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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Buyer guide
Gain valuable insights from real users who purchased this product, powered by PeerSpot.
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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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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Customer service
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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
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
Anson D.
Weights & Biases Makes Experiment Tracking and Run Comparisons Effortless
Reviewed on Jul 11, 2026
Review provided by G2
What do you like best about the product?
What I like most about Weights & Biases is how easy it is to keep track of experiments in one place. The dashboard is well organized and makes it simple to compare runs, monitor metrics, and visualize results. It saves a lot of time compared to manually recording experiment details.
What do you dislike about the product?
The platform has a lot of features, so it can feel a bit overwhelming when you're getting started. It took me some time to understand where everything was. Apart from that, I haven't faced any major issues while using it.
What problems is the product solving and how is that benefiting you?
Weights & Biases helps me organize and track machine learning experiments instead of managing everything manually. Having metrics, logs, and experiment history in one dashboard makes it much easier to compare results and understand what changes are improving the model. It has made my workflow more organized and efficient.
Kumar S.
Easy Experiment Tracking and Smooth PyTorch Lightning Integration
Reviewed on Jul 09, 2026
Review provided by G2
What do you like best about the product?
It has easy experiment tracking and smooth integration with tools like PyTorch Lightning, which makes logging metrics and comparing runs very simple
What do you dislike about the product?
The tracked-hours pricing can become expensive when running multiple experiments in parallel. The dashboard also slows down with large logs, and offline sync isn't always reliable after interrupted runs. Improving performance, sync stability, and making pricing more predictable would make the overall experience much better.
What problems is the product solving and how is that benefiting you?
Earlier, we relied on Excel sheets and screenshots to track experiments, which made comparing models and managing runs quite messy. Now W&B automatically logs metrics, hyperparameters, resource usage, and predictions in one dashboard. Comparing runs is much easier, the whole team has better visibility, reports are easy to share, and hyperparameter sweeps have saved us a lot of manual effort while making experiments more reproducible.
Punit Jain
Experiment tracking has transformed model tuning and now supports faster, more informed AI workflows
Reviewed on Jun 30, 2026
Review provided by PeerSpot
What is our primary use case?
I use Weights & Biases primarily for experiment tracking, logging metrics such as loss and accuracy, learning rate, and other parameters. It helps in visualizing training progress in real time, particularly for deeper projects involving dataset modeling, CI/CD pipelines, and similar tasks.
I used Weights & Biases in my personal project involving self-generating adversarial networks, where I tracked generator and discriminator losses over time, logged sample outputs, and compared architectures and hyperparameters. Those losses helped me analyze my model to optimize it so that they became negligible or minimal. Weights & Biases provides real-time dashboards, image logging, experiment comparison, and other useful features.
One thing I appreciate about using Weights & Biases is how it fits into the bigger picture of ML workflows. As a developer, I can integrate end-to-end workflow integrations, which include data pipelines to track, model registry to store and manage, and deployment monitoring, so that I can analyze how models are performing, the losses, and the gains. It also supports hyperparameter tuning and model comparisons, including the comparison of losses and gains. In my opinion, it is a research notebook experiment collaboration tool.
Using Weights & Biases gave me the ability for faster development and also saved my time since analyzing the discriminator and generator losses, which would have taken a lot of time if I did it manually, was done very easily with Weights & Biases. The graphs it provided were also very helpful in analyzing the gains and losses and the accuracy of the generator and discriminator model.
What is most valuable?
The best features that Weights & Biases offers include experiment tracking to monitor accuracy, loss, and learning rate in real time, visualizing the training process with dashboards. Additionally, it automates testing of hyperparameter configurations for different models through the hyperparameter feature. Weights & Biases has a model registry to version data, models, prompts, and code, ensuring reproducibility by linking experiments. Weights & Biases also has an integration ecosystem that works seamlessly with frameworks like PyTorch and TensorFlow .
What needs improvement?
Deployment and monitoring stands out as a feature I wish had further improvement. When I used it, it served as a fine-tuned model directly from Weights & Biases, providing automations for CI/CD pipelines and machine learning.
From my perspective, I don't think Weights & Biases needs significant improvement, but areas involving more image tracking and additional integrations with tools like PyTorch or TensorFlow would be beneficial. I would prefer some AI tools to be integrated, such as Vercel or Netlify for deployments, as that would create ease of use for developers.
In terms of Weights & Biases's AI capabilities, I believe improvements can be made regarding governance and security. In the AI world, many organizations struggle with securing their codes effectively, so if Weights & Biases introduced features related to security score levels, it would be helpful in enhancing security and strengthening code in a cohesive manner.
For how long have I used the solution?
I have been using Weights & Biases since last year.
What do I think about the stability of the solution?
Weights & Biases is stable.
Which solution did I use previously and why did I switch?
Before using Weights & Biases, I had only developed in personal projects involving self-generating adversarial networks, where I analyzed the performance of generator and discriminator models along with their gains and losses.
What was our ROI?
I have seen a return on investment in terms of time saved, with improved accuracy, reduced losses, and increased gains.
What's my experience with pricing, setup cost, and licensing?
I only use the free tier of Weights & Biases, and I do not have any information regarding the prices or setup costs for licensing. The free tier is sufficient for developers who are in college, in my opinion.
Which other solutions did I evaluate?
I only use Weights & Biases and did not evaluate any other options.
What other advice do I have?
I suggest avoiding making the interview too lengthy, as it is meant for review purposes and should not take up thirty minutes to one hour. My overall review rating for Weights & Biases is eight out of ten.