WhyLabs

WhyLabs is an AI observability platform that provides real-time monitoring, explainability, and data quality assurance for machine learning models in production. Founded in 2019 and headquartered in Seattle, Washington, WhyLabs was created by a team of former Amazon AI engineers who experienced firsthand the challenges of operating ML systems at scale. The platform is built on top of the open-source whylogs library, which provides lightweight, privacy-preserving data logging capabilities. WhyLabs' core value proposition is enabling organizations to monitor their ML models and data pipelines continuously without requiring access to raw data. The platform uses statistical profiling to capture the behavior of data and models over time, detecting anomalies, drift, and quality issues in real time while preserving data privacy. This privacy-preserving approach is particularly valuable for organizations in regulated industries that cannot send sensitive data to external monitoring services. On the explainability front, WhyLabs integrates feature importance tracking and model behavior analysis into its monitoring workflows. The platform can track how feature contributions change over time, identify when model explanations shift in ways that may indicate problems, and alert teams when model behavior deviates from expected patterns. While WhyLabs' explainability capabilities are more focused on monitoring explanation quality over time than generating initial explanations (for which tools like SHAP are better suited), this temporal perspective on explainability is uniquely valuable for production systems. WhyLabs offers a freemium pricing model, with a generous free tier that supports basic monitoring for a limited number of models, making it accessible for teams to evaluate and adopt incrementally. The platform integrates with major ML frameworks and deployment tools including MLflow, SageMaker, Databricks, and Apache Spark, enabling rapid adoption into existing workflows. The platform's drift detection capabilities are particularly strong, supporting both data drift and concept drift monitoring across structured and unstructured data types including text, images, and embeddings. WhyLabs also provides automated alerting, dashboarding, and reporting features that help ML teams stay on top of model health without requiring constant manual oversight.