Prefect

Prefect turns ordinary Python functions into production-grade workflows by adding scheduling, retries, state tracking, and observability with minimal decoration. Slap @flow on a function and you get automatic retry logic, logging, and run history without learning a new framework or rewriting your code.

It's built for data engineers and ML teams who need reliable pipelines but don't want to babysit infrastructure. Prefect Cloud runs the orchestration control plane, so there's no scheduler to host or upgrade. Your code and data stay inside your own environment (VPC, Kubernetes, ECS, or serverless), while scheduling and visibility stay central. That hybrid model is what gets most security and compliance reviews through.

Key capabilities:

• Durable execution: flows retry on failure, recover mid-run, and scale to thousands of parallel tasks with global concurrency limits
• Flexible scheduling: cron, event-driven triggers, and backfills, all with automatic recovery when runs fall behind
• Unified observability: one UI shows every run, log, and state across all teams and infrastructure, with SLA tracking and failure alerts
• Deploy anywhere: the same code runs on managed compute, your own VPC, Kubernetes, or ECS without modification
• Automations: set alerts on failures, late runs, and SLA misses without building a custom monitoring stack

Compared to tools like Kestra or Mage, Prefect's main differentiator is that workflows are just Python functions. There are no DAG definition files, no XML, no domain-specific language. A coding agent that knows Python can generate a working flow directly. Teams that already write Python pipelines can adopt Prefect incrementally, one flow at a time.

The framework is open source under Apache 2.0. Prefect Cloud adds a managed control plane, enterprise authentication, priority support with a 30-minute response SLA, and the scale needed for production workloads running millions of runs.