Trust your process,
not your agents.

Prefect is the name of the company and of its workflow orchestration product. The company makes three open source products: Prefect, for orchestrating workflows and AI agents; Dagster, for data orchestration; and FastMCP, for connecting agents to tools through the Model Context Protocol (MCP). Each has a managed service built on the same engine as its open source core.

Workflow orchestration coordinates the steps of a process: the order they run in, what happens when one fails, and who has to approve what before the work moves on. Prefect does this for Python data pipelines and AI agent workflows, with scheduling, retries, durable execution and human review built into the engine instead of into your code.

Data orchestration builds and maintains the data a business runs on: pulling it in from source systems, transforming it, testing it and keeping it fresh, with a record of where every table came from. In Dagster, each table, file or ML model is an asset defined in Python, so you can see what depends on what, which version is current and whether its quality checks passed. People and agents downstream all work from that checked version.

An MCP gateway is one governed endpoint between AI clients and your MCP servers. It authenticates every client, authorizes each tool call per agent and role, and logs the result, so agents reach the tools they are allowed to use and nothing else. FastMCP Gateway is the managed MCP gateway and MCP hosting service from the team behind FastMCP.

They answer three different questions about an agent. Dagster handles what it knows: your data and business knowledge, curated into context. FastMCP handles what it can do: which tools and systems each agent may reach, and the record of every call. Prefect handles what its goal is: the workflow that runs the work, checks every result and brings in a person when it should.

No. Each product works on its own and most teams start with one. Data teams usually start with Dagster, teams building MCP servers start with FastMCP, and teams automating workflows or agents start with Prefect. Using them together adds decision lineage: every outcome traces back to the context it used, the tools it called and who approved it.

Prefect workflows are plain Python functions that can branch, loop and create tasks at runtime, where Airflow asks you to declare a DAG up front. Durable execution is built into Prefect's engine, so retries and crash recovery are not code you write. And on Prefect's managed service there is no scheduler for your team to host or get paged about.

Yes. A Prefect workflow is ordinary Python with two decorators, so any coding agent that writes Python can produce a working flow, add retries, and deploy it without learning a DSL. Your engineers review and own the result, and the same is true for humans: if you can write a Python function, you can ship a Prefect flow.

Yes. All three are open source under the Apache 2.0 license, and each managed service runs the same engine rather than a fork. FastMCP 1.0 was folded into the official MCP Python SDK.

Only if you want to. Every product has a managed service. Prefect can run your flows on serverless compute, so you can start with no infrastructure, or use the hybrid model: workers inside your environment run the code next to your data while the managed control plane handles scheduling and visibility. Dagster offers the same choice of serverless or hybrid deployment. FastMCP Gateway can host your MCP servers or govern servers that stay in your own cloud. All three open source projects can also be self-hosted end to end.

Prefect acquired Dagster Labs in July 2026. Nothing changes for users of either product: Dagster keeps its name and open source license, and no customer needs to take any action. The full announcement is at prefect.io/prefect-acquires-dagster.