Raven: Real-Time AI Model Monitoring for Reliable ML Systems

Raven is a powerful AI model monitoring and alerting system designed to ensure the reliability and performance of machine learning models in production. It provides real-time insights into confidence scores, latency, throughput, and feature drift, enabling teams to proactively detect issues and maintain model integrity. Built for self-hosted deployment, Raven integrates seamlessly with Kubernetes using Helm charts and runs entirely within the user's environment, ensuring data security and privacy. With automated drift detection, customizable alerts via Slack or email, and fast metrics through ClickHouse, Raven empowers data scientists and ML engineers to monitor models at scale with minimal overhead.

Key Features:

• Real-time monitoring of confidence, latency, throughput, and output mix
• Automatic detection of data drift and feature-level drift
• Self-hosted and Kubernetes-ready deployment via Helm chart
• Alert notifications via Slack or email
• Fast metrics and long-term data retention using ClickHouse
• Minimal Python and JVM SDKs for easy integration
• Secure, on-premise deployment keeping sensitive data private
• Self-hosted dashboard for exploring trends and model history
• Support for ingestion of model logs in seconds
• Two deployment bundles: Compact (low-traffic) and Enterprise (high-traffic)
• Flexible licensing: Community, Plus, and Enterprise tiers

Pricing: Raven offers a Free tier with core metrics and HTTP ingest, a Pro plan at $199/month with drift detection and alerts, and an Enterprise plan (coming soon) for high throughput and scalability.

Conclusion: Raven is an essential tool for ML teams looking to monitor, debug, and optimize their models in production. With its robust feature set, secure self-hosted architecture, and scalable deployment options, Raven ensures that AI systems remain reliable, transparent, and performant over time.