Prefect or Dagster?

Use Prefect to write Pythonic workflows that automate every bespoke workflow in your business. With Prefect’s configurable model, your flows can run on dynamically deployed infrastructure and scale out with ease.

The Prefect Approach

Prefect is optimized for handling the long-tail of custom Python workflows. Prefect's infrastructure design empowers data engineering teams to handle versatile deployment options, while the code design minimizes the cost of failure with transactional rollback semantics.

The Dagster Approach

Analytics engineers may find Dagster's asset-forward approach natural, but data engineers will quickly feel constrained when building custom Python workflows as the warehouse has become increasingly commoditized.

Workflow specific infrastructure

Instead of limiting your execution to single infrastructure types, customize deployment needs down to each flow. Prefect allows you to operate lean and securely—cut your infrastructure bill and development time when collaborating with devops.

2- name: deployment-1
3  entrypoint: flows/
4  work_pool: *my_docker_work_pool
5  # Use default Docker build
6  build: *docker_build
8- name: deployment-2
9  entrypoint: flows/
10  work_pool: *my_docker_work_pool
11  build:
12      - prefect_docker.deployments.steps.build_docker_image:
13          <<: *docker_build_config
14          # Override with custom Dockerfile
15          dockerfile: Dockerfile.custom
Event-based triggers

Prefect Cloud does not rely on clunky sensors—it is event-based at its core. This means flows can run as a response to internal or external webhook events, which are first-class observable objects within the Prefect dashboard.

Self-managed secure option

Prefect is built for highly regulated industries. Our out-of-the-box hybrid model does not have access to your data. Additionally if required, run a replica of Prefect Cloud fully within your own infrastructure, with dedicated support from our team.

Framework for agentic workflows

ControlFlow is a Python framework for building AI workflows, powered by Prefect 3.0. ControlFlow's task-centric approach brings software engineering principles to AI development. Assign specialized agents to your tasks, each with its own instructions, tools, and even different LLM models.

1import controlflow as cf
2from pydantic import BaseModel
5class ResearchTopic(BaseModel):
6    title: str
7    keywords: list[str]
11def research_workflow() -> str:
12    topic = cf.Task(
13        "Generate a research topic",
14        result_type=ResearchTopic,
15        user_access=True,
16    )
17    outline = cf.Task("Create outline", context=dict(topic=topic))
18    draft = cf.Task("Write 1st draft", context=dict(outline=outline))
19    return draft
22result = research_workflow()
Cron-based scheduling
Task dependency UI
Serverless orchestration option
Retries & logging
Framework for agentic workflows
Workflow-specific infrastructure
Event-based triggers
Self-managed option
Data lake support
Data asset centric