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By the team that created FastMCP and Marvin

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:

LLM API calls can fail due to rate limits, timeouts, or service issues
Agent workflows branch unpredictably based on context and reasoning
Training jobs may crash due to resource constraints or infrastructure problems
Model outputs vary and can trigger different code paths during execution
Batch jobs might fail on some inputs while succeeding on others
Some workflows need human input at points you can't predict upfront

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

Configure tasks to automatically retry when they fail

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

Prefect can process items in parallel as separate tasks

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.


Prefect workflows can wait for external input, then resume when ready.

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

From Our Customers, To You

Actium Health: 99% decrease in ML model training time
Relativity: Processing 300K+ Documents Daily
Endpoint: Increased ML turnaround time by 3x
Made with ☕️ by Prefect

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.

Build powerful MCP servers in seconds

Expose tools, resources, and prompts with clean, Pythonic code.

Everything you need, built-in

FastMCP handles the hard parts in one cohesive toolkit.

The official MCP standard, trusted by thousands

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

Adithya B.

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

Dr. Wolfgang S.

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

Andreas N.

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

Wendy T.

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

Kamilly R.

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

Sunil U.

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

ML Engineering Lead

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

Jonathan W.

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

Kaleb K.

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