The Datadog/New Relic for LLM applications - deep observability powered by production data.
LLM applications in production lack proper observability. Unlike traditional apps with APM tools like Datadog, LLM apps operate as black boxes. You can't see token usage, model performance, failure patterns, or cost optimization opportunities.
Building Teneo exposed this gap - processing millions of tokens with no visibility into what was working, what was failing, or how to optimize costs and performance.
Complete observability platform specifically designed for LLM applications. Drop-in SDK that automatically tracks generations, analyzes patterns, and provides actionable insights for optimization.
Vercel Edge Functions for global low-latency collection. ClickHouse for time-series analysis.
Automatic book ID extraction and chapter progress monitoring built from Teneo requirements.
Drop-in instrumentation, decorators, context managers, CLI tool for testing
Vercel Edge Functions, Zod validation, automatic batching, retry logic
ClickHouse time-series, Redis real-time metrics, materialized views
Next.js 14, real-time updates, API key auth, dark mode
Python SDK, edge collector, real-time dashboard, basic analytics
Predictive model degradation, pattern recognition, ML insights
Team collaboration, model router API, cost optimization
Built from real production needs. Teneo generates millions of tokens and needed deep visibility into performance patterns.
The book-specific tracking features came directly from monitoring chapter generation and identifying optimization opportunities.
Primary use case for book generation monitoring
Cover generation monitoring and optimization
Conversation analytics and performance tracking
Predictive model degradation detection
ML-powered insights from production data