Metaflow vs MLflow
| Feature | Metaflow | MLflow |
|---|---|---|
| Category | AI / ML | AI / ML |
| Sub-category | ML Pipeline | ML Pipeline |
| Maturity | stable | mature |
| Complexity | intermediate | intermediate |
| Performance tier | medium | enterprise grade |
| License | Apache-2.0 | Apache-2.0 |
| License type | permissive | permissive |
| Pricing | fully free | fully free |
| GitHub stars | 8.0K | 20.0K |
| Contributors | 150 | 800 |
| Commit frequency | weekly | daily |
| Plugin ecosystem | none | large |
| Docs quality | excellent | excellent |
| Backing org | Netflix / Outerbounds | Linux Foundation / Databricks |
| Funding model | vc_backed | foundation |
| Min RAM | 512 MB | 1 GB |
| Min CPU cores | 1 | 1 |
| Scaling pattern | single_node | horizontal |
| Self-hostable | Yes | Yes |
| K8s native | No | Yes |
| Offline capable | No | No |
| Vendor lock-in | none | none |
| Languages | Python | Python, Java, R |
| API type | SDK | REST, SDK |
| Protocols | HTTP | HTTP |
| Deployment | pip | pip, docker |
| SDK languages | python | python, java, r, javascript |
| Team size fit | solo, small, medium | solo, small, medium, enterprise |
| First release | 2019 | 2018 |
| Latest version | — | — |
When to use Metaflow
- ✓ Structure ML projects with reproducible steps
- ✓ Scale experiments to cloud with @batch decorator
- ✓ Version datasets and models automatically
- ✓ Transition notebook experiments to production
When to use MLflow
- ✓ Track and compare ML experiments across teams
- ✓ Version and deploy models to production
- ✓ Monitor LLM applications with tracing
- ✓ Manage AI Gateway for multi-provider LLM access
- ✓ Evaluate and optimize prompts systematically
Metaflow anti-patterns
- ✕ AWS-centric cloud integration
- ✕ Less community than MLflow
- ✕ No built-in model registry
- ✕ Not a full MLOps platform
MLflow anti-patterns
- ✕ Not a training framework itself
- ✕ Self-hosted tracking server needs PostgreSQL setup
- ✕ UI can be slow with very large experiment counts