score is calculated
Editorial fit ×0.387
Reviews ×0.363
Clicks (90 days) ×0.150
Freshness ×0.15100
Votes ×0.150
0 counted reviews, 0 up and 0 down votes. Full formula
3Best for: ML and data teams on Kubernetes who want typed, reproducible Python pipelines.
Open sourceSelf-hostableSelf-hosted
Why switch from Dagster
Kubernetes-native, strongly typed workflows with caching and versioning, popular for ML pipelines at scale.
Pros
- Free and Apache 2.0 licensed
- Typed task inputs and outputs catch errors early
- Caching and retries resume from the failed step
- Managed option through Union.ai
Cons
- Needs a Kubernetes cluster for production use
- Flyte 2 changes the SDK, so Flyte 1 code needs migrating
- Smaller community than Airflow
How this score is calculated
Editorial fit ×0.369
Reviews ×0.363
Clicks (90 days) ×0.150
Freshness ×0.15100
Votes ×0.150
0 counted reviews, 0 up and 0 down votes. Full formula
4Best for: Platform teams already on Kubernetes who want container-native batch and ML pipelines.
Open sourceSelf-hostableSelf-hosted
Why switch from Dagster
Container-native workflows defined as Kubernetes resources, a good fit for teams that already run everything on Kubernetes.
Pros
- Free, Apache 2.0 licensed CNCF project
- Any language works, since each step is a container
- Handles large parallel jobs on Kubernetes
- Python users can define workflows with Hera
Cons
- Requires a Kubernetes cluster
- YAML definitions get verbose for data engineers used to Python
- No hosted version from the project itself
How this score is calculated
Editorial fit ×0.363
Reviews ×0.363
Clicks (90 days) ×0.150
Freshness ×0.15100
Votes ×0.150
0 counted reviews, 0 up and 0 down votes. Full formula