Sold by: WhyLabs
Model monitoring, data health, data drift detection, and AI observability.
4.6
Overview
WhyLabs is the essential AI Observability Platform for model and data health. It is the only machine learning monitoring and observability platform that doesn't operate on raw data, which enables a no-configuration solution, privacy preservation, and massive scale.
Machine learning engineers and data scientists rely on the platform to monitor ML applications and data pipelines by surfacing and resolving data quality issues, data bias, and concept drift. These capabilities help AI builders reduce model failures, avoid downtime, and ensure customers are getting the best user experience. With out-of-the-box anomaly detection and purpose-built visualizations, WhyLabs eliminates the need for manual troubleshooting and reduces operational costs.
The platform can monitor tabular, image, and text data. It integrates with many popular ML and data tools including Pandas, Apache Spark, AWS Sagemaker, MLflow, Flask, Ray, RAPIDS, Apache Kafka, and more. To learn more about what data types WhyLabs can work with and which tools we integrate with, check out the whylogs GitHub page: https://github.com/whylabs/whylogs
WhyLabs was created at the Allen Institute for Artificial Intelligence (AI2) by Amazon Machine Learning alums and is backed by Andrew Ng's AI Fund.
For custom pricing, EULA, or a private contract, please contact AWSMarketplace@whylabs.ai for a private offer.
Highlights
- Enable data and model monitoring quickly and securely: Automated monitoring and alerting across dozens of "data vitals" with out-of-the-box configurations and lightweight integrations. Cloud agnostic, built with AWS-grade privacy and security. Integration takes less than an hour.
- Deliver the impact models were designed for: Improve model performance, resilience, and auditability with alerting and reporting tools. Monitor model inputs, outputs, performance as well as upstream data quality in one platform.
- Achieve AI Governance across the organization: Track all relevant metrics associated with the data that flows through Al applications. Enabling observability in Al applications is key for achieving Al Governance best practices.
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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. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
1 Model (free tier) | Monitoring for one model | $0.00 |
2 Models | Monitoring for two models | $100.00 |
3 Models | Monitoring for three models | $200.00 |
4 Models | Monitoring for four models | $300.00 |
5 Models | Monitoring for five models | $400.00 |
WhyLabs Enterprise | Enterprise contract with model monitoring at scale | $8,333.33 |
Vendor refund policy
No refunds
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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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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.
Resources
Vendor resources
Support
Vendor support
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 Fiddler AI
By Arize AI
Top
100
In Data Governance
Top
50
In Computer Vision
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25
In Observability, Software Development
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
Data Quality Monitoring
Automated monitoring and alerting across data vitals with out-of-the-box anomaly detection and configurations for identifying data quality issues.
Multi-Data Type Support
Capability to monitor tabular, image, and text data types across machine learning applications and data pipelines.
Privacy-Preserving Architecture
Platform operates on processed data summaries rather than raw data, enabling privacy preservation and no-configuration deployment at scale.
Comprehensive ML Observability
Unified monitoring of model inputs, outputs, performance metrics, data drift, concept drift, and upstream data quality issues in a single platform.
Broad Integration Ecosystem
Integration with popular ML and data tools including Pandas, Apache Spark, AWS SageMaker, MLflow, Flask, Ray, RAPIDS, and Apache Kafka.
Multi-Model Type Support
Supports monitoring and observability for tabular, deep learning, computer vision, natural language processing, and large language model deployments
Performance and Drift Detection
Identifies and mitigates model performance degradation, data drift, data integrity issues, hallucination, accuracy, safety, and security issues in production deployments
Root Cause Analysis and Diagnostics
Provides powerful root cause analysis and diagnostic capabilities with 3D UMAP visualization for macro-level trend analysis and micro-level issue identification
Enterprise Security and Access Control
Implements SOC2 Type 2 security compliance and role-based access control (RBAC) for level-specific user permissions across protected environments
Customizable Analytics and Metrics
Offers customizable dashboards, reports, and custom metrics to track model performance aligned with business KPIs and enable data-driven decision-making
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
No
No
No
Customer reviews
Akashkhurana Hirana
Monitoring multi-agent LLM workflows has become reliable and protects PII in real time
Reviewed on Jun 29, 2026
Review from a verified AWS customer
What is our primary use case?
My main use case for WhyLabs was for LLM monitoring and observability. At that time, I had an AI application that I deployed on Vertex AI, and I used WhyLabs for the observability, logging, and monitoring of that application and the model.
I can provide a specific example of how I used WhyLabs for monitoring my LLM application. It was a multi-agent system with around four agents involved, and each agent had around seven or eight tools that it could use or invoke. Whenever a user sent a query to the main agent, its responsibility was to delegate the request among the other sub-agents. Each sub-agent could communicate with each other using the A2A protocol and call their tools. I monitored how the request progressed through the system. For instance, if a user sent a request to one agent, which then transferred it to a third agent, the third agent used a tool, and then it went to the seventh agent. I could easily monitor all this communication between the agents, the logging time, the request, the response, any errors, and any guardrails I wanted in my application in WhyLabs.
This was my only use case, and then WhyLabs got discontinued. WhyLabs was acquired by Apple in January or February 2025. The company then open-sourced their software so that anyone can use it. It is now open-source software available on GitHub where you can set it up yourself and use it.
What is most valuable?
WhyLabs's best features are real-time guardrails, PII personal information data detection, hallucination mitigation, and monitoring. It has a centralized dashboard so I can create a project and see an overall summary of the dashboards, and I can check the health metric on specific dates or specific times for WhyLabs or for the application. Additionally, it provides an alerting system. If there is an error or the system is down, it generates an alert via email.
Out of all those features, I find the PII detection and the monitoring most valuable in my day-to-day work because it is very hard to monitor an LLM application. As I mentioned earlier, it was a multi-agent system and a query can go from one agent to another agent very easily, which created problems in debugging how the request was progressing and how the data flow was happening. The monitoring and the PII detection of the guardrails are the three features most useful to me. Regarding the guardrails or the PII detection, if I do not want my PII data given to the agents or any LLM, this feature is particularly useful in that scenario.
WhyLabs has positively impacted my organization by reducing the error time and debugging time. It has increased and enhanced the user experience. When the application is down, I receive alerts, which has reduced a significant amount of time for my team.
What needs improvement?
Regarding how WhyLabs can be improved, since it is not available in the market as of now, improvements cannot be made to the product itself. However, there is an open-source version that anyone can set up on their machine and try to accomplish the same things.
I do not think there is anything else needed for improvement.
For how long have I used the solution?
I was using it in 2024 for around 1.5 years.
What was our ROI?
WhyLabs has saved my team time by 30 to 40%.
What other advice do I have?
Regarding WhyLabs's AI capabilities, I believe its governance and security are totally secured. It was deployed in our on-premises infrastructure, so all the data remains in our infrastructure only. The guardrails and the PII detection work perfectly. I have not seen any scenario where it has not generated an alert for PII data or the guardrails have not worked, so it performed very well.
In terms of WhyLabs's AI capabilities, I believe it is totally accurate. I used it for around 1.5 years, and it was the best software available, but it was discontinued. However, it was a very good software.
My advice to others considering WhyLabs is that as of now it is open-source, and you can set it up on your own machine for free and use it. It has very good features. I would rate this product a 10 out of 10.
Which deployment model are you using for this solution?
On-premises
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Consulting
I built a monitoring solution with Whylabs for multiple ML models for a client of mine.
Reviewed on Nov 21, 2024
Review provided by G2
What do you like best about the product?
The UI is very user friendly and the support team is extremely responsive and helpful.
What do you dislike about the product?
Some features such as deleting a profile or changing the data type of a feature can only be done through the API.
What problems is the product solving and how is that benefiting you?
Data and concept drift detection
Model performance monitoring
Model performance monitoring
Internet
Whylabs helped us setup end-to-end monitoring of our ML projects
Reviewed on Nov 19, 2024
Review provided by G2
What do you like best about the product?
* The customer support is very helpful and proactive
* Tool allows for easy ingestion of big number of features and setting up initial monitoring on them
* We can use it to monitor both: input quality and model performance
* The alerts can be raised to specific group of users via specific channels (email/slack), which is helpful
* Tool allows for easy ingestion of big number of features and setting up initial monitoring on them
* We can use it to monitor both: input quality and model performance
* The alerts can be raised to specific group of users via specific channels (email/slack), which is helpful
What do you dislike about the product?
* It can be challenging to setup the monitoring in the correct way when it comes to sensitivty - it requires a lot of trial and error
* Some actions are not possible via UI and require specific API calls
* Documentation can be hard to navigate
* Some actions are not possible via UI and require specific API calls
* Documentation can be hard to navigate
What problems is the product solving and how is that benefiting you?
Monitoring model performance and input data quality in one place.
Houssam K.
Excellent tool for ML Monitoring with many out-of-the box solutions
Reviewed on Nov 14, 2024
Review provided by G2
What do you like best about the product?
Great to collaborate with; very responsive; really appreciate their OHs to help out with issues that pop up; many out-of-the-box solutions for different kinds of ML models which really helped us out given the wide variety of ML models we run at the company.
What do you dislike about the product?
Nothing major to mention! We got everything resolved and the team was very helpful.
What problems is the product solving and how is that benefiting you?
Data Drift and ML Monitoring
Rafael S.
Developed efficient solutions for optimizing ERP workflows through data analysis
Reviewed on Sep 18, 2024
Review provided by G2
What do you like best about the product?
One of the standout features of WhyLabs is its robust data observability capabilities. It provides continuous monitoring of data pipelines and ML models, allowing teams to quickly identify issues like data drift, model degradation, and training-serving skew. The platform's privacy-preserving integration ensures that data can be analyzed without moving or duplicating it, which is critical for maintaining security and privacy in sensitive industries like healthcare and finance
What do you dislike about the product?
One potential drawback of WhyLabs is its relatively limited user reviews and feedback due to its newness in the market, making it harder for potential users to gauge its real-world performance at scale. This lack of detailed reviews can raise concerns about its maturity and support infrastructure.Additionally, since it’s a newer platform, some advanced features might still be in development, and there could be steep learning curves for teams unfamiliar with observability tools in machine learning.
What problems is the product solving and how is that benefiting you?
Data quality issues: It helps detect and address data drift and data integrity problems early, which is crucial for maintaining accurate and reliable ML models