Dagster vs Prefect - how to compare

Dagster vs. Prefect

Dagster and Prefect are often evaluated against one another despite being very different tools used for different use cases. In this guide, we answer the most common questions when comparing these two tools.

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Both Dagster and Prefect encourage local development, offer production infrastructure, and include integrations with other data tools. However, each aspect is influenced by the framework’s philosophy.

Prefect is a general-purpose workflow runner: in many ways, it’s an extension of the Python interpreter, helping software engineers run functions reliably with better retry behavior and logging. Prefect views the DAG as an inflexible abstraction that doesn’t generalize well. It adopts a pure Python approach that makes coding simple tasks much more straightforward.

Dagster is an orchestrator specifically for data engineers. While Dagster helps run Python functions reliably, the primary goal is to help teams define and build data assets in code. This declarative approach enables key capabilities that make data engineering teams far more productive.

How to compare Dagster and Prefect

Software Development Life Cycle

Prefect focuses on being minimally invasive, offering limited tooling to help software engineers run workflows locally and in production. This approach requires teams to design their workflows and infrastructure separately from Prefect's features for orchestration and monitoring.

Dagster provides a fully-featured integrated SDLC, allowing data teams to test locally before pushing to production. The robust production environment of Dagster+ includes branch deployments and data sandboxes, helping data practitioners evaluate PRs in an isolated setting which mirrors production before merging their changes.

In production

Dagster and Prefect offer self-hosted open-source stack options. Both also offer a hybrid deployment model. Additionally, Dagster offers a fully serverless environment for data teams who want to eliminate infrastructure management and get projects up and running quickly.


Both Dagster and Prefect offer numerous integrations, but the approach is very different. Consider dbt:

Dagster: Imports individual dbt models as assets, helping teams view dbt model lineage, and enables sub-graph runs, scheduling, and SLAs for specific dbt models. Wraps the dbt CLI under the hood.

Prefect: Wraps the dbt CLI, but requires teams to specify when and how the CLI should be called in workflows with no representation of the resulting data models.

To summarize:

Goal of the solution.Help software engineers run Python workflows more effectivelyHelp data engineers manage data assets
Run Python code reliably and provide flexibility for complex programming tasks.
  • @task and @workflow decorators create workflows of tasks
  • Retries
  • Run Queues, Parallelization (Dask) Conditional Execution
  • Logging
  • Dynamic Fan Out
  • @asset decorator creates DAG of assets
  • Retries
  • Run Queues, Parallelization Conditional Execution
  • Logging
  • Dynamic Fan Out
Data assetsNo Asset abstraction
  • Global Asset Lineage
  • Partitions & Backfills
  • Reconciliation
  • Freshness SLAs
  • Schedules
  • Sensors (Manually written)
  • Schedules
  • Sensors (Event-Driven)
  • SLA Driven Runs
Software development lifecycle
  • Local Development
  • Hybrid SaaS
  • Email Alerts
  • Local Development
  • GUI
  • Hybrid SaaS or Serverless Deployment
  • Email/Slack Alerts
  • Branch Deployments
  • Data Sandboxes
  • Separation of Logic/IO
  • Mock Resources
IntegrationsAPI wrapperAsset-first


Dagster has a growing community of forward-thinking engineers who see the value of our differentiated approach. The Dagster engineering team is directly involved in supporting both open-source and Dagster+ users.

Interested in getting an objective 3rd party perspective? Join the Dagster Slack and interact with current users.

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