MLOps🔴Developer

Weights & Biases

Name: Weights & Biases
Brand: Weights & Biases
Availability: InStock

End-to-end MLOps and AI developer platform — Models (experiment tracking, sweeps, model registry) plus Weave (LLM/agent observability and evals) — used by frontier labs and enterprise ML teams.

Starting atFree

Visit Weights & Biases →

💡

In Plain English

End-to-end MLOps and AI developer platform — Models (experiment tracking, sweeps, model registry) plus Weave (LLM/agent observability and evals) — used by frontier labs and enterprise ML teams.

Overview

Weights & Biases (W&B) is the canonical experiment-tracking and MLOps platform for serious model builders. The classic product, W&B Models, logs every training run with hyperparameters, metrics, system stats, code state, dataset versions, and artifacts; powers hyperparameter sweeps; and hosts a Model Registry with stage transitions, lineage, and CI hooks for promotion to production. The newer pillar, W&B Weave, targets LLM and agent builders specifically: it traces every prompt, tool call, and chain step, attaches cost and latency, runs scored evaluations (LLM-as-judge, programmatic, or human), and feeds the same data into Models for fine-tuning datasets. Around those, W&B ships Reports (shareable notebook-style analyses), Launch (queueing jobs onto Slurm, Kubernetes, or cloud), and W&B Inference / Serverless Endpoints for hosting open-weight models. The company is now part of CoreWeave, which has tightened the integration with CoreWeave's GPU cloud while keeping W&B usable on any other compute backend.

🦞

Using with OpenClaw

▼

Monitor OpenClaw agent performance and usage through Weights & Biases integration. Track costs, latency, and success rates.

Use Case Example:

Gain insights into your OpenClaw agent's behavior and optimize performance using Weights & Biases's analytics and monitoring capabilities.

Learn about OpenClaw →

🎨

Vibe Coding Friendly?

▼

Difficulty:intermediate

Analytics platform requiring some technical understanding but good API documentation.

Learn about Vibe Coding →

Was this helpful?

Editorial Review

Weights & Biases brings its proven ML experiment tracking experience to LLM observability with W&B Weave. The platform excels at experiment comparison, artifact versioning, and collaborative workflows for ML teams. LLM-specific features like prompt tracing and evaluation are newer and less mature than dedicated LLM tools. Best for teams already invested in the W&B ecosystem who want to extend it to LLM development rather than adopt a separate tool.

Key Features

•Workflow Runtime
•Tool and API Connectivity
•State and Context Handling
•Evaluation and Quality Controls
•Observability
•Security and Governance

Pricing Plans

Free / Personal

Teams

Per-seat paid plan

Enterprise

Custom

See Full Pricing →Free vs Paid →Is it worth it? →

Ready to get started with Weights & Biases?

View Pricing Options →

Getting Started with Weights & Biases

1Sign up for free W&B account at wandb.ai and install the Python SDK: pip install wandb
2Import wandb in your code and login with wandb.login() to authenticate your session
3For LLM work, initialize a Weave project and start tracing with weave.init() in your application
4Log experiments using wandb.log() for metrics and wandb.Table() for structured data
5Create evaluation datasets and use Weave's evaluation framework to score model outputs

Ready to start? Try Weights & Biases →

Best Use Cases

🎯

ML research teams training their own models who need rigorous experiment tracking

⚡

Enterprise ML platforms standardizing on a model registry and CI for model promotion

🔧

LLM/agent teams that want unified eval + observability via Weave alongside training

🚀

Hyperparameter sweeps across large compute clusters

Integration Ecosystem

9 integrations

Weights & Biases works with these platforms and services:

🧠 LLM Providers

OpenAIAnthropicGoogle

☁️ Cloud Platforms

AWSGCPAzure

💾 Storage

S3GCS

🔗 Other

GitHub

View full Integration Matrix →

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Weights & Biases doesn't handle well:

⚠LLM-specific features are newer and evolving — dedicated LLM tools often ship improvements faster
⚠The platform has a significant learning curve for teams that only need LLM observability
⚠Self-hosting (W&B Server) requires substantial infrastructure and is more complex than lighter alternatives
⚠Real-time production alerting for LLM applications is less mature than W&B's core offline experiment capabilities

Pros & Cons

✓ Pros

✓Best-in-class experiment-tracking UI — researchers genuinely prefer it
✓Weave bridges classical ML and LLM observability in one platform
✓Mature integrations with virtually every major training framework
✓Reports make collaboration and asynchronous review of experiments easy
✓CoreWeave acquisition gives a clear long-term home and GPU compute story

✗ Cons

✗Paid tiers can get expensive at team scale relative to self-hosted MLflow
✗SaaS-first posture; on-prem requires Enterprise tier
✗Weave is newer and still catching up to LangSmith on some LangChain-specific niceties
✗Storage of large artifacts (datasets, checkpoints) can become a hidden cost driver
✗Some teams find the breadth (Models + Weave + Launch + Inference) overwhelming to adopt all at once

Frequently Asked Questions

Is W&B Weave a separate product from Weights & Biases?+

Weave is a product layer within W&B focused on LLM application development. It uses the same W&B account, workspace, and infrastructure. Think of it as the LLM-specific interface built on top of W&B's core experiment tracking capabilities.

How does W&B compare to Langfuse or Braintrust for LLM observability?+

W&B is broader (covering traditional ML + LLM) while Langfuse and Braintrust are deeper on LLM-specific features. W&B excels at experiment comparison and team reporting. If you only do LLM work, dedicated tools are more streamlined. If you do both ML and LLM, W&B unifies everything.

Can W&B handle production monitoring for LLM applications?+

Yes, through Weave's tracing and W&B's monitoring features. However, W&B's roots are in offline experiment tracking, so real-time production alerting is less mature than dedicated monitoring tools. Many teams use W&B for evaluation and a separate tool for production monitoring.

What does W&B cost for a team of 10 engineers?+

The free tier supports small teams with limited storage and compute. The Team plan starts around $50/user/month. For 10 engineers, expect $500-1,000/month depending on usage. Enterprise pricing is custom and includes SSO, audit logs, and dedicated support.

🔒 Security & Compliance

🛡️ SOC2 Compliant

✅

SOC2

Yes

✅

GDPR

Yes

—

HIPAA

Unknown

✅

SSO

Yes

🔀

Self-Hosted

Hybrid

✅

On-Prem

Yes

✅

RBAC

Yes

✅

Audit Log

Yes

✅

API Key Auth

Yes

❌

Open Source

✅

Encryption at Rest

Yes

✅

Encryption in Transit

Yes

Data Retention: configurable

Data Residency: US, EU

📋 Privacy Policy →🛡️ Security Page →

🦞

New to AI tools?

Read practical guides for choosing and using AI tools

Read Guides →

Get updates on Weights & Biases and 370+ other AI tools

Weekly insights on the latest AI tools, features, and trends delivered to your inbox.

What's New in 2026

•Launched W&B Weave 2.0 with native LLM evaluation framework and automated quality monitoring

•Added support for tracing multi-agent systems with agent-to-agent communication visualization

•New model registry integration allowing direct comparison between LLM versions using production trace data

Alternatives to Weights & Biases

CrewAI

AI Agents

Open-source Python framework for orchestrating role-playing, autonomous AI agents that collaborate as a 'crew' to complete complex tasks.

Microsoft AutoGen

Multi-Agent Builders

Microsoft's open-source framework for building multi-agent AI systems with asynchronous, event-driven architecture.

LangGraph

AI agent framework

LangGraph is LangChain's open-source framework for building stateful, durable, multi-agent workflows in Python and JavaScript with graph-based control flow.

Microsoft Semantic Kernel

AI Agent Builders

SDK for integrating cutting-edge LLM technology into applications, with support for building AI agents and connecting model capabilities into existing app workflows.

View All Alternatives & Detailed Comparison →

User Reviews

No reviews yet. Be the first to share your experience!

Quick Info

Try Weights & Biases Today

Get started with Weights & Biases and see if it's the right fit for your needs.

Get Started →

Need help choosing the right AI stack?

Take our 60-second quiz to get personalized tool recommendations

Find Your Perfect AI Stack →

Want a faster launch?

Explore 20 ready-to-deploy AI agent templates for sales, support, dev, research, and operations.

Browse Agent Templates →

More about Weights & Biases

Pricing Review Alternatives Free vs Paid Pros & Cons Worth It?Tutorial

Overview

Editorial Review

Getting Started with Weights & Biases

1Sign up for free W&B account at wandb.ai and install the Python SDK: pip install wandb

2Import wandb in your code and login with wandb.login() to authenticate your session

3For LLM work, initialize a Weave project and start tracing with weave.init() in your application

4Log experiments using wandb.log() for metrics and wandb.Table() for structured data

5Create evaluation datasets and use Weave's evaluation framework to score model outputs

Best Use Cases

🎯

ML research teams training their own models who need rigorous experiment tracking

⚡

Enterprise ML platforms standardizing on a model registry and CI for model promotion

🔧

LLM/agent teams that want unified eval + observability via Weave alongside training

🚀

Hyperparameter sweeps across large compute clusters

Limitations & What It Can't Do

We believe in transparent reviews. Here's what Weights & Biases doesn't handle well:

⚠LLM-specific features are newer and evolving — dedicated LLM tools often ship improvements faster

⚠The platform has a significant learning curve for teams that only need LLM observability

⚠Self-hosting (W&B Server) requires substantial infrastructure and is more complex than lighter alternatives

⚠Real-time production alerting for LLM applications is less mature than W&B's core offline experiment capabilities

Pros & Cons

✓ Pros

✓Best-in-class experiment-tracking UI — researchers genuinely prefer it
✓Weave bridges classical ML and LLM observability in one platform
✓Mature integrations with virtually every major training framework
✓Reports make collaboration and asynchronous review of experiments easy
✓CoreWeave acquisition gives a clear long-term home and GPU compute story

✗ Cons

✗Paid tiers can get expensive at team scale relative to self-hosted MLflow
✗SaaS-first posture; on-prem requires Enterprise tier
✗Weave is newer and still catching up to LangSmith on some LangChain-specific niceties
✗Storage of large artifacts (datasets, checkpoints) can become a hidden cost driver
✗Some teams find the breadth (Models + Weave + Launch + Inference) overwhelming to adopt all at once

Frequently Asked Questions

Is W&B Weave a separate product from Weights & Biases?+

How does W&B compare to Langfuse or Braintrust for LLM observability?+

Can W&B handle production monitoring for LLM applications?+

What does W&B cost for a team of 10 engineers?+

What's New in 2026

•Launched W&B Weave 2.0 with native LLM evaluation framework and automated quality monitoring

•Added support for tracing multi-agent systems with agent-to-agent communication visualization

•New model registry integration allowing direct comparison between LLM versions using production trace data