Prefect vs Temporal

Prefect

Modern Python-native workflow orchestration

Temporal

Durable workflow execution platform for microservices

Feature Prefect Temporal
Category Automation Automation
Sub-category Workflow Workflow
Maturity stable stable
Complexity intermediate intermediate
Performance tier medium medium
License Apache-2.0 MIT
License type permissive permissive
Pricing fully free fully free
GitHub stars 18.0K 13.0K
Contributors 300 0
Commit frequency weekly weekly
Plugin ecosystem none none
Docs quality good good
Backing org Prefect Temporal Technologies
Funding model vc_backed vc_backed
Min RAM 1 GB 2 GB
Min CPU cores 1 2
Scaling pattern single_node single_node
Self-hostable Yes Yes
K8s native No No
Offline capable No No
Vendor lock-in none none
Languages Python Go
API type REST REST
Protocols HTTP HTTP
Deployment pip, docker docker, npm
SDK languages
Team size fit small, medium solo, small, medium, enterprise
First release 2020 2020
Latest version

When to use Prefect

  • Python-native data pipeline orchestration
  • ML training pipeline management
  • Scheduled ETL with automatic retries

When to use Temporal

  • Primary: long-running-workflows
  • Primary: microservice-orchestration
  • Primary: saga-pattern

Prefect anti-patterns

  • Python-only
  • Cloud UI is the best experience — self-hosted UI limited
  • Smaller plugin ecosystem than Airflow

Temporal anti-patterns

  • Complex to operate in production
  • Steep learning curve
  • Overkill for simple automations
Full Prefect profile → Full Temporal profile → All comparisons