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.
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.
1deployments:
2- name: deployment-1
3 entrypoint: flows/hello.py:my_flow
4 work_pool: *my_docker_work_pool
5 # Use default Docker build
6 build: *docker_build
7
8- name: deployment-2
9 entrypoint: flows/goodbye.py:my_other_flow
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.customPrefect 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.
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.
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
3
4
5class ResearchTopic(BaseModel):
6 title: str
7 keywords: list[str]
8
9
10@cf.flow
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
20
21
22result = research_workflow()
23print(result)