Weights & Biases → MLflow
What it saves, what actually moves, what you rebuild — and the thing that catches people.
What it costs, and what it saves
From about $50 per user per month plus storage and tracked-hour overage, to free. A six-person team goes from roughly $3,600 a year plus storage to the cost of a small server and an object bucket — call it $20 a month.
Moves cleanly
Run history and metrics, via the W&B export API. Both tools log the same primitives — params, metrics, artefacts, tags — so the concepts map almost one to one.
You rebuild
Sweeps. W&B's hyperparameter orchestration has no direct MLflow equivalent; you either drive searches with Optuna and log to MLflow, or move that specific need to Determined AI. Reports also do not port — they are W&B's best feature and you will miss them.
What Weights & Biases costs you today
Free for personal and academic use with generous limits. Team plans are per-seat, commonly around $50 per user per month on the standard tier, plus storage and tracked-hour charges above the included allowance. Enterprise and self-hosted deployments are negotiated separately and are substantially more.
What actually holds you in
Moderate. New logging code is a small change — the APIs are similar enough across these tools that swapping is largely mechanical, and MLflow can ingest a good deal of history. The sticky part is accumulated run history and stored artefacts, which is the whole reason the tool exists. Export before you cancel, and be aware that pulling large artefact stores out takes longer than the migration itself.
What you are moving to
MLflow is the most widely adopted open ML platform, and its scope is why: experiment tracking, a model registry with stage transitions, reproducible project packaging and deployment tooling, all Apache-2.0. It runs as a local file store for one person or as a server with a database and object store for a team, integrates with essentially every framework, and is supported natively by every major cloud — so it is the option least likely to become a dead end.
Free, Apache-2.0. Managed versions are sold by the clouds if you want one.
MLflow strengths
- Covers tracking, registry, packaging and deployment — not just tracking
- The de facto standard; integrated with every major framework and cloud
- Scales from a local directory to a full server deployment
- Apache-2.0 with unlimited seats
What you give up
- UI is functional rather than pleasant, and slows on very large run counts
- Team deployment means running a server, a database and object storage
- No built-in sweep orchestration comparable to W&B's
The migration, step by step
- 1Export everything before you cancel. Use the W&B public API to pull run history and artefacts. Checkpoints are large and this takes longer than the rest of the migration combined — start it first.
- 2Stand up MLflow with a real backend. `mlflow server --backend-store-uri postgresql://... --artifacts-destination s3://...`. The local file store is fine for one person and wrong for a team.
- 3Swap the logging calls. `wandb.log({...})` becomes `mlflow.log_metrics({...})`; `wandb.config` becomes `mlflow.log_params`. Usually a handful of lines per training script.
- 4Point artefact storage at your own bucket and set a lifecycle policy while you are there. Unbounded checkpoint growth is what made the W&B bill climb.
- 5Move the model registry across. MLflow's registry with stage transitions covers what most teams used W&B artefacts for, and it is the piece that replaces a second tool.
- 6Run both for one training cycle, then cancel — after confirming the export actually landed.
The gotcha
MLflow's default file-based store will not survive a team. It has no concurrency control, so two runs writing at once can corrupt the store, and the UI crawls past a few thousand runs. Deploy with PostgreSQL and object storage on day one; retrofitting it after you have history is far more annoying than doing it first.
When to stay on Weights & Biases
Reports are doing real work for you. Turning a training run into something a non-ML stakeholder will actually read is W&B's genuine advantage and no open tool matches it. Stay too if you run large distributed sweeps, where the hosted orchestration is mature, or if your team is small enough that per-seat pricing is not the dominant line and nobody wants to own a tracking server, a database and a bucket.
More Layer 4 migrations
Figures verified 2026-08-11 against vendor pricing pages. Prices change and migrations differ by estate — treat the cost delta as a starting model, not a quote. Rankings and recommendations here are merit-only; affiliate income never changes a verdict. See our methodology.