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WhyLabs

Freemium
mlopsai observabilitymodel monitoringdata driftmachine learningllm monitoringdata qualitywhylogsdeveloper toolsartificial intelligence

WhyLabs is an AI observability platform that enables data scientists and ML engineers to monitor the health, performance, and integrity of their machine learning models and data pipelines in production environments.

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WhyLabs offers a comprehensive suite for AI observability, specializing in monitoring machine learning models after deployment. Built upon the open-source `whylogs` standard for data logging, the platform helps teams detect critical issues like data drift, concept drift, and data quality degradation in real-time. It targets ML engineers and data scientists who need to ensure the reliability and performance of their AI systems. Its unique value lies in providing a unified view across all models, supporting structured data, unstructured data, and embeddings, while also offering an AI Firewall to monitor LLM applications. This proactive monitoring approach helps prevent model failures and maintain trust in AI-powered products.

Pros

  • Built on the open-source `whylogs` library, preventing vendor lock-in and fostering community-driven development.
  • Provides holistic monitoring across data drift, data quality, and model performance metrics.
  • Supports a wide range of data types including structured, unstructured (images, text), and embeddings.
  • Offers an "AI Firewall" feature specifically for monitoring and securing Large Language Model (LLM) applications.
  • Integrates seamlessly with major cloud platforms and MLOps tools like AWS SageMaker, Databricks, and MLflow.

Cons

  • The platform has a significant learning curve for users not already familiar with MLOps or observability concepts.
  • The starting price for paid plans is substantial, potentially creating a barrier for smaller teams or individual developers.
  • While powerful, the user interface can feel dense and complex for non-technical stakeholders.

Key features

  • Data and Concept Drift Detection
  • Data Quality Monitoring
  • Model Performance and Prediction Monitoring
  • AI Firewall for LLM Security and Monitoring
  • Customizable Alerting and Notifications
  • Data Profiling with whylogs
  • Bias and Fairness Tracking
  • Support for Batch and Streaming data

Integrations

AWS S3Amazon SageMakerDatabricksSnowflakeApache SparkMLflowKubeflowGoogle Cloud Platform (GCP)Microsoft AzureRay

Target audience

Machine Learning Engineers, Data Scientists, and MLOps teams responsible for deploying, monitoring, and maintaining AI/ML models in production.

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Founded

2019

Headquarters

Seattle, USA

Pricing Tiers

Free

For individuals & early-stage projects. Includes up to 2 models/datasets, up to 100 profiles per hour, unlimited users, and 7-day data retention.

Free

Starter

For teams deploying models to production. Includes up to 10 models/datasets, unlimited profile ingestion, 30-day data retention, AI Firewall, and advanced user permissions.

$500/mo

Enterprise

For large-scale ML and mission-critical systems. Includes custom model/dataset limits, extended data retention, flexible deployment options (SaaS or Private Cloud/VPC), and premium support.

Custom

Frequently Asked Questions

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Datadog

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