MLflow vs W&B Weave
A side-by-side comparison of MLflow and W&B Weave, two Observability tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | MLflow | W&B Weave |
|---|---|---|
| Category | Observability | Observability |
| Pricing (differs) | FREE | FREEMIUM |
| License (differs) | Open source | Open core |
| Deployment (differs) | Self-host | Hybrid |
| Platforms (differs) | Web, CLI, API, Linux, macOS, Windows | API, Web |
| Model support (differs) | Model-agnostic | BYO key / model |
| Vendor (differs) | Linux Foundation | Weights & Biases |
The honest brief
MLflow
The most widely adopted open-source option: one platform spanning tracing, evals, prompt registry, and classic ML.
- Fully open source, no lock-in
- OpenTelemetry-based, framework-agnostic
- Built-in metrics and LLM judges
- Large community + Linux Foundation backing
- Self-host on your own infrastructure
- Self-hosting adds operational overhead
- Broad scope can feel heavy for simple needs
- Managed convenience needs Databricks or DIY
- UI less polished than some SaaS rivals
W&B Weave
One @weave.op decorator handles both tracing and evaluation, tied into the mature W&B experiment-tracking platform.
- Single decorator traces every call
- Tracing + evaluation in one SDK
- LLM-as-judge and custom scorers
- Apache-2.0 SDK
- Ties into W&B experiment tracking
- Traces land in W&B hosted platform
- Best value if already on W&B
- Free only for solo use