Interactive selector
MLOps Platform Selector
Weight what you actually need across eight capability axes, set your hard constraints (self-host, cloud, team size, OSS-only, budget), and get a ranked shortlist with a per-axis fit matrix and an explicit 'forces a second tool for…' gap note for each platform.
Scoring is transparent: capability scores (0–3) per axis are shown for every platform. Methodology in our platform selection framework (reviewed 2026-05-12).
All 10 platforms · capability scores (0–3)
| Platform | Experiment tracking | Model registry | Feature store | Model serving | Pipeline orchestration | Data versioning | Evaluation | Governance |
|---|---|---|---|---|---|---|---|---|
| MLflow ↗ Open source | 3 | 3 | 0 | 2 | 1 | 1 | 2 | 2 |
| Weights & Biases ↗ Commercial SaaS (self-host tier) | 3 | 3 | 0 | 1 | 1 | 2 | 3 | 2 |
| Amazon SageMaker ↗ Managed cloud (AWS) | 2 | 3 | 3 | 3 | 3 | 2 | 2 | 3 |
| Google Vertex AI ↗ Managed cloud (GCP) | 2 | 3 | 3 | 3 | 3 | 2 | 3 | 3 |
| Kubeflow ↗ Open source (Kubernetes) | 1 | 2 | 0 | 3 | 3 | 1 | 1 | 2 |
| ZenML ↗ Open source + managed | 2 | 2 | 1 | 2 | 3 | 2 | 2 | 2 |
| Metaflow ↗ Open source (Netflix/Outerbounds) | 2 | 1 | 0 | 1 | 3 | 3 | 1 | 1 |
| Databricks (Lakehouse + Mosaic AI) ↗ Managed platform (multi-cloud) | 3 | 3 | 3 | 3 | 2 | 3 | 3 | 3 |
| Feast ↗ Open source (feature store) | 0 | 0 | 3 | 1 | 0 | 1 | 0 | 1 |
| DVC + DVCLive ↗ Open source (data/model versioning) | 2 | 1 | 0 | 0 | 2 | 3 | 1 | 1 |