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 |
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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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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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
Customer service
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Positive reviews
Mixed reviews
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
Oil & Energy
Clear ML Experiment Tracking with Easy Integration and Reliable Versioning
Reviewed on Aug 11, 2026
Review provided by G2
What do you like best about the product?
They are experiment tracking dashboard makes it completely easier for us to compare training runs, metrics and other hyperparameters in one place. Integration requires only a few lines of code and works well with popular ml frameworks. The visualisations are very clear and particularly helpful when debugging model performance. Artefact and other registry which also provide reliable versioning for datasets and models. Overall, I would say it creates a strong shade workspace for ML teams.
What do you dislike about the product?
This platform sometimes uh being overwhelming initially because it includes many features, dashboards and other configuration options. Organising projects also becoming bit difficult if the team does not establish consistent naming conventions early. Their interface can also Occasionally feels bit slower when loading projects contain a large number of runs. And some of their advanced collaboration along with the governance and deployment capabilities, are also limited to the paid plans. Pricing may become expensive for growing teams with extensive usage.
What problems is the product solving and how is that benefiting you?
This platform replaces manual spreadsheets and scattered logs with the centralized record of every machine learning experiment. It also helps us to reproduce previous results by capturing metrics, parameters, system usage and other data set models. Comparing runs allows us to identify the best-performing configuration much faster. And our teams can review progress and share findings right on the spot without repeatedly exchanging files. This overall reduced the experimentation time and improved the collaboration throughout the model development life cycle.
Atharva S.
Streamlined ML Experiment Tracking with Rich Visualizations and Team Collaboration
Reviewed on Aug 05, 2026
Review provided by G2
What do you like best about the product?
What I like best about Weights & Biases is how it simplifies machine learning experiment tracking, model management, and collaboration through an intuitive and well-designed platform. It makes it easy to monitor training runs, compare experiments, visualize metrics, and organize models in one place, which significantly improves the development workflow. I also appreciate its rich visualizations, seamless integration with popular ML frameworks like PyTorch and TensorFlow, and strong collaboration features for teams. Overall, Weights & Biases accelerates model development, improves experiment reproducibility, and makes managing machine learning projects much more efficient.
What do you dislike about the product?
One area where Weights & Biases could improve is offering more advanced customization for dashboards, reporting, and experiment organization to better support very large machine learning projects. While the platform is feature-rich, new users may experience a learning curve when exploring advanced capabilities such as artifact management and workflow automation. I'd also like to see broader integrations with additional MLOps and enterprise platforms, along with more flexible access controls and reporting options. Overall, the experience has been very positive, but greater customization, expanded integrations, and enhanced enterprise features would make Weights & Biases even more valuable.
What problems is the product solving and how is that benefiting you?
Weights & Biases solves the challenge of managing machine learning experiments by providing a centralized platform for experiment tracking, model evaluation, dataset versioning, and collaboration. Instead of manually recording training metrics and comparing results across different runs, it automatically logs parameters, visualizes performance, and organizes experiments in a structured way. This improves reproducibility, accelerates model iteration, simplifies collaboration among data science teams, and reduces the time spent on experiment management. As a result, it has streamlined the machine learning development workflow, increased productivity, and made it much easier to build, compare, and deploy high-performing models.
Muhammed A.
Essential ML Experiment Tracking with Real-Time Metrics and Team Collaboration
Reviewed on Jul 31, 2026
Review provided by G2
What do you like best about the product?
Weights & Biases has become an essential platform for managing machine learning experiments, model training, and performance tracking. The interface makes it easy to compare runs, visualize metrics in real time, and collaborate across teams, while integrations with popular ML frameworks simplify adoption. Experiment tracking, artifact versioning, and reproducibility features significantly reduce manual work, helping teams iterate faster, improve model quality, and maintain organized AI development workflows.
What do you dislike about the product?
Weights & Biases offers a comprehensive feature set, but new users may face a learning curve when configuring advanced experiment tracking, reports, and team workflows. Large projects with thousands of experiment runs can sometimes make dashboards feel cluttered, and premium features may be costly for smaller teams. I would also like to see more customization options for visualizations and reporting, along with additional native integrations for enterprise MLOps environments.
What problems is the product solving and how is that benefiting you?
Before using Weights & Biases, tracking machine learning experiments, comparing model performance, and managing training artifacts across multiple projects was time-consuming and difficult to reproduce. The platform centralized experiment tracking, visualization, model versioning, and collaboration in a single workspace, making it much easier to monitor progress and identify the best-performing models. This has reduced manual effort, improved reproducibility, accelerated model development cycles, and enabled the team to make faster, data-driven decisions throughout the ML lifecycle.
Automotive
Automatic Metrics Tracking, but Overall Experience Needs Improvement
Reviewed on Jul 30, 2026
Review provided by G2
What do you like best about the product?
Automatically records metrics, code versions, making results better
What do you dislike about the product?
Projects can get cluttered over time, and that can feel overwhelming.
What problems is the product solving and how is that benefiting you?
Keeps records and training for every run.
Helps identify changes
Helps identify changes
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.