Workflow Orchestration for AI Teams
Orchestrate and monitor fully dynamic AI workflows, written entirely in Python.
The Reality of Building AI Systems
Here's what can happen when you put AI workflows in production:
Why AI teams choose Prefect
Deploy models faster without sacrificing flexibility. Prefect bridges the gap between ML experimentation and production, letting you focus on models while we handle the infrastructure.
Workflows Adapt to Your Code Structure
You write Python functions. Prefect manages how they execute and tracks the workflow based on actual behavior.

Native Human-in-the-Loop Workflows
Workflows can pause for human approval, input, or review, then resume immediately.

Intelligent Retries for AI Workflows
Add retry logic to tasks with parameters. Useful for LLM API calls, model training, and data processing operations that can fail due to transient issues.

Hybrid AI Execution
Run workflows on your laptop, in the cloud, or across multiple environments. Workers connect to Prefect Cloud but execute in your infrastructure.

Deploy from Git with Automatic Versioning
Connect your GitHub repository to Prefect, and deployments automatically sync with your commits. Roll back problematic deployments and revert to any previous version in seconds.

Observability That Helps Debug Issues
See exactly where workflows failed, what inputs caused problems, and how long each step took.

How Prefect Handles AI Challenges
If a training job crashes, Prefect can restart it without losing all progress. You can set retry delays and limits based on your needs.

f some items fail, the successful ones continue processing. You get visibility into exactly which items succeeded or failed, and can rerun just the failures.

This enables human-in-the-loop AI workflows where people review results or provide guidance at key decision points.

From Our Customers, To You
FastMCP: Connecting AI to Everything
FastMCP makes it simple to build AI-native APIs using the Model Context Protocol (MCP). FastMCP turns complex protocol implementation into straightforward Python functions.
Expose tools, resources, and prompts with clean, Pythonic code.
FastMCP handles the hard parts in one cohesive toolkit.
14.6k+ stars. FastMCP powers Anthropic's official MCP Python SDK.
Your LLM’s interface to the world
The fast, Pythonic way to build MCP servers and clients.

Hear from ML teams

Prefect helps us automate the workflow pipelines and data processing jobs which help feed into larger ML model systems. With Prefect, we can host on multiple platforms and run jobs that suit our data needs and infra requirements. - G2 Crowd

Prefect helps me to automatically schedule and run data & machine learning workflows in the cloud. With this serverless setup, I am saving costs and dev/maintenance work. - G2 Crowd

Prefect elegantly solves the problem of Python script automation and data/workflow orchestration. It adds logging/observation to Python scripts. Prefect is the backbone of my data landscape - orchestrating my data integrations, data models, and machine learning training. - G2 Crowd

With Prefect, we're doing things like pulling data, transforming features, splitting data sets, and training models. We wanted to do more than Airflow could offer - like making sure very large and small tasks don't run on the same machine, and adding custom Python packages. - Case Study

Prefect allows us to monitor our machine learning models efficiently. The logging is very useful. - G2 Crowd

Prefect provided the flexibility to choose code storage, runners, and executors. The cherry on top was the ability to handle multi-tenancy, which simplified the workflows and reduced the development time. - G2 Crowd

Prefect's flexibility with compute resources let us run different parts of our pipeline on the right infrastructure - CPU for preprocessing, GPU for training, and distributed systems for inference. - G2 Crowd

Helps Us Focus On Our Areas of Expertise
Prefect has enabled our team to orchestrate the execution of a variety of services, with complex interdependencies, into a single flow. Automating complex workflows helps reduce user error and helps engineers focus on their areas of expertise. - G2 Crowd

It's helping us bridge the gap between on-prem legacy systems and modern cloud-based systems. It's allowed us to automate the routine tasks between those systems and saves us time and helps reduce errors. - G2 Crowd