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.
4.7
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.
Details
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Delivery method
Deployed on AWS
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Introducing multi-product solutions
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Features and programs
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 |
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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Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
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Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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 infrastructure support
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 Dataloop AI/GenAI development platform
By Scale AI
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
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.
Generative AI and RAG Pipeline Deployment
Deploy RAG pipelines for GenAI solutions including summarization, chatbots, and data preparation with support for RLHF and RLAIF incorporation.
Unstructured Data Management and Analysis
Explore and analyze unstructured data from diverse sources with automated preprocessing, embeddings generation, and similarity identification capabilities.
Model Versioning and Experimentation
Version, experiment, compare, and fine-tune AI models with production deployment capabilities without requiring external tool integration.
Workflow Orchestration with Drag-and-Drop Interface
Orchestrate data, models, applications, and human feedback using a drag-and-drop interface or Python SDK with pre-created pipeline templates.
Enterprise Security and Compliance
Implement GDPR, ISO 27001, ISO 27701, and SOC 2 Type II compliance with RBAC, SSO, 2FA, AES-256 encryption, and granular audit trail capabilities.
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.
Standard contract
No
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Customer reviews
reviewer2842122
Experiment tracking has streamlined hyperparameter search and collaboration in daily model work
Reviewed on May 16, 2026
Review provided by PeerSpot
What is our primary use case?
My main use case for Weights & Biases is experiment tracking.
What is most valuable?
Weights & Biases is a very handy library when I want to track experiments and find the optimal parameters for training models or determine which model is the best when experimenting with multiple models. This library is very useful.
When I need to find the optimal hyperparameter, I can use Weights & Biases to track different hyperparameters for training a model. Weights & Biases offers experiment tracking, hyperparameter optimization, and model artifact versioning.
I rely the most on hyperparameter optimization in my daily work because it is very useful for training models.
Weights & Biases is very useful when I need to review the past and see which model performed better or which parameters were the best. It provides good versioning and history, which is a feature I use frequently.
I think it provides easier collaboration. Even if I want to share my model with someone, they can see the metrics that I am getting in that model.
What needs improvement?
I think there are not enough tutorials or training available for Weights & Biases. That would be much more beneficial. A better integration with cloud providers would also help.
For how long have I used the solution?
I have been using Weights & Biases for three years.
What do I think about the stability of the solution?
Weights & Biases is stable in my experience.
What do I think about the scalability of the solution?
Weights & Biases is scalable.
Which other solutions did I evaluate?
I evaluated MLflow and TensorBoard before choosing Weights & Biases.
What other advice do I have?
I chose a rating of 8 out of 10 for Weights & Biases because it is easy to use and a very good library for machine learning engineers. Weights & Biases is a very useful library if you want to track training experiments.
reviewer2842017
Experiment tracking has improved collaboration and has reduced time spent debugging workflows
Reviewed on May 16, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for Weights & Biases revolves around experiment tracking and model evaluation.
In my previous job, I used Weights & Biases for experiment tracking, model evaluation, visibility, and collaboration between the different teams that we had at the company, mostly in product and engineering, while we were working on AI-driven workflows and AI features.
How has it helped my organization?
Weights & Biases has positively impacted my organization by improving operational efficiency and functional visibility.
After adopting Weights & Biases, I noticed positive outcomes such as reduced time spent on debugging and rerunning experiments, allowing teams to quickly identify what configuration produced the best results.
What is most valuable?
One of the best features Weights & Biases offers is hyperparameter optimization because it lets us run large-scale hyperparameter searches using random or grid search.
Hyperparameter optimization from Weights & Biases helped my team significantly by reducing the manual trial and error and improving model performance much faster.
What needs improvement?
For improvement, I would say cost and scalability could be addressed, and visibility could be improved further on AI workflows.
For how long have I used the solution?
I have used Weights & Biases for around a year in my last job.
What do I think about the stability of the solution?
Weights & Biases is stable.
What do I think about the scalability of the solution?
Scalability of Weights & Biases has not become a bottleneck in our training workflow.
How are customer service and support?
My experience with customer support for Weights & Biases was good.
Which solution did I use previously and why did I switch?
Weights & Biases was the first solution I used for experiment tracking and model management, although we were considering Arise AI or BrainTrust at some point.
Before choosing Weights & Biases, I did evaluate other options, including Arise AI and BrainTrust.
How was the initial setup?
Weights & Biases was already in our system and we did not purchase it through AWS Marketplace .
What about the implementation team?
I had a good experience with pricing, setup cost, and licensing, and everything was smooth.
What was our ROI?
While I cannot share specific metrics due to confidentiality, the return on investment from using Weights & Biases has been really good.
What's my experience with pricing, setup cost, and licensing?
I had a good experience with pricing, setup cost, and licensing, and everything was smooth.
What other advice do I have?
My advice for others looking into using Weights & Biases is that they should use it and experience the benefits of this product. I would rate this review as a 9 out of 10.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Amazon Web Services (AWS)
Mamoon K.
Effortless Training Run Tracking Made Simple
Reviewed on Dec 04, 2025
Review provided by G2
What do you like best about the product?
It helps track training runs easily. I can see all the logged runs in one place without manually checking
What do you dislike about the product?
There should be an easy way to discard non useful runs.
What problems is the product solving and how is that benefiting you?
The problem of manually checking training runs again and again in the code makes it tedious. For people learning code like me Weights and Biases presents a unique alternative
Research
Very useful quite powerful tool
Reviewed on Mar 24, 2025
Review provided by G2
What do you like best about the product?
Easy of use, ease of implementation, the possibility to gather all my results, ease of sharing results with teammates, It can compare a lot of data interactively which in other cases could be hard to implement
What do you dislike about the product?
It is online approach which is both strong and weak side, sometimes servers are bit laggy.
What problems is the product solving and how is that benefiting you?
It makes easy for me to store and analyze experiments results, which in case of using own implementation approach using matplotlib for example would require quite a lot of work.
Amir Masoud N.
Seamless Integration and Reliable Support: A Daily Essential for Machine Learning
Reviewed on Mar 08, 2025
Review provided by G2
What do you like best about the product?
It is highly and well integrated with libraries I am using like PyTorch Lightning. Many times I have set that up because it was very easy, and after a while, it has happened that I lost part of my results, and W&B helped me to easily recover them through the logs, which without it, I probably wouldn't have. The next and very important feature to me is that I can use many different machines and servers at the same time and without being worried about gathering all results together, then using tools like TensorBoard, having them online without any effort(most of the time saves me when I am using supercomputers). It is part of my daily tools, and when I am teaching students machine learning, in very early sessions after teaching them visualization, I will have them use W&B to repeat whatever they have learned so far. I have never had any problem with W&B, but I have heard from one of my friends, whom I recommended he use W&B, that customer support is very fast and experienced.
What do you dislike about the product?
Sometimes it is bothering me when I am looking for very basic functionality of W&B and it doesn't provided good documentation for that.
What problems is the product solving and how is that benefiting you?
Monitoring training models is an integral part of my research ad W&B made it easy for me.