Kedro vs Metaflow
| Feature | Kedro | Metaflow |
|---|---|---|
| Category | AI / ML | AI / ML |
| Sub-category | ML Pipeline | ML Pipeline |
| Maturity | stable | stable |
| Complexity | intermediate | intermediate |
| Performance tier | medium | medium |
| License | Apache-2.0 | Apache-2.0 |
| License type | permissive | permissive |
| Pricing | fully free | fully free |
| GitHub stars | 10.0K | 8.0K |
| Contributors | 250 | 150 |
| Commit frequency | weekly | weekly |
| Plugin ecosystem | none | none |
| Docs quality | excellent | excellent |
| Backing org | McKinsey QuantumBlack | Netflix / Outerbounds |
| Funding model | corporate | vc_backed |
| Min RAM | 512 MB | 512 MB |
| Min CPU cores | 1 | 1 |
| 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 | Python |
| API type | SDK | SDK |
| Protocols | HTTP | HTTP |
| Deployment | pip | pip |
| SDK languages | python | python |
| Team size fit | solo, small, medium | solo, small, medium |
| First release | 2019 | 2019 |
| Latest version | — | — |
When to use Kedro
- ✓ Standardize ML project structure across teams
- ✓ Build reproducible data transformation pipelines
- ✓ Visualize data dependencies with Kedro-Viz
- ✓ Transition from notebooks to production code
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
Kedro anti-patterns
- ✕ Opinionated project structure may not fit all teams
- ✕ Learning curve for catalog system
- ✕ Less suited for real-time or streaming
- ✕ Smaller community than Airflow/MLflow
Metaflow anti-patterns
- ✕ AWS-centric cloud integration
- ✕ Less community than MLflow
- ✕ No built-in model registry
- ✕ Not a full MLOps platform