Prefect
Company Information
Headquarters
Washington, DC, USA
Year Founded
2018
Industries
Enterprise Software, Data Engineering, Cloud Computing, Machine Learning, IT Automation, Software Development, Data Analytics, Financial Services
What Is Prefect?
Prefect is a workflow orchestration and automation company that develops software for managing data pipelines, machine learning processes, and AI agent workflows. Its platform allows developers to turn Python code into automated workflows that can be scheduled, monitored, and executed across different computing environments.
The company provides an open-source orchestration framework and a managed cloud platform, helping organizations operate complex software processes without building their own scheduling and monitoring infrastructure. Prefect also develops technologies for connecting AI agents with enterprise tools and data.
What Does Prefect Do?
Prefect provides software that helps organizations automate and coordinate processes involving data, applications, and artificial intelligence.
Developers use Prefect to create automated workflows from Python functions, including data extraction, transformation, machine learning training, and AI agent operations. The platform manages scheduling, execution, retries, and error recovery, reducing the need to build these capabilities separately.
Prefect also provides centralized monitoring tools that allow engineering teams to track workflow performance, investigate failures, and receive alerts when processes do not complete as expected.
Through Prefect Cloud, organizations can manage workflows running in their own infrastructure or use managed computing resources.
The company also develops AI agent infrastructure, including tools that connect AI applications with databases, software services, and enterprise systems through the Model Context Protocol (MCP).
Prefect Technology
Prefect’s technology is built around Python-based workflow orchestration, allowing developers to coordinate complex processes using ordinary Python functions rather than defining workflows through specialized configuration languages.
Its open-source framework uses decorators such as @flow and @task to convert Python functions into monitored workflows. The orchestration engine manages execution states, scheduling, retries, caching, concurrency, and recovery from failures.
Prefect supports dynamic workflow execution, meaning processes can branch, repeat tasks, or change their execution path while running. This capability is particularly relevant for AI agents, which may select different actions based on model outputs or changing information.
Its cloud architecture separates workflow coordination from execution. Prefect Cloud manages scheduling and monitoring, while workers can execute code within an organization’s existing infrastructure, including Kubernetes, cloud services, and private computing environments.
Prefect also develops FastMCP, an open-source Python framework for building Model Context Protocol servers. MCP provides a standardized way for AI applications to access external tools, databases, and services.
Through Prefect Horizon, organizations can deploy and manage MCP servers with authentication, access controls, and monitoring. These capabilities help developers connect autonomous AI agents with enterprise systems while maintaining oversight of available tools and permissions.
The combination of workflow orchestration and MCP infrastructure allows Prefect to support traditional data engineering alongside emerging AI agent applications.
Why Prefect Matters
As organizations rely on more automated data pipelines and AI systems, coordinating their execution becomes an important software infrastructure challenge.
Prefect addresses this problem by providing reliable workflow execution and monitoring without requiring developers to build separate scheduling, retry, and recovery systems.
Its relevance extends beyond traditional data engineering. Autonomous AI agents increasingly need to access external tools, execute multiple steps, and recover from unexpected failures. Prefect provides infrastructure for managing these processes while supporting dynamic execution paths.
The company’s development of FastMCP and Horizon also places it within the growing ecosystem of AI agent infrastructure and enterprise AI integration.
Prefect illustrates how workflow orchestration is evolving from managing scheduled data processes toward coordinating AI-powered applications that interact with business systems and external services.
Prefect Products & Platforms
Prefect Open Source
Prefect Open Source is a Python workflow orchestration framework for building and running automated processes. Developers can schedule workflows, monitor execution states, configure retries, and recover from failures. It supports dynamic workflows for data engineering, machine learning, and AI applications.
Prefect Cloud
Prefect Cloud is a managed workflow orchestration platform that provides centralized scheduling, monitoring, automation, and governance. It supports hybrid execution, allowing organizations to run workflows within their own infrastructure, as well as serverless execution using managed computing resources. Enterprise capabilities include access controls, audit logs, alerts, and data lineage.
FastMCP
FastMCP is an open-source framework for building Model Context Protocol (MCP) servers. It allows developers to expose software functions, data sources, and external services as tools that AI agents can access. The framework simplifies the development of integrations between AI applications and existing enterprise systems.
Prefect Horizon
Prefect Horizon is an enterprise platform for deploying and governing MCP servers. It includes managed server deployment, a centralized registry, authentication, access controls, and monitoring. Its gateway manages how AI clients and agents access tools, helping organizations maintain security and oversight across MCP integrations.
Prefect Applications
Prefect supports workflow automation across data-intensive industries and software environments.
In data engineering, organizations use Prefect to coordinate data extraction, transformation, and loading processes between databases, cloud platforms, and analytics systems.
For machine learning and AI development, the platform can schedule model training, manage data preparation, and coordinate multi-step AI agent workflows.
In financial services, Prefect can automate data processing, reporting, and operational workflows. Healthcare and research organizations can use its orchestration capabilities to manage data pipelines and computational processes.
Software engineering teams also use Prefect for scheduled jobs, infrastructure automation, and application integrations.
Through FastMCP and Horizon, organizations can connect AI agents with internal databases, APIs, and enterprise applications while managing access permissions and tool execution.
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Frequently Asked Questions
How is Prefect different from Apache Airflow?
Prefect uses ordinary Python functions to define workflows and supports dynamic branching and task execution at runtime. Apache Airflow traditionally organizes workflows around directed acyclic graphs (DAGs). Prefect also provides managed scheduling, execution-state tracking, and failure recovery through its orchestration engine.
Can Prefect orchestrate autonomous AI agents?
Yes. Prefect supports AI agent workflows that involve conditional execution, repeated operations, external tool calls, and human approval steps. Its dynamic Python execution model allows workflows to respond to decisions made during an agent’s operation.
What is the difference between Prefect and FastMCP?
Prefect coordinates the execution of workflows, including scheduling, retries, and monitoring. FastMCP provides a framework for creating MCP servers that expose tools and data to AI applications. The two technologies address different requirements and can be used together.
Does Prefect require organizations to move their data to Prefect Cloud?
No. Prefect supports hybrid deployment, where workflow code and data remain within an organization’s infrastructure. Prefect Cloud manages scheduling and orchestration while receiving execution metadata and logs. Organizations can also choose managed execution when appropriate.
What happened to Dagster after Prefect acquired Dagster Labs?
Prefect acquired Dagster Labs in July 2026. Dagster remains available under its existing name and open-source license. The acquisition brought together Prefect’s Python workflow orchestration technology and Dagster’s asset-based data orchestration capabilities.
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