Comparison

Dagster vs Prefect

No clear leader: Dagster (70.4) and Prefect (69.8) are within the 5-point margin; treat as a tie. The attribute-by-attribute breakdown below, with a source and date on every value, is the honest way to compare them.

Machine formatsJSONMarkdownGraphQLor send Accept: application/json
Dagster70
Prefect70
Score
Vioscale score
Dagster70 / 100medium · 68%
Prefect70 / 100high · 76%
Pricing
Free tier
Dagster
Prefect
Model
Dagsterfreemium
Prefectcommercial
Price level
Dagsterlow
Prefecthigh
Transparent
Dagster
Prefect
Integrations
Count
Dagster14
Prefect5
Reliability
Sla pct
Dagster
Prefect99.99
Status page
Dagster
Prefect
Adoption
Dependent repos
Dagster286
Prefect767
Github stars
Dagster16,066
Prefect23,691
Package downloads weekly
Dagster2,176,853
Prefect2,710,819
Activity
Commits last 30d
Dagster81
Prefect100
Release
Cadence days
Dagster7
Prefect1
History
Dagster20 items
Prefect20 items
License
Spdx
DagsterApache-2.0
PrefectApache-2.0
Language
Primary
DagsterPython
PrefectPython

Capabilities

Feature-by-feature on the axes that matter for data engineering tools. “-” means undocumented, not absent.

Core
Tool role
DagsterOrchestrator
PrefectOrchestrator
Processing paradigm
DagsterBatch
PrefectBatch
Connectivity
Connector count
Dagster-
PrefectNot specified
CDC / log-based replication
Dagster
Prefect-
Transformation
In-warehouse transformation (push-down)
Dagster
Prefect-
dbt-native orchestration
Dagster
Prefect
Deployment
Self-hosted / open-source available
Dagster
Prefect
Managed cloud available
Dagster
Prefect
Governance
Data lineage / asset catalog
Dagster
Prefect
Authoring
Python-first authoring
Dagster
Prefect
Execution
Incremental / partition-aware runs
Dagster
Prefect
Quality
Built-in data quality / tests
Dagster
Prefect-
Scale
Horizontal scale (distributed executor)
Dagster
Prefect

What each one is

The product in its own terms, so the numbers below have context.

Dagster

A unified orchestration and observability platform that manages data pipelines through assets, tracks dependencies and lineage, enforces data quality, and integrates with existing tools like dbt, Snowflake, and Spark. Supports both self-hosted open-source and managed cloud deployment.

Independently observed

Prefect

A workflow orchestration platform that enables teams to build resilient automated processes using Python, with features like automatic retries, state tracking, and run observability. Supports flexible deployment options ranging from managed cloud infrastructure to self-hosted environments on Kubernetes or user-provided compute.

Independently observed

Pricing

List pricing as published by each vendor, with the date we read it. Always verify at the source before you buy.

Dagster

from $0.01/moSubscription30-day trial

From $120/month. 30-day free trial. Subscription with metered credits (asset materializations and ops executed); serverless compute charged separately.

  • Solo$120/month with 7.5k credits/month included; overages at $0.040/credit
    • 1 User
    • 1 Code location
    • 1 Deployment
    • Core orchestration
    • Asset-based orchestration
    • +6 more
  • Starter$1200/month with 30k credits/month included; overages at $0.035/credit
    • Up to 3 Users
    • 5 Code locations
    • 1 Deployment
    • Catalog search
    • Column-level lineage
    • +8 more
  • Pro/EnterpriseContact sales
    • Unlimited code locations
    • Unlimited deployments
    • Custom serverless compute pricing
    • Cost tracking and insights
    • Personalized onboarding support
    • +8 more
as of verify ↗

Prefect

from $100/moSubscriptionFree tier

Free tier. Starter from $100/mo. Team at $100/user/mo. Enterprise custom pricing.

  • HobbyFree
    • workflow scheduling
    • workflow observability
    • logging & alerting
  • Starter$100/mo
    • bring your own compute
    • webhooks
    • API access
  • Team$100/user/mo
    • service accounts
    • audit logging
    • team collaboration
  • EnterpriseContact sales
    • SSO (SAML/OIDC)
    • RBAC with object-level ACLs
    • multiple workspaces
    • directory sync (SCIM)
    • IP allowlisting
    • +1 more
as of verify ↗

Platform & deployment

Where each product runs and how it can be hosted. A dash means undocumented, not unsupported.

Platforms
Web
Dagster
Prefect
CLI
Dagster
Prefect
Deployment
Cloud / SaaS
Dagster
Prefect
Self-hosted
Dagster
Prefect
Hybrid
Dagster
Prefect

Integrations

What each product connects to. Counts come from the vendor's own integration directory where one exists.

In common (1)
  • dbt

Dagster

14 total - 13 not shared
  • Snowflake
  • Spark
  • Databricks
  • Azure
  • AWS
  • Airbyte
  • Tableau
  • Soda
  • Slack
  • Microsoft Teams
  • PagerDuty
  • Datadog
  • Python
Independently observed

Prefect

5 total - 4 not shared
  • Pydantic AI
  • OpenAI
  • AWS Secrets Manager
  • HashiCorp Vault
Independently observed

Comparison generated from independently-sourced facts. Every value links to its source and retrieval date. See the method.