Documentation for automated readers
A curated documentation index is available at: https://grafana.com/llms.txt
A complete documentation index is available at: https://grafana.com/llms-full.txt
These indexes can help with page discovery before fetching individual documents.
This page is also available in Markdown, which may be easier for automated readers and AI tools to parse than HTML. The Markdown version is available at https://grafana.com/docs/grafana-cloud/monitor-applications/ai-observability/genai/observability.md, or by sending Accept: text/markdown to https://grafana.com/docs/grafana-cloud/monitor-applications/ai-observability/genai/observability/. For broader documentation discovery, the curated index is available at https://grafana.com/llms.txt and the complete index is available at https://grafana.com/llms-full.txt.
GenAI observability
GenAI Observability provides a complete monitoring for Large Language Model (LLM) applications including performance metrics, token usage tracking, cost analysis, and user interaction patterns.
Overview
The GenAI Observability dashboard is the main monitoring dashboard for LLM applications, offering insights into:
- Request monitoring - Volume, success rates, and response times
- Cost analysis - Real-time spend tracking and optimization insights
- Token usage - Consumption patterns and efficiency metrics
- Performance analytics - Model comparisons and trend analysis
- Error tracking - Detailed failure analysis and categorization
Key features
Performance monitoring
- Request volume and frequency tracking
- Success and failure rate analysis
- Response time distribution and latency monitoring
- Throughput analysis and concurrent request handling
Cost optimization
- Real-time cost calculation and tracking
- Cost per request analysis and optimization
- Model cost comparison and recommendations
- Budget tracking and cost alert capabilities
Token analytics
- Input and output token consumption tracking
- Token efficiency analysis and optimization
- Usage pattern identification and trends
- Model-specific token cost analysis
Getting started
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