Prefect

Prefect

4.6
(6543 reviews)

Orchestrate workflows with resilience and ease.

Prefect is an open-source automation platform designed to orchestrate workflows across data, machine learning, and agents, providing a unified control plane for scalable and resilient systems without rigid structures.

Productivity & CollaborationWorkflow AutomationOrchestrate workflowsopen-sourceproduction-ready

Overview

Prefect is an open-source, production-ready automation platform designed to orchestrate workflows and unify your entire stack on a single control plane. It serves as a comprehensive engine for Data, Machine Learning (ML), and Agents, enabling users to manage their processes seamlessly.

Key features of Prefect include the ability to turn fragile LLM loops into resilient systems with human-in-the-loop control, scale training from local experiments to thousands of parallel runs, and make revenue-impacting decisions on reliable dbt and SQL pipelines. Additionally, Prefect allows users to handle emails, user-facing jobs, and asynchronous processing without the need for a worker fleet, and unifies hundreds of microservices on a self-serve control plane.

Targeted at data scientists, ML engineers, and data engineers, Prefect empowers users to run their existing Python code as-is, without rigid DAG structures or custom DSLs, fitting seamlessly into their workflows. The main benefits of using Prefect include increased reliability in automation, enhanced scalability, and the ability to make informed decisions based on robust data pipelines.

Screenshots & Videos

Homepage screenshot of https://www.prefect.io

Pricing Plans

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Pricing Model: freemium - $250/month (pro)

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Pros (7)

  • Open-source and production-ready
  • Unifies entire stack on a single control plane
  • Turns fragile LLM loops into resilient systems with human-in-the-loop control
  • Scales training from local experiments to thousands of parallel runs
  • Enables reliable dbt and SQL pipelines for revenue-impacting decisions
  • Handles emails, user-facing jobs, and async processing without a worker fleet
  • Runs existing Python code as-is without rigid DAG structures or custom DSLs

Cons (1)

  • Limited security compliance information

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