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)

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