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 | 61.8 |
| 4 |  Tekton | Open source | — | Yes | Self-hosted | 4 of 4 | 52.6 |
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 Argo Workflows
Python-defined DAGs with a huge operator library, and it doesn't require Kubernetes.
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 Argo Workflows
Write flows as plain Python functions and run them on any infrastructure, with a hosted UI.
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 Argo Workflows
Also Kubernetes-based, but with typed Python tasks, caching and ML-focused integrations.
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 ×0.377
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 building their own CI/CD system on Kubernetes
Open sourceSelf-hostableSelf-hosted
Why switch from Argo Workflows
Another Kubernetes-native engine where each step runs in a pod, focused on CI/CD pipelines.
Pros
- Kubernetes-native resources and tooling
- Reusable tasks from a shared catalog
- Supply chain security with Tekton Chains
Cons
- Low-level and verbose for everyday CI
- Requires running Kubernetes
- No hosted offering from the project
How this score is calculated
Editorial fit ×0.346
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