Weights & Biases
Weights & Biases tracks ML experiments and traces LLM apps so teams can ship AI models faster
W&B still earns its keep for teams that want experiment tracking, a model registry, and LLM tracing under one roof instead of three vendors. The free and academic tiers are genuinely usable, and Pro at $60/month billed monthly now includes a 30-day free trial rather than a card-first commitment. The catch is that Pro carries an under-50-employees guideline and the platform now sits inside CoreWeave Forge, so teams wanting isolated agent sandboxes or per-token inference are buying into the CoreWeave stack, not just a tracker. Storage overages ($0.03/GB) and Weave ingestion ($0.10/MB) remain where costs quietly accumulate — model those before you standardize. Pass if you need a fully
Verified 7d ago · liveness 87/100 · cite: rightaichoice.com/tools/wandb
- ML teams that need centralized experiment tracking and shared dashboards across projects
- Researchers and academics running many model experiments — 200 GB free storage on the research tier
- Teams building LLM apps that need tracing, evaluation scorers, and production monitoring in one place
- Groups wanting serverless fine-tuning or inference for open-source models without provisioning GPUs
- Teams that will exceed Pro's under-50-employee guideline and don't want an Enterprise contract
- Organizations that need a fully open-source, self-managed stack with no vendor dependency
- High-volume LLM apps sensitive to metered Weave ingestion at $0.10/MB and storage at $0.03/GB
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Skip Weights & Biases if you need a fully open-source, self-managed stack, on-prem deployment without an Enterprise contract, or you already know you'll blow past the under-50-employee Pro guideline.
Going past your plan's included storage adds $0.03/GB, and 5 GB on Free runs out faster than most teams expect once artifacts pile up.
Free covers solo projects and small academic work; Pro at $60/month billed monthly fits teams under 50 employees that want unlimited teams, service accounts, and CI/CD automations. Enterprise is custom-priced for HIPAA, SSO, and single-tenant needs. Compared to a bare experiment tracker it costs more, but it replaces a separate LLM observability and inference vendor, which is where the math usually flips.
In short
Weights & Biases — Weights & Biases tracks ML experiments and traces LLM apps so teams can ship AI models faster. Best for ML teams that need centralized experiment tracking and shared dashboards across projects, Researchers and academics running many model experiments — 200 GB free storage on the research tier, Teams building LLM apps that need tracing, evaluation scorers, and production monitoring in one place. Free to start; paid plans from $60/mo.
What's new in Weights & Biases
Checked 7 days agoAcross the latest 1 update: 1 launch.
Viability Score
How well maintained and how widely used is Weights & Biases? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Auto-log hyperparameters, metrics, and outputs from training runs
- Hyperparameter sweeps for model optimization
- Model registry with lineage tracking
- Dataset versioning and artifact storage
- Collaborative dashboards and reports
- Weave LLM tracing of inputs, outputs, and metadata
- LLM evaluations with scorers and LLM-as-a-judge metrics
- Production monitoring with PII data redaction
- Serverless SFT for fine-tuning open models with nothing to provision
- Managed Serverless RL for reinforcement learning
- Serverless inference for open-source models at $5/mo per model
- Dedicated inference on GPUs you choose, managed for you
- Agent Lens observability that turns agent traces into fixes (public preview)
- CoreWeave Sandboxes for isolated agent and RL code execution (public preview)
- Skills for coding agents
About Weights & Biases
Weights & Biases (W&B) is an MLOps platform for teams that build and ship machine learning models and LLM applications. It auto-logs hyperparameters, metrics, and outputs from training runs, then layers on dataset versioning, a model registry with lineage tracking, and collaborative dashboards so results don't stay trapped on one engineer's laptop. Hyperparameter sweeps and CI/CD automations cover the optimization and release side of the lifecycle. For generative AI work, Weave handles LLM observability: tracing captures each inference's inputs, outputs, and metadata, evaluation scorers compare fine-tuning, RAG, and prompting recipes on accuracy, latency, and token usage, and production monitors track cost and health with PII data redaction. W&B now runs inside CoreWeave Forge: alongside training and tracking you get Serverless SFT for fine-tuning open models with nothing to provision, managed Serverless RL for reinforcement learning, Serverless Inference for open-source models billed per token at $5/mo per model, Dedicated Inference on GPUs you choose, Agent Lens (public preview) for turning agent traces into fixes, CoreWeave Sandboxes (public preview) for isolated agent and RL code execution, ARIA, a research agent that analyzes your runs, and reactive Python notebooks connected to those runs. The platform slots into standard ML stacks through PyTorch, TensorFlow, Keras, Hugging Face, and LLM frameworks such as LangChain and LlamaIndex. It fits teams that want one hub from research through production rather than stitching together a tracker, a registry, and a separate LLM observability tool.
Behind the Verdict
Weights & Biases started as the experiment tracker ML engineers actually wanted: run W&B.init() in a Python script and hyperparameters, metrics, system stats, and artifacts stream into a shared dashboard without anyone maintaining a logging server. That core is still the strongest part of the product — sweeps, the model registry with lineage tracking, dataset versioning, and collaborative reports cover the full research loop. For academic groups the research tier is unusually generous: unlimited projects, unlimited teams, and 200 GB of free cloud storage. The bigger change since the last refresh is the CoreWeave Forge framing. W&B is no longer sold as a standalone tracker; the pricing page now describes a single platform covering "Models, agents, training, and inference." Practically that adds Serverless SFT (fine-tune open models with nothing to provision), managed Serverless RL, Serverless Inference for open-source models on one API at $5/mo per model with free credits for a limited time, Dedicated Inference on GPUs you choose, Agent Lens for turning agent traces into fixes (public preview), CoreWeave Sandboxes for isolated agent and RL code execution (public preview), ARIA, a research agent that analyzes your runs, and reactive Python notebooks wired to your runs. If your team was already paying for a tracker plus a separate serving stack, this consolidation is real money and real operational simplification. Where it gets uncomfortable: Pro is priced at $60/month billed monthly and explicitly aimed at early-stage teams of fewer than 50 employees — exceed that guideline and you are required to move to CoreWeave Forge Enterprise at custom pricing. On-premises deployment is also Enterprise-only. The free tier caps at 5 model seats, 5 GB storage, and 1 GB/mo Weave ingestion, so a hobby project that suddenly logs a large eval set will notice. Overages run $0.03/GB for storage and $0.10/MB for Weave data ingestion, and at high LLM volume those ingestion meters move faster than the seat line. Enterprise adds HIPAA, SSO, automated user provisioning, customer-managed encryption keys, secure private connectivity, and custom roles with audit logs. Fit-wise: research teams, ML platform groups, and LLM app teams that want tracing, scorers, and production monitoring colocated with training get the most out of it. Teams that need a fully open-source self-managed stack, or that want on-prem without an Enterprise contract, should look at alternatives. Teams wanting agent sandboxes or inference in production today should note both features are still public preview. And because the docs and integrations pages were not reachable during this refresh, treat SDK and framework coverage as something to verify against your own stack before standardizing.
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Real-world workflow fit
Concrete scenarios for the personas Weights & Biases actually fits — and what changes day-one when you adopt it.
Starts a training run with the Python SDK, lets W&B auto-log hyperparameters and metrics, then uses sweeps to compare 40 configurations in the shared dashboard.
Outcome: The team sees the best configuration and its artifacts in one place instead of trading CSV files, and the model registry keeps lineage from dataset to checkpoint.
Wraps inference calls with Weave tracing, scores retrieval and answer quality with evaluation scorers, and turns on production monitoring with PII data redaction.
Outcome: Regressions surface against a baseline instead of in user complaints, and token usage and latency are tracked next to the traces that caused them.
Uses Serverless SFT to fine-tune an open model with nothing to provision, then serves it through Serverless Inference at $5/mo per model with free credits.
Outcome: They get a tuned model behind an OpenAI-compatible API without hiring infrastructure staff, and swap to Dedicated Inference on chosen GPUs when volume grows.
Use Cases
- Track and compare thousands of ML experiments in a central dashboard
- Optimize hyperparameters using sweeps
- Version control datasets and models with artifacts
- Evaluate and debug LLM applications with Weave tracing
- Fine-tune open models with Serverless SFT and manage RL post-training
- Serve open-source models on one API billed per token
- Monitor production AI applications with guardrails and evaluations
- Automate ML workflows with CI/CD integrations
Models Under the Hood
as of 2026-09-15
Limitations
- Free tier caps at 5 model seats, 5 GB storage, and 1 GB/mo Weave data ingestion; overages are $0.03/GB and $0.10/MB.
- Pro tier is positioned for teams under 50 employees — larger teams are required to move to CoreWeave Forge Enterprise at custom pricing.
- On-premises deployment requires Enterprise.
- Integration is via Python SDK, not plug-and-play.
- HIPAA, SSO, automated user provisioning, and customer-managed encryption keys are Enterprise-only.
- Agent Lens and CoreWeave Sandboxes are public preview, so don't plan production workloads on them yet.
as of 2026-09-30
Verification history
We have re-verified Weights & Biases 20 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Weights & Biases tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Individual developers and small academic projects validating whether tracking and LLM tracing fit their workflow before spending anything.
What this tier adds
Starting tier: $0/mo with up to 5 model seats, 5 GB storage, AI application evaluations, tracing and scorers, and community support.
Pro
Starts at $60/mo billed monthly
Ideal for
Early-stage teams under 50 employees that need shared dashboards, CI/CD automations, and alerts for a product already in motion.
What this tier adds
Adds unlimited teams, team-based access controls, service accounts, priority email and chat support, and starts at $60/month billed monthly with a 30-day free trial.
Academic Research
Free
Ideal for
University labs and research groups running many parallel experiments who can't pay per seat.
What this tier adds
Free access with unlimited projects, unlimited teams, and 200 GB of cloud storage for coordinating projects remotely.
Enterprise
Custom
Ideal for
Companies with security and compliance obligations — HIPAA, SSO, or a requirement for single-tenant deployment in a chosen region.
What this tier adds
Everything in Pro plus HIPAA-compliant option, secure private connectivity, customer-managed encryption key, SSO, automated user provisioning, and custom roles with audit logs.
Advanced Enterprise
Custom
Ideal for
Large organizations that need flexible deployment options beyond the standard Enterprise terms.
What this tier adds
Custom pricing with flexible deployment options, HIPAA-compliant option, secure private connectivity, customer-managed encryption key, SSO, automated user provisioning, and custom roles with audit logs.
Where the pricing makes sense
The company stage and team size where Weights & Biases's pricing actually pencils out — and where peers do it cheaper.
Free covers solo projects and small academic work; Pro at $60/month billed monthly fits teams under 50 employees that want unlimited teams, service accounts, and CI/CD automations. Enterprise is custom-priced for HIPAA, SSO, and single-tenant needs. Compared to a bare experiment tracker it costs more, but it replaces a separate LLM observability and inference vendor, which is where the math usually flips.
Setup time & first value
How long it actually takes to get something useful out of Weights & Biases — broken out by persona, not the marketing-page minute.
Individual developers: minutes — install the Python SDK, call wandb.init(), and the first run appears in the dashboard. Teams: plan on an afternoon to wire shared projects, service accounts, and CI/CD automations on Pro. Fine-tuning via Serverless SFT and inference deployment add a day or two of evaluation before anything goes in front of users.
Switching to or from Weights & Biases
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From TensorBoard: point the Python SDK at your training scripts and W&B logs the same scalars, plus artifacts and lineage TensorBoard doesn't track.
- →From MLflow: export runs and artifacts, then re-log them with the SDK so sweeps and the registry replace the local tracking server.
- →From a spreadsheets-and-CSV workflow: add the SDK to existing scripts and keep the shared dashboards as the single comparison surface.
- →From a separate LLM observability tool: route tracing and scorers through Weave so evaluations sit next to the training runs that produced the model.
- ↗To MLflow: export run history and artifacts, then stand up a self-managed tracking server for a fully open-source stack.
- ↗To a self-hosted tracker: re-log metrics from the Python SDK into your own store if vendor dependency is the blocker, accepting the loss of managed serving.
- ↗To a separate inference vendor: keep W&B for tracking and move serving off Serverless Inference, taking on the integration work yourself.
Integrations
Resources & Guides
- Guidedocs.wandb.ai
W&B Models
Use W&B Models for experiment tracking, dataset versioning, model management, and collaborative ML development.
- Resourcewandb.ai
Academy
Learn to train, fine-tune, and deploy LLMs and tackle real-world MLOps and LLMOps challenges with free Weights & Biases AI Academy courses.
- Resourcewandb.ai
For academic research
Discover W&B tools for AI experiments, data management, and effective collaboration. Free for students and researchers.
- Resourcewandb.ai
MLOps For Enterprise
Unify all of your AI projects, models, datasets, experiments, and pipelines on a single enterprise platform with Weights & Biases.
Tutorials & Learning
Learn Weights and Biases Now! Beginner Tutorial
Michael Hammer
Weights & Biases End-to-End Demo
Weights & Biases
🔥 Integrate Weights & Biases with PyTorch
Weights & Biases
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