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It's a split decision.
Prefect turns Python functions into durable workflows, with retries, scheduling, and execution options from managed compute to your own infrastructure. Dagster centers orchestration on data assets, lineage, dependencies, and health, with hybrid deployment and native integrations including dbt, Snowflake, and Fivetran. Pick Prefect for a Python-first workflow model and its free Hobby tier. Pick Dagster when asset-level context and observability are central. Both are open source and self-hostable; check license terms and export options before committing.
01Side by side
| Prefect | Dagster | |
|---|---|---|
| Pricing model | Monthly plans; Team charges per user | Monthly plans plus credit and compute usage |
| Free tier | Hobby free; 2 users, 5 deployments | No free plan; 30-day Solo trial |
| Open source and license | Open source; license not named | Open source; license not named |
| Self-hosting | Yes; run on your infrastructure | Yes; hybrid deployment supports your infrastructure |
| API and SDKs | Python @flow and @task; SDK matrix not listed | Asset-based pipelines; SDK matrix not listed |
| Integrations | Python workflows; named native integrations not listed | Native dbt, Snowflake, and Fivetran integrations |
| Deployment model | Managed compute, Kubernetes, ECS, VPC, or serverless | Hybrid: compute in your infrastructure, managed control plane |
| Limits and scaling | Hobby: 500 serverless minutes monthly; Enterprise: unlimited deployments | Solo: 1 code location; Pro: unlimited locations and deployments |
Prefect Choose Prefect if
- Choose Prefect if Python functions are your preferred workflow interface.
- Choose Prefect if a free plan with 500 serverless minutes fits.
- Choose Prefect if you need Kubernetes, ECS, or VPC execution.
Dagster Choose Dagster if
- Choose Dagster if asset lineage and health are core requirements.
- Choose Dagster if you need its listed dbt, Snowflake, or Fivetran integrations.
- Choose Dagster if hybrid deployment with a managed control plane fits.
02Pricing at real usage
Prefect's free Hobby plan includes 2 users, 5 deployments, 500 serverless minutes per month, 7-day run retention, and 5 automations. Its Starter plan is $100 per month and includes 3 users, 20 deployments, and 75 serverless hours monthly. Team is $100 per user per month for 4–8 users, with 225 serverless hours monthly. Dagster has no free plan; Solo is $10 per month, with $0.040 per credit and a 30-day free trial. Starter is $100 per month for up to 3 users, with $0.035 per credit. On both Solo and Starter, serverless compute costs $0.010 per minute; hybrid deployments have no compute charge. Dagster's costs therefore grow with credits or serverless minutes, while Prefect usage can run into plan allowances. Ask both vendors how overages are handled; custom Enterprise and Pro pricing is not published.
03API, SDKs and integration
Prefect presents Python functions as the core workflow interface: add @flow to a function and use @task for task-level behavior such as retries. Its homepage describes scheduling, logging, run states, and recovery as part of that orchestration model. Dagster organizes work around assets and their dependencies, then adds lineage, data health, and operational context for diagnosing failures. Its homepage lists native integrations with dbt, Snowflake, and Fivetran. Prefect's listed integration points are its Python workflows and execution environments rather than named data connectors. Neither homepage identifies a full SDK language matrix, CLI command set, or API endpoint format. Teams choosing either tool should verify current API references, CLI support, and SDK version policies against their requirements. The main architectural distinction is function-oriented workflow authoring in Prefect versus asset-oriented orchestration in Dagster.
04Open source, self-hosting and lock-in
Both Prefect and Dagster are open source and self-hostable. The product pages do not name their open-source licenses, so check the repository license and any separate terms for hosted services before adopting either. Prefect can run workflows on Kubernetes, ECS, in a VPC, or on serverless compute; its homepage also offers Prefect Cloud for managed orchestration. Dagster's hybrid model keeps compute in your infrastructure while Dagster manages the control plane. That can reduce the need to move execution into a hosted service, but it still leaves a platform dependency for orchestration. Neither product page specifies data-export formats or a migration path between platforms. Before committing, ask how to export run history, logs, asset metadata, and configuration, and estimate the work to rewrite workflow definitions: Prefect's Python function model and Dagster's asset model are not interchangeable.
Questions people ask
Is Prefect or Dagster better for Python workflows?
Prefect is the more direct fit when you want to turn Python functions into workflows using @flow and @task. Dagster can orchestrate data pipelines too, but its defining model is assets and their dependencies. Choose based on whether your codebase is organized primarily around function runs or data assets.
How do Prefect and Dagster differ in data observability?
Prefect offers run observability, including workflow states and logs, plus alerts for failures, SLA misses, and late runs. Dagster emphasizes asset health, lineage, and dependencies across a data platform. If you need operational context centered on the data itself, Dagster's asset model is the closer match.
Can teams self-host Prefect and Dagster?
Yes. Both products are listed as open source and self-hostable. Prefect supports execution on infrastructure such as Kubernetes, ECS, or a VPC. Dagster describes a hybrid setup where compute stays in your infrastructure and it manages the control plane. Check license terms and deployment requirements before choosing.
Which is cheaper for a small production team?
It depends on usage. Prefect has a free Hobby plan with 500 serverless minutes per month; Starter is $100 monthly. Dagster Solo is $10 monthly plus credits, while Starter is $100 monthly plus credits. Compare expected compute, credit use, seats, and plan limits rather than base prices alone.