Weights & Biases vs MLflow vs Comet (2026): Choosing by Constraint, Not Hype

Weights & Biases, MLflow, and Comet get compared as if they were three brands of the same product. They are not. They overlap heavily on the surface — all three track experiments, store metrics, register models, and now do LLM tracing — but the decision that matters is rarely “which has the nicer charts.” It is a decision about a constraint: who hosts it, who pays, and whether your data can leave your boundary. Pick by the constraint and the rest follows.

What all three genuinely do

To be fair to the “they’re all the same” instinct, the common ground is real. Every one of these will: log metrics and params per run, version artifacts, store and compare experiments in a UI, provide a model registry with stage transitions, and offer some path to LLM/agent tracing and evaluation. If your only requirement is “see my training curves and compare runs,” any of the three works on day one. The differences appear at the edges — and the edges are where production teams live.

MLflow: the open-source, self-hosted default

MLflow’s distinguishing fact is its license and deployment model. It is Apache-2.0 open source, runs entirely on your own infrastructure, and has no paid tier, usage cap, or feature gate — the cost is the infrastructure and maintenance you take on, not a per-seat bill. You can start trivially (SQLite plus local file storage on one host) and scale to a Postgres backend with S3/GCS/Azure artifact storage when you outgrow it.

• Owns: the “our data does not leave our boundary” requirement, a strong cross-ecosystem model registry (classical ML and LLM under one registry with stage transitions and lineage), and vendor-neutrality. If procurement, compliance, or air-gap rules are your constraint, MLflow is usually the answer before you compare features.
• Costs you: the glue. You run the tracking server, manage the database and artifact store, handle backups and access control, and own upgrades. The dashboards and collaboration polish are more utilitarian than the commercial options.

Weights & Biases: the managed collaboration layer

W&B is a managed SaaS (with self-managed options for enterprise) whose strength is the experience around experiments — rich interactive dashboards, report-building for sharing results, sweeps for hyperparameter search, and team collaboration that feels designed rather than assembled. It offers a free tier (small seat count and a few GB of storage) and paid plans above that.

• Owns: team-scale experiment collaboration and visualization. If many people need to look at, annotate, and share runs, and you are comfortable with a hosted service holding that data, W&B’s polish is the differentiator.
• Costs you: money at scale (per-seat pricing climbs as the team grows) and a data-residency decision. The managed convenience and the data-leaving-your-boundary trade-off are the same coin.

Comet: the W&B-style managed option with an open LLM path

Comet sits closest to W&B in shape — a managed experiment-tracking and model-management platform with comparable dashboards and an enterprise orientation — and differentiates on its LLM observability story, including an open-source LLM tracing/evaluation path you can adopt without buying the whole platform.

• Owns: teams that want a W&B-style managed experience but want the generative-AI tracing/eval piece available on an open-source path, so the LLM-observability component is not locked behind the full commercial product.
• Costs you: like W&B, it is a per-user managed model with an enterprise focus and a limited free tier; the same data-residency question applies.

The decision that actually matters

Stop comparing dashboards and answer three questions in order:

1. Can your data leave your boundary? If no (compliance, air-gap, sensitive data), the comparison is effectively over — self-hosted MLflow, or the self-managed enterprise tier of a commercial tool. Most teams stop here.
2. Who is paying, and at what team size? Per-seat managed pricing is cheap for five people and a real line item for fifty. MLflow trades that bill for infrastructure-and-maintenance cost — which is not free, just shaped differently. Be honest about which cost your org would rather carry.
3. Is your hard requirement collaboration polish, registry/lineage, or LLM tracing? Collaboration and visualization → W&B leads. One registry across classical ML and LLM with strong lineage → MLflow leads. Managed experience with an open LLM-observability path → Comet’s angle.

Avoid the lock-in trap regardless of choice

Whichever you pick, instrument your LLM and agent traces against the OpenTelemetry GenAI semantic conventions ↗ rather than a vendor’s bespoke SDK shape. All three tools increasingly ingest or interoperate with OTel, and standard traces keep you portable. The most expensive mistake here is not picking the “wrong” tool — all three are capable — it is wiring your instrumentation so tightly to one SDK that re-evaluating in a year means re-instrumenting everything.

The honest summary

There is no universally best choice, only a best choice for your constraint. MLflow is the right answer when control and registry are the constraint and you can absorb the operational cost. W&B is the right answer when collaboration and polish are the constraint and managed-SaaS data residency is acceptable. Comet is the right answer when you want that managed experience but specifically value an open path for LLM observability. The teams that regret their pick almost always chose on feature checklists instead of the host/pay/data-residency question — which is the one the marketing pages are quietest about.

See also

• The open-source ML observability stack: Evidently to Phoenix
• End-to-end tracing for LLM applications: what belongs in a span