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About
WhyLabs defined the AI Observability category, providing enterprises and ML teams with tools to monitor, secure, and audit machine learning models and large language models (LLMs) in production. The platform enabled teams to detect data drift, model degradation, and anomalous behavior in real time, helping organizations adopt AI responsibly at scale. Following the discontinuation of commercial operations, WhyLabs has open-sourced its complete platform to support the continued advancement of AI observability research. Two key open-source projects live on: whylogs, the open standard for privacy-preserving data logging and monitoring in AI pipelines, and langkit, a toolkit specifically designed for monitoring and securing LLMs while preserving user privacy. WhyLabs served trailblazing enterprise customers, providing visibility into model performance, data quality, and safety metrics across production AI systems. Its contributions to the Robust and Responsible AI community—spanning thousands of practitioners—continue to shape best practices across the industry. Developers and ML engineers looking to instrument their AI systems with observability tooling can access the open-source projects directly via GitHub.
Key Features
- AI Observability Platform: End-to-end platform for monitoring ML models and LLMs in production, detecting drift, degradation, and anomalies in real time.
- whylogs – Privacy-Preserving Data Logging: An open standard for logging and monitoring AI/ML data pipelines without exposing raw sensitive data.
- langkit – LLM Monitoring Toolkit: Open-source toolkit for monitoring and securing large language models in production while preserving end-user privacy.
- Data Drift & Model Degradation Detection: Automated statistical profiling to detect shifts in data distributions and model performance before they impact business outcomes.
- Fully Open-Sourced Platform: The complete WhyLabs platform has been open-sourced, enabling the research community and engineering teams to build on its AI observability foundation.
Use Cases
- Monitoring production ML models for data drift and performance degradation over time.
- Securing and auditing LLM outputs in customer-facing AI applications.
- Implementing privacy-preserving logging in regulated industries such as finance or healthcare.
- Building internal AI observability infrastructure using open-source components.
- Researching and advancing responsible AI operations and MLOps best practices.
Pros
- Pioneering Open-Source Observability: WhyLabs helped define the AI observability category and its open-source tools (whylogs, langkit) are widely adopted in the ML community.
- Privacy-Preserving by Design: whylogs enables statistical monitoring without logging raw data, making it suitable for privacy-sensitive enterprise deployments.
- LLM-Specific Monitoring: langkit provides targeted tooling for the unique challenges of monitoring LLM outputs, including toxicity, relevance, and security.
Cons
- Commercial Operations Discontinued: WhyLabs as a company has shut down, meaning no ongoing commercial support, SLAs, or managed cloud offerings.
- Community-Driven Maintenance Only: Future development relies on open-source community contributions, which may result in slower updates and limited roadmap visibility.
Frequently Asked Questions
Is WhyLabs still available to use?
WhyLabs has discontinued commercial operations, but the full platform has been open-sourced. The whylogs and langkit libraries remain available on GitHub for community use.
What is whylogs?
whylogs is an open-source data logging standard for AI and ML pipelines. It generates lightweight statistical profiles of data without storing raw records, preserving privacy while enabling monitoring.
What is langkit?
langkit is an open-source Python toolkit for monitoring and securing LLMs. It helps teams evaluate LLM outputs for issues like toxicity, prompt injection, and relevance while maintaining user privacy.
Who was WhyLabs designed for?
WhyLabs was designed for ML engineers, data scientists, and AI platform teams at enterprises needing production-grade monitoring for machine learning models and LLMs.
Where can I find the open-sourced WhyLabs code?
The whylogs and langkit projects are available on GitHub under their respective repositories. You can reach the founding team at [email protected] for further guidance.
