Sold by: Weights & Biases
Deployed on AWS
Weights & Biases provides AI developers with the tools needed to build models faster, fine-tune LLMs, and develop GenAI applications with confidence for enterprises of all sizes in any vertical.
4.5
Overview
Weights & Biases provides AI developers with the tools needed to build models faster, fine-tune LLMs, and develop GenAI applications with confidence for enterprises of all sizes in any vertical. The company is trusted by over 1,300 customers including more than 30 foundation model builders.
We provide a comprehensive developer platform to productionize AI. W&B Weave helps developers evaluate, monitor, and iterate to deliver LLM-powered applications, and W&B Models enables ML engineers to train, fine-tune, and manage AI models. Weights & Biases brings together all the developer tools you need for AI into a single, unified platform, delivering enterprise-level performance, scaling, governance, and security.
Weights & Biases helps AI teams of all sizes:
- Build system of record for AI
- Run rigorous evaluations of AI applications
- Debug AI applications pre-production and monitor them in production
- Track experiments for reproducibility and governance
- Track lineage for datasets, models, and metadata
- Collect human feedback and annotations
- Create training datasets leveraging production traces
- Share insights interactively with collaborators
- Implement CI/CD for AI models
Highlights
- W&B was created by AI engineers for AI engineers. Our mission is to build the best tools for Artificial Intelligence.
- Weights & Biases is trusted by more than 1M AI practitioners and used by AI leaders including at OpenAI, Cohere, Toyota Research Institute, and others across industries.
- Weights & Biases works seamlessly with any AI framework or existing architecture, whether in the cloud or on your own infrastructure.
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Deployed on AWS
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Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Annual Single User License for W&B Models | Single user license for 12 months of W&B Models | $4,800.00 |
Annual Commitment for W&B Weave, 10GB | Pricing is dependent on estimated usage of the platform. | $25,000.00 |
Dimension | Description | Cost/unit |
|---|---|---|
overage | Storage overage | $0.001 |
Dimensions summary
This listing bills through an annual contract with three separate dimensions you combine based on need. You buy Annual Single User Licenses for W&B Models, priced per user for a 12-month term, to train and manage AI models. You add an Annual Commitment for W&B Weave in 10GB units to develop GenAI applications, with pricing tied to your estimated platform usage. Storage overage covers usage beyond your committed amount and is billed on consumption. Model seats prorate if added mid-term. Storage and Weave usage are measured over time and billed based on what you use.
Top-of-mind questions for buyers
Ingested bytes are the data received, processed, and stored on your behalf. This includes trace metadata and model inputs and outputs you log to Weave. It excludes communication overhead like HTTP headers. Bytes count as ingested once, at the time they are received and stored.
Storage includes artifacts and data logged to runs. W&B measures your usage over the last 30 days using GB-days, then divides by the number of days to get an average. Your usage is rounded to the nearest megabyte at the end of the period.
Model seat licenses are invoiced annually upfront. Model seats prorate if added mid-term, though removed seats do not earn credits. Weave data ingestion and storage overage are billed monthly in arrears based on the usage you actually consume during each month.
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Non-Refundable. Unless otherwise expressly provided for in this agreement or the applicable Order Form, (i) all fees are based on services purchased and not on actual use; and (ii) all fees paid under this agreement are non-refundable.
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Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
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AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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By Weights & Biases
By Scale AI
By Arize AI
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
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Positive reviews
Mixed reviews
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AI generated from product descriptions
Experiment Tracking and Reproducibility
Track experiments with lineage for datasets, models, and metadata to enable reproducibility and governance of AI development workflows.
LLM Fine-tuning and Model Management
Fine-tune large language models and manage AI models through integrated tools for training, versioning, and lifecycle management.
LLM Application Evaluation and Monitoring
Evaluate, monitor, and iterate on LLM-powered applications with tools for pre-production debugging and production monitoring.
Framework and Infrastructure Agnostic Integration
Integrate seamlessly with any AI framework or existing architecture, supporting both cloud-based and on-premises infrastructure deployments.
AI Governance and CI/CD Implementation
Implement continuous integration and continuous deployment pipelines for AI models with enterprise-level governance, security, and scaling capabilities.
Model Performance Evaluation
Human and machine-based evaluations leveraging AWS Bedrock to assess GenAI application accuracy and performance, with options for subject matter expert review or automated evaluation methodologies
Industry Benchmarking
Curated industry benchmarks enabling comparison of GenAI applications against peer implementations and use cases with regularly refreshed standards
Vulnerability Assessment
Red teaming capabilities to identify and assess security vulnerabilities and potential failure modes in GenAI applications
Data Preparation and Optimization
Data processing capabilities including chunking, embedding generation, and RAG knowledge base construction for improved retrieval and model ingestion
Flexible Deployment Architecture
Deployment options supporting both SaaS-based and customer-hosted AWS VPC environments
Agent and Application Observability
Full visibility into AI agent behavior through tree-structured traces capturing user inputs, routing logic, tool calls, memory access, and model outputs with native support for Amazon Bedrock Agents and open-source frameworks
Prompt Optimization and Testing
Prompt IDE environment enabling design, testing, and comparison of prompt versions with live inputs, outputs, and integrated evaluation results for iterative improvement
LLM and Agent Evaluation
Offline and online LLM-as-a-Judge evaluations assessing accuracy, tool-calling, planning, and goal achievement across agent workflows
Closed-Loop Improvement Workflows
Self-improving agent capabilities combining trace analysis, evaluation feedback, and golden datasets for continuous iteration and performance enhancement
Real-Time Monitoring and Alerting
Custom metrics definition and monitoring of latency, token usage, and failures with alert configuration for production issue detection
Standard contract
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Customer reviews
Rohan J.
Easy Machine Learning Experiment Tracking and Performance Monitoring
Reviewed on Oct 09, 2026
Review provided by G2
What do you like best about the product?
What I like best about Weights & Biases is its ability to track machine learning experiments, visualize results, and compare different model versions in one place. It makes it easier to monitor performance, identify problems, and collaborate with team members. The organized dashboard and clear metrics help improve productivity and make AI development more efficient.
What do you dislike about the product?
One thing I dislike about Weights & Biases is that it can be difficult for beginners to understand all its features at first. Setting up experiment tracking and configuring integrations may require some technical knowledge. The interface can also feel overwhelming when managing multiple experiments, and some advanced features may not be necessary for smaller projects.
What problems is the product solving and how is that benefiting you?
Weights & Biases helps solve the problem of tracking and managing machine learning experiments. It allows me to compare model performance, monitor important metrics, organize results, and identify issues more easily. This saves time, improves workflow organization, and helps me understand which experiments perform best. It also makes collaboration and tracking progress throughout AI development more convenient.
Achyutam V.
Intuitive UI and Powerful Experiment Tracking for Systematic, Reproducible Research
Reviewed on Oct 08, 2026
Review provided by G2
What do you like best about the product?
I find the UI simple and intuitive, especially when I’m analyzing multiple experiments at the same time. The ability to integrate with various ML libraries fits my workflow perfectly. So far, I haven’t run into any issues monitoring my runs, metrics, parameters, or overall model performance. It also saves me time by automatically organizing my experiment data, which means I don’t have to rely on separate spreadsheets to keep everything straight. The value it provides for research feels really high, largely because of its strong focus on reproducibility. Onboarding was fairly straightforward, too, and I was able to find all the documentation I needed. I’ve also found its AI features extremely useful for comparing models and digging deeper into experiment results. Overall, W&B makes my research workflow more systematic and efficient.
What do you dislike about the product?
One difficulty I’ve faced is that the interface can become confusing when you start using the more advanced functions. Integration and experiment configuration can also take some time to set up, especially for a novice. For larger-scale experiments with frequent logging, there can end up being too much data to sift through, and the dashboards may feel overwhelming. I also feel the price could become an issue for individual researchers or small academic research groups. The initial learning curve seems a bit steeper than I expected from a beginner’s point of view. The documentation does help, but sometimes it still takes extra effort to track down the specific information you need. Some AI-related aspects could also be made more intuitive and actionable. Overall, W&B is an effective tool, but for academic research I would benefit from something more convenient and easier to navigate.
What problems is the product solving and how is that benefiting you?
With the help of W&B, I can track all my research experiments, model versions, parameters, and results in one place. It lets me compare and analyze different model runs without having to manually piece together results from separate files or spreadsheets. The dashboards make it easy to see which model versions are improving key metrics like accuracy and F1-score. W&B integrations also speed up setup when I’m working with Python-based machine learning platforms and workflows. With consistent experiment logging, I don’t lose important data even when I’m running many experiments and switching between models. I can always go back to earlier runs and understand which settings led to specific outcomes, which is especially useful when I need to reproduce and analyze experiments for research papers. Overall, W&B saves me a lot of time and makes my research process more structured.
Nikhil L.
Clear Dashboards and Easy Integrations for Streamlined ML Experiment Tracking
Reviewed on Oct 08, 2026
Review provided by G2
What do you like best about the product?
I like Weights & Biases because it makes experiment tracking, model versioning, and collaboration simple and well organized. The dashboards are clear, and the integrations are easy to set up, which helps teams monitor ML experiments and improve models efficiently.
What do you dislike about the product?
The biggest drawback is that it can feel a bit complex for beginners, and some of the more advanced features may be too expensive for smaller teams or individual users.
What problems is the product solving and how is that benefiting you?
Weights & Biases addresses the challenge of managing and tracking machine learning experiments. It lets me compare results across runs, monitor model performance, keep everything organized, and collaborate with others. Overall, it saves me time and makes the development process more efficient.
Nilesh K.
User-Friendly Experiment Tracking That Streamlines ML Workflows
Reviewed on Oct 07, 2026
Review provided by G2
What do you like best about the product?
I like Weights & Biases because it has a user-friendly interface, efficient experiment tracking, real-time performance visualization, and makes collaboration easy. Overall, it simplifies my machine learning workflow and helps me manage projects more effectively.
What do you dislike about the product?
The learning curve can feel a bit steep for new users, and some of the more advanced features may be complex to set up, navigate, and use at first.
What problems is the product solving and how is that benefiting you?
It addresses the challenge of tracking, comparing, and managing machine learning experiments all in one place. That saves time, supports better collaboration, and makes it easier to understand model performance while identifying the best results.
Zaid Z.
User-Friendly Dashboard and Automated Sweeps Deliver Huge ROI
Reviewed on Oct 06, 2026
Review provided by G2
What do you like best about the product?
The real-time experiment dashboard is very user-friendly and easy to navigate, making it simple to compare complex loss curves, validation statistics, and confusion matrices. Hyperparameter sweeps are automated and work like an intelligent assistant, cutting down on guesswork by testing different configurations to identify the best ones. Getting started took virtually no time thanks to the quick-start guides and the documentation. Live GPU and system resource information is delivered with very little latency, so divergences or training issues can be spotted immediately. Considering how much development effort this tool saves, the ROI feels huge.
What do you dislike about the product?
When you’re working on very large projects with many historical training sessions, the interactive charts and run-comparison tables can take a while to load, especially after you apply several filters. Also, more advanced tasks—like configuring an artifact pipeline—may require digging through community discussions, since getting timely help from the support team can take too long if you’re not on an enterprise-level subscription. Lastly, the pricing tiers can jump quite sharply as your storage and computing needs grow.
What problems is the product solving and how is that benefiting you?
Before I started using it, logging deep learning experiments and hyperparameter sweeps meant juggling manual text logs and local spreadsheets across multiple computers. Weights & Biases keeps all the key artifacts in one place—model outputs, dataset versions, and training sessions—and it still renders everything smoothly even under heavy logging. It also saves me the constant frustration of trying to reproduce the best-performing model from weeks ago, because the full history is captured automatically. With the sweep algorithms handling optimization of the model architecture, I can focus on feature engineering and pipeline design instead. New team members can quickly see where we are in the project without me having to explain everything from scratch, which helps us avoid wasting GPU compute and the money that goes with it.