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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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 |
Vendor refund policy
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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Delivery details
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.
Updated weekly
By Weights & Biases
By Scale AI
By Arize AI
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
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Positive reviews
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Negative reviews
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 Agnostic Integration
Support for seamless integration with any AI framework or existing architecture, deployable in cloud or on-premises infrastructure.
AI Governance and Lineage Tracking
Implement governance controls with comprehensive tracking of datasets, models, and metadata lineage, including human feedback collection and CI/CD for AI models.
Model Performance Evaluation
Human and machine-based evaluations leveraging AWS Bedrock to assess GenAI application performance, with options for subject matter expert evaluation or automated assessment methodologies.
Industry Benchmarking
Curated industry benchmarks enabling comparison of GenAI applications against industry peers 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 performance.
Flexible Deployment Architecture
Deployment options supporting both SaaS-based and customer-hosted AWS VPC deployment models.
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 and prevention
Standard contract
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Customer reviews
Biotechnology
ML Experiment Tracking, Forward Deployment, and Open-Weight Models Made Easy
Reviewed on Jul 28, 2026
Review provided by G2
What do you like best about the product?
Makes tracking training experiments and sharing training data with my team easy, with dashboards similar to Tensorboard and low performance overhead. Easy to get started with. Backs up data to the cloud and works from a remote cluster seamlessly. Plus offers support for purchasing cloud compute for LLM fine-tuning and FAAS.
What do you dislike about the product?
It doesn't display large quantities of data well, and it's difficult to use some of the more complex visualizations. As a place for publishing/using models, HuggingFace has a larger library and simpler API. Cloud compute pricing is competitive but higher than competitors.
What problems is the product solving and how is that benefiting you?
It helps us log ML training/evaluation data (though the Experiments and Reports features) remotely as I work on an HPC cluster. I can access the data anytime through the mobile app or website, which is convenient because we don't need a secure connection to the cluster. We can also save model weights/architectures and publish them online alongside our academic papers.
Dhruv P.
Solid MLOps platform for experiment tracking with great collaboration features
Reviewed on Jul 28, 2026
Review provided by G2
What do you like best about the product?
Excellent experiment tracking and visualization dashboard that makes it easy to compare model runs and parameters. Strong integrations with major ML frameworks and seamless team collaboration features. The API is intuitive and well-documented, making it straightforward to log metrics and artifacts.
What do you dislike about the product?
Pricing scales steeply with team size, which can be a barrier for smaller organizations. The learning curve for advanced features like custom dashboards and reports is moderate, and documentation could be more comprehensive for edge cases. Occasional UI/UX inconsistencies across different features.
What problems is the product solving and how is that benefiting you?
Helps organize and track ML experiments systematically, reducing time spent manually managing experiment logs and parameters. Enables better collaboration across teams by centralizing model run history and results. Improves reproducibility and debugging of models by maintaining complete audit trails. Accelerates model iteration cycles and provides visibility into which hyperparameters yield the best performance.
Jeni J.
A Must-Have Tool for Keeping ML Experiments Organized
Reviewed on Jul 28, 2026
Review provided by G2
What do you like best about the product?
I primarily use Weights & Biases to track and compare machine learning experiments, monitor training metrics in real time, and manage model versions. I really like how it solves the challenge of keeping experiments organized and reproducible, with everything logged automatically. What I like most about Weights & Biases is how effortless it makes experiment tracking and visualization. The interactive dashboards, real-time training metrics, hyperparameter comparison tools, and artifact management are great for understanding model performance, reproducing results, and collaborating with teammates without adding much overhead to the workflow. The initial setup was developer friendly too. the AI finetuning, monitoring was very good.
What do you dislike about the product?
One area that could be improved is the onboarding experience for new users, especially when exploring advanced features like Sweeps, Artifacts, and Reports. While the platform is very powerful, it can feel overwhelming at first, so more guided tutorials, in-app tips, and ready-to-use workflow templates would help users become productive much faster. I'd also like to see more flexible dashboard customization and filtering options for large projects with hundreds of experiment runs. Better cost and resource usage insights, along with faster loading times for very large experiment histories, would make the platform even more efficient for teams managing complex machine learning and LLM workflows.
What problems is the product solving and how is that benefiting you?
Weights & Biases solves organizing and reproducing ML experiments, automates tracking metrics, hyperparameters, and versions, and aids collaboration in AI projects. It helps me monitor training metrics, manage model versions, and track experiments effortlessly.
Muhammad O.
A Reliable Platform for Tracking Machine Learning Experiments
Reviewed on Jul 25, 2026
Review provided by G2
What do you like best about the product?
What I like most is how easy it is to get started and keep all my experiments organized in one place. The dashboard feels clean and intuitive, so it’s straightforward to track runs, compare results, and share progress with teammates. Overall, it helps me manage model development in a more structured way without ever feeling overly complicated.
What do you dislike about the product?
The platform offers a lot of features, so it can feel a bit overwhelming when you’re first getting started. It took me some time to figure out where everything was and how it all fit together, but after I spent a little time exploring, it became much easier to navigate.
What problems is the product solving and how is that benefiting you?
Weights & Biases helps me keep machine learning experiments organized by tracking runs, comparing results, and making it easier to see which changes actually improve a model. It saves time, supports collaboration, and makes it much simpler to reproduce past experiments rather than having to start from scratch.
Clarion I.
AI Tracing and Evaluation Made Easy
Reviewed on Jul 22, 2026
Review provided by G2
What do you like best about the product?
AI tracing feature for most AI models and evaluation.
What do you dislike about the product?
The free plan has limited features hence the need to upgrade to ensure one gets all features for deploying AI models.
What problems is the product solving and how is that benefiting you?
AI inference, tracing and evaluation