Quick comparison # App Pricing Verified price Open source Platforms Verified facts Score 1 Apache AirflowOpen source — Yes Linux, Self-hosted, Web 5 of 5 68.8 2 PrefectOpen source $100/month Yes Web, Linux, macOS +2 5 of 5 64.8 3 FlyteOpen source — Yes Self-hosted 4 of 4 61.8 4 TektonOpen source — Yes Self-hosted 4 of 4 52.6
1 Best for: Data engineering teams that want a proven, free orchestrator with a huge integration ecosystem
Open source Self-hostable Linux, 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 providersCons 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 itselfHow this score is calculated Editorial fit ×0.3 100
Reviews ×0.3 63
Clicks (90 days) ×0.15 0
Freshness ×0.15 100
Votes ×0.1 50
0 counted reviews, 0 up and 0 down votes. Full formula
2 Best for: Python teams who want orchestration without rewriting code into a separate DAG framework
Open source Self-hostable Web, 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 serverlessCons 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 pricingHow this score is calculated Editorial fit ×0.3 87
Reviews ×0.3 63
Clicks (90 days) ×0.15 0
Freshness ×0.15 100
Votes ×0.1 50
0 counted reviews, 0 up and 0 down votes. Full formula
3 Best for: ML and data teams on Kubernetes who want typed, reproducible Python pipelines.
Open source Self-hostable Self-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.aiCons Needs a Kubernetes cluster for production use Flyte 2 changes the SDK, so Flyte 1 code needs migrating Smaller community than AirflowHow this score is calculated Editorial fit ×0.3 77
Reviews ×0.3 63
Clicks (90 days) ×0.15 0
Freshness ×0.15 100
Votes ×0.1 50
0 counted reviews, 0 up and 0 down votes. Full formula
4 Best for: Platform teams building their own CI/CD system on Kubernetes
Open source Self-hostable Self-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 ChainsCons Low-level and verbose for everyday CI Requires running Kubernetes No hosted offering from the projectHow this score is calculated Editorial fit ×0.3 46
Reviews ×0.3 63
Clicks (90 days) ×0.15 0
Freshness ×0.15 100
Votes ×0.1 50
0 counted reviews, 0 up and 0 down votes. Full formula