Overview

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Why you need LLM observability?

• Performance monitoring: Track response times, token usage, and model performance to ensure your AI systems are working optimally.
• Cost management: Gain visibility into model usage patterns, identify expensive prompts, and optimize spending across different LLM providers.
• Quality assurance: Detect hallucinations, accuracy issues, and unexpected outputs before they impact your users.
• Debugging: Quickly identify and troubleshoot issues by examining the complete AI session.
• Usage analytics: Understand how users interact with your AI features and which prompts generate the most value.

Without proper observability, LLM-powered applications become black boxes - expensive to run, difficult to debug, and impossible to systematically improve.

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Getting started