Kubeflow vs MLflow
| Feature | Kubeflow | MLflow |
|---|---|---|
| Category | AI / ML | AI / ML |
| Sub-category | ML Pipeline | ML Pipeline |
| Maturity | stable | mature |
| Complexity | expert | intermediate |
| Performance tier | enterprise grade | enterprise grade |
| License | Apache-2.0 | Apache-2.0 |
| License type | permissive | permissive |
| Pricing | fully free | fully free |
| GitHub stars | 14.0K | 20.0K |
| Contributors | 600 | 800 |
| Commit frequency | weekly | daily |
| Plugin ecosystem | none | large |
| Docs quality | good | excellent |
| Backing org | Google / CNCF | Linux Foundation / Databricks |
| Funding model | foundation | foundation |
| Min RAM | 8 GB | 1 GB |
| Min CPU cores | 4 | 1 |
| Scaling pattern | distributed | horizontal |
| Self-hostable | Yes | Yes |
| K8s native | Yes | Yes |
| Offline capable | No | No |
| Vendor lock-in | none | none |
| Languages | Python, Go | Python, Java, R |
| API type | REST, SDK | REST, SDK |
| Protocols | HTTP | HTTP |
| Deployment | docker | pip, docker |
| SDK languages | python | python, java, r, javascript |
| Team size fit | medium, enterprise | solo, small, medium, enterprise |
| First release | 2018 | 2018 |
| Latest version | — | — |
When to use Kubeflow
- ✓ End-to-end ML pipelines on Kubernetes
- ✓ Distributed training across GPU clusters
- ✓ Hyperparameter search with Katib
- ✓ Model serving with KServe at scale
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
Kubeflow anti-patterns
- ✕ Requires Kubernetes — not for simple setups
- ✕ Complex installation and maintenance
- ✕ Overkill for single-model projects
- ✕ Steep learning curve
MLflow anti-patterns
- ✕ Not a training framework itself
- ✕ Self-hosted tracking server needs PostgreSQL setup
- ✕ UI can be slow with very large experiment counts