Go for Comet if you're shipping LLM agents and want open-source observability you can inspect yourself, a free tier and tracking of Claude Code and Codex usage. Go for Weights & Biases if you train or fine-tune models at scale, need listed compliance certifications or private and on-premises options, and you're fine with it sitting inside CoreWeave.
What are you building this quarter: agents that call large language models, or models you train yourself? Your honest answer to that matters more here than any feature grid, because each vendor now puts its weight behind a different half of AI work, even though both still sell tools for the other half.
Two products on each side, not one
It's easy to think of this as experiment tracker versus experiment tracker. It isn't any more. Each vendor sells two main products under the banner of an AI developer platform.
- Comet sells Opik for LLM observability and evaluation, and Experiment Management for classic ML tracking, artifacts and a model registry.
- W&B sells Weave for tracing and evaluating agentic apps, and Models for experiment tracking, hyperparameter sweeps and a registry.
So you're really comparing Opik with Weave, and Experiment Management with Models. Most teams will care far more about one of those pairings than the other.
W&B now sits inside an AI cloud
This is the change that reshapes the decision. The W&B Models page, as of Nov 2026, redirects to coreweave.com, where it's listed under a CoreWeave Forge menu next to GPU cloud, inference and sandboxes. In other words, it's now presented as part of an AI cloud vendor rather than an independent tracking tool.
For some teams that's a plus: if you already run training on CoreWeave GPUs, having tracking in the same family is tidy. For others it's a reason to pause, because the tool you pick for experiment records is one you'll live with for years. Neither reading is wrong. Just make the choice knowingly.
The other side has moved too, in a different direction. Its weight is now on Opik, an open-source LLM observability product, more than on classic experiment tracking.
How the two line up
| Area | Comet | Weights & Biases |
|---|---|---|
| Open source | Opik has an open-source version and a free tier | No open-source product claimed; sign-up and demo requests are the way in |
| LLM observability | Opik: traces, Diagnostics for silent errors, the Ollie debugging agent, test suites and 30+ LLM-judge metrics | Weave: traces, evaluations, a playground and monitors for AI applications |
| Classic ML tracking | Experiment Management in two lines of code, artifacts, model registry, production monitoring | Models: track, version and compare experiments; Bayesian, grid and random search with early stopping |
| Coding-agent usage | Tracks Claude Code and Codex usage across engineering teams | No equivalent described |
| Deployment and compliance | Not detailed on its site | Managed cloud, private cloud, on-premises and hybrid; ISO 27001, SOC 2 and HIPAA listed |
| Beyond tracking | Stays on evaluation, observability and optimisation | Also serverless fine-tuning, reinforcement learning, inference, sandboxes and notebooks |
Which half of your AI work matters more?
If you're shipping agents and LLM apps
Opik is the more detailed offer on paper. Beyond traces, it lists Diagnostics for silent errors, a debugging agent called Ollie, test suites and more than 30 LLM-judge metrics. The vendor says it turns trace data and eval results into code fixes, and points to more than 23,000 GitHub stars. Because there's an open-source version, your engineers can read the code and self-host. Weave covers the same core jobs, traces, evaluations, a playground and monitors, but it isn't open source.
If you're training and fine-tuning models
Here the CoreWeave-owned product has the deeper menu. Models handles versioning and comparing experiments, with Bayesian, grid and random hyperparameter search plus early stopping, and the vendor says it copes with hundreds of thousands of experiments without slowing training. Around it sit serverless fine-tuning, reinforcement learning, inference and notebooks. Comet's Experiment Management covers tracking, artifacts, a registry and production monitoring, and says you can start with two lines of code.
If your engineers use coding agents
One small extra stands out. Opik includes a feature that tracks Claude Code and Codex usage across engineering teams. If your developers lean on coding agents and you'd like visibility of that, only one vendor here describes it.
Three questions for your shortlist call
- Do you need on-premises, private cloud or hybrid deployment? Only one lists all of those.
- Does your security team ask for ISO 27001, SOC 2 or HIPAA? Those are listed on one side; ask the other directly.
- Would you rather your observability layer be open source and self-hostable? Only one offers that.
If you'd like more options on the training side, my list of MLOps tools worth shortlisting covers the wider field.
Verdict: match the platform to what you ship
Pick Comet if you want open-source, self-checkable LLM observability with a free tier and tracking of coding-agent usage. Pick Weights & Biases if you train or fine-tune models at scale, need listed compliance certifications or several deployment options, and you're comfortable with it being part of CoreWeave. Neither vendor's site crowns a winner, and there isn't one: they've each bet on a different half of AI development.
Five more platforms to shortlist
Langfuse
Best for: Self-hosted LLM tracing, with open-source observability, prompt management, evaluation and an AI gateway; its site says Langfuse joins ClickHouse.
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MLflow
Best for: Open-source tracking with no vendor lock-in, covering experiments, a model registry, tracing, evaluations and an AI gateway.
Arize
Best for: Agent observability and evaluation either as the managed Arize AX or the open-source Phoenix.
LangSmith
Best for: Teams building agents with LangChain or LangGraph, with tracing, evaluation and deployment plus Python, TypeScript, Go and Java SDKs.
ClearML
Best for: Infrastructure and platform teams managing GPUs, with a control plane, AI development center and GenAI app engine.
Do you make an MLOps or LLM observability platform? Tool makers can ask to be considered for a featured place on this page.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
Is Weights & Biases part of CoreWeave now?
Its Models page now redirects to coreweave.com, where it sits under a CoreWeave Forge menu alongside GPU cloud, inference and sandboxes.
Is Comet open source?
Opik, its LLM observability product, has an open-source version and a free tier. Its Experiment Management product is its classic ML tracking tool.
What's the difference between Opik and Weave?
Both trace and evaluate LLM apps. Opik adds Diagnostics, the Ollie debugging agent, test suites and 30+ LLM-judge metrics and is open source. Weave offers traces, evaluations, a playground and monitors.
Which one suits regulated industries?
The CoreWeave-owned platform lists ISO 27001, SOC 2 and HIPAA plus on-premises and hybrid deployment. The other doesn't detail certifications on its site, so ask.
Can either do hyperparameter sweeps?
Models offers Bayesian, grid and random search with early stopping. Experiment Management focuses on tracking, artifacts, a registry and production monitoring.
