Pick Weights & Biases if you want a managed platform with listed enterprise compliance such as ISO 27001, SOC 2 and HIPAA, and ready-made integrations. Pick MLflow if open source, control over your own hosting and avoiding a vendor platform matter most. There's no clear winner here: the right one depends on who's going to run the infrastructure.
Who keeps the lights on? That's the fork in the road with this pair. One is a commercial, hosted AI developer platform you sign up for. The other is an open source project you can run yourself, with a managed route if you'd rather not. Everything else, from LLM tracing to model registries, sits on top of that choice.
Both sides at a glance
| Area | Weights & Biases | MLflow |
|---|---|---|
| Model | Commercial hosted platform with sign up and demo pages | Open source project, with a separate MLflow on Databricks route |
| LLMs and agents | Weave for traces, evaluations, playground and monitors | Observability, evaluations, prompt registry and AI gateway |
| Classic ML | Experiment logging with one line of code; integrations for PyTorch, Keras, TensorFlow, Scikit-learn, XGBoost and Lightning | Experiment tracking, model evaluation, models format, model registry and deployment |
| Hosting options | Multi-tenant and dedicated deployment | Self-run open source, or the Databricks managed route |
| Security claims | ISO 27001, SOC 2 and HIPAA listed | No certification claims on its site |
| Who runs it | Company product, now part of CoreWeave Forge | A project of LF Projects, LLC |
| Setup | One-line SDK and a mobile app for watching runs | Lets a coding agent set up tracing for you |
A vendor product versus a community project
W&B is a company product. Its site sells it as the AI developer platform for tracking experiments and building agents, applications and models. In short, it's a hosted service with enterprise deployment options, and you don't run the servers.
The open source side runs as a project of LF Projects, LLC, and describes itself as an AI engineering platform for agents, LLMs and models. Its own pitch is that it's the leading open source option in that space, which is a vendor claim rather than something this page can settle. If you'd like to avoid tying your ML work to a single company's roadmap, that governance setup is the main attraction.
Ownership has shifted on both sides
Here's what changes the picture. W&B now describes its Models product as part of CoreWeave Forge. So it's no longer a standalone company product in the way many older comparisons assume. It sits inside a larger infrastructure offering, which may suit you if you already work with that provider, and may give you pause if you don't.
The open source project isn't quite as hands-off as the label suggests, either. Its docs point readers to a managed option on Databricks as well as the self-run route. So the honest framing isn't hosted versus self-hosted. It's a commercial platform versus an open source tool that also has a managed home. If Databricks is part of your thinking, our page on alternatives to Databricks is worth a read too.
Tracing LLMs and agents
Both have moved their headline messaging towards LLM and agent observability rather than only classic experiment tracking. On the hosted side that work lives in Weave, which covers traces, evaluations, a playground and monitors.
The open source project groups it under LLMs and Agents: observability, evaluations, a prompt registry and an AI gateway, with what it calls production-grade tracing and prompt management. It also highlights letting a coding agent set up tracing for you, which is a neat touch if your developers already work alongside one.
Experiment tracking hasn't gone away
If you're training models rather than calling them, the older features still count. The hosted platform says you can log experiments with one line of code, and it names integrations for PyTorch, Keras, TensorFlow, Scikit-learn, XGBoost and Lightning. It also has a mobile app for keeping an eye on training runs, which is handy if long jobs run overnight.
The open source side covers experiment tracking, model evaluation, its own models format, and a model registry with deployment. That's the full lifecycle in one project, which many teams like.
Security sign-off and deployment
For teams that need to pass a security review, the hosted platform lists ISO 27001, SOC 2 and HIPAA, along with multi-tenant and dedicated deployment. The open source project doesn't make certification claims on its site. That's normal for open source: if you run it yourself, compliance depends on how and where you host it, and that work lands on your own team.
Other ML platforms in the running
If neither suits, these five are the nearest rivals. Our wider roundup of the best MLOps tools goes further.
Comet
Best for: Teams building LLM agents who want Opik to turn trace data and eval results into code fixes, according to the vendor.
ClearML
Best for: Enterprises and cloud providers managing GPU infrastructure, with remote GPU access and Kubernetes deployment.
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Aim
Best for: Individual researchers after a light open source tracker that installs with pip and compares runs in a simple UI.
Valohai
Best for: Multi-cloud or on-prem ML pipelines needing automatic versioning, full lineage and one-click reproduction.
Kubeflow
Best for: Platform engineers already on Kubernetes who want an open source, modular, community-built AI platform.
The verdict for ML teams
Choose Weights & Biases if you want someone else to run the platform, need listed certifications such as ISO 27001, SOC 2 and HIPAA for a security review, and value ready-made framework integrations. Choose MLflow if open source, governance outside a single company and control over where your data lives matter more, keeping in mind there's a managed Databricks route if self-hosting turns out to be too much. Neither is the better tool in every case. Decide who runs the infrastructure, and the choice follows.
Do you make an MLOps or experiment tracking tool? Tool makers can ask to be considered for a featured place on this page.
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Frequently asked questions
Can I run MLflow myself?
Yes. It's an open source project, so your team can host it. Its docs also point to a managed option on Databricks.
Who owns Weights & Biases now?
Its site describes the Models product as part of CoreWeave Forge, so it now sits inside a larger infrastructure offering.
Which is better for LLM and agent tracing?
Both cover it. The hosted platform has Weave for traces, evaluations and monitors; the open source project offers observability, evaluations, a prompt registry and an AI gateway.
Which one helps with a security review?
The hosted platform lists ISO 27001, SOC 2 and HIPAA. With a self-run open source tool, compliance depends on how your own team hosts it.
Can I log experiments from PyTorch or Keras?
The hosted platform names integrations for PyTorch, Keras, TensorFlow, Scikit-learn, XGBoost and Lightning. The open source project covers experiment tracking across the model lifecycle.
