The choice is mostly about who runs it
All three do the core job: log experiments, compare runs, version datasets and models, and produce something a team can review. The differences that matter in practice are who operates the service, what happens when you have thousands of runs, and how much of your workflow is LLM rather than classical ML.
✓ Quick answer
- Best hosted experience and LLM tooling: Weights & Biases
- Best free and self-hosted option: MLflow — open source, no vendor lock-in
- Best at very high run volume: Neptune — built for large-scale logging
- Best if you are already on Databricks: MLflow — managed and integrated
- Biggest 2026 change: W&B was acquired by CoreWeave — worth confirming roadmap before a long commitment
Weights & Biases
The most polished of the three, and the one most teams reach for first. Run comparison, sweeps for hyperparameter search, artifact versioning and report generation are all well built, and the collaborative reports feature is genuinely useful for sharing results with people who are not going to open a dashboard themselves.
W&B moved earliest into LLM-specific tooling — prompt tracking, trace inspection and evaluation workflows — which matters if your team has shifted from training models to building on top of them. That is where the practical gap over MLflow is widest.
The 2026 consideration: Weights & Biases was acquired by CoreWeave. For most users nothing has changed day to day, but if you are signing a multi-year contract it is worth asking directly about roadmap commitments and whether pricing or deployment options are expected to change under the new owner.
MLflow
Open source, Apache-licensed, and the de facto standard for teams that want to run their own tracking server. There is no per-seat cost and no vendor relationship — you host it, you own the data, and it works the same whether you are on AWS, GCP, Azure or your own hardware.
The trade-off is polish and operational overhead. The UI is functional rather than pleasant, comparing large numbers of runs is slower than in W&B or Neptune, and you are responsible for the database, storage and upgrades. For teams with platform engineering capacity, that is a reasonable price for zero lock-in and no per-seat billing.
If you are on Databricks, managed MLflow removes most of the operational burden while keeping the open-source format — which makes it the obvious default in that environment.
Neptune
Neptune's specialisation is scale of logging. If you are running thousands of experiments, or a smaller number of experiments producing very large volumes of metrics, Neptune handles it more gracefully than either alternative — queries stay responsive where other dashboards start to struggle.
It is a smaller product with a smaller community, so there are fewer tutorials, fewer Stack Overflow answers and fewer integrations. For a research team running large sweeps it is frequently the right answer; for a team of four running a handful of experiments a week, the scale advantage does not pay for the ecosystem gap.
Which to choose
| Situation | Choose | Why |
|---|
| Small team, want it to just work | Weights & Biases | Best UX, least setup, strongest collaboration features |
| No budget, or strict data control | MLflow | Free, self-hosted, no vendor relationship |
| Thousands of runs or huge metric volume | Neptune | Purpose-built for logging at scale |
| Already on Databricks | MLflow | Managed and natively integrated |
| Working mainly on LLM applications | Weights & Biases | Strongest prompt, trace and evaluation tooling |
| Need everything inside your own VPC | MLflow | Self-hosting is the default, not an enterprise add-on |
An honest note on switching cost
Experiment tracking is stickier than it looks. Once a team has a year of runs logged, comparing a new experiment against historical baselines requires that history — and migration between platforms is rarely clean. Choosing on a two-week trial is reasonable; expecting to switch painlessly in eighteen months is not. If lock-in worries you, MLflow's open format is the answer, and it is a legitimate reason to accept a less polished UI.
Frequently asked questions
Is MLflow really free?
The software is Apache-licensed and free. You pay in infrastructure and engineering time — you run the tracking server, the backing database and the artifact storage, and you handle upgrades. Managed MLflow on Databricks removes that work at a cost.
Does the CoreWeave acquisition change anything for W&B users?
Nothing has changed in day-to-day use. The reason to raise it is contractual: if you are committing to a multi-year agreement, ask about roadmap continuity and whether deployment or pricing options are expected to change. That is standard diligence after any acquisition, not a specific warning.
Can I use these for LLM applications rather than model training?
Weights & Biases has the most developed tooling for prompt tracking, trace inspection and evaluation. MLflow has added LLM tracking features but they are less mature. If LLM application development is the majority of your work rather than classical ML training, that gap is the main thing separating them.