AI Observability

OpenTelemetry-native AI observability with distributed tracing across your complete AI stack. Monitor and visualize real-time performance of LLMs, vector databases, GPUs and MCP Servers (Model Context Protocol)

Note
To monitor and observe LLM agents in production, refer to the Machine Learning AI Observability documentation.

Overview

Grafana AI Observability is a complete solution designed to monitor and optimize your entire AI stack. It provides end-to-end observability across all components of your AI stack.

GenAI observability

Performance tracking: Monitor LLM response times, throughput, and availability across providers
Cost management: Real-time spend tracking, cost optimization, and budget management for LLM usage
Token analytics: Track consumption patterns, efficiency metrics, and usage optimization opportunities
User interactions: Gain insights into user interactions, prompts, and completions for performance understanding

GenAI evaluations

Quality assessment: Automated hallucination detection, factual accuracy verification, and content quality scoring
Safety monitoring: Continuous toxicity detection, bias assessment, and compliance tracking for responsible AI
Evaluation scoring: Confidence levels, quality gates, and automated quality assurance workflows
Problem identification: Detailed analysis and categorization of AI model issues and failure patterns

GenAI Agent Observability

Invocation tracking: Monitor total agent invocations, usage distribution by source, and percentage breakdown across your agentic AI systems
Cost management: Real-time tracking of total agent costs in USD, per-agent cost breakdown, and cost attribution for budget optimization
Performance monitoring: Track 95th percentile operation duration, average latency by agent and provider, and operation throughput rates
Logs and debugging: Integrated agent logs with OpenTelemetry trace and span ID correlation for distributed tracing and root cause analysis

VectorDB observability

Query performance: Monitor similarity search response times, throughput, and query optimization
Database operations: Track insert, update, and delete operations across different vector database providers
Resource utilization: Monitor memory usage, storage efficiency, and infrastructure scaling needs
Index management: Track index building, optimization, and maintenance for optimal search performance

MCP observability

Protocol health: Track session management, connection stability, and protocol compliance metrics
Tool analytics: Monitor tool usage patterns, performance, and availability across your AI ecosystem
Transport monitoring: Analyze communication performance across HTTP, WebSocket, and other transport layers
Integration insights: Track tool invocation patterns, payload analysis, and system reliability

GPU observability

Performance monitoring: Track GPU utilization, compute efficiency, and processing throughput
Thermal management: Monitor temperatures, cooling systems, and prevent thermal throttling
Resource optimization: Analyze memory usage, power consumption, and multi-GPU coordination
Infrastructure health: Monitor hardware status, driver stability, and predictive maintenance metrics