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 59.4 4 Argo WorkflowsOpen source — Yes Self-hosted 4 of 4 57.5
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 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 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 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 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 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.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 69
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 already on Kubernetes who want container-native batch and ML pipelines.
Open source Self-hostable Self-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 HeraCons Requires a Kubernetes cluster YAML definitions get verbose for data engineers used to Python No hosted version from the project itselfHow this score is calculated Editorial fit ×0.3 63
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