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Grafana Cloud
GenAI observability and evaluations
Complete monitoring and evaluation for your generative AI applications, covering both performance observability and quality assessment.
Overview
GenAI monitoring provides three approaches to ensure your AI applications perform optimally and safely.
GenAI Observability
Monitor the operational aspects of your LLM applications:
- Performance tracking - Response times, throughput, and availability
- Cost management - Real-time spend tracking and optimization
- Token analytics - Usage patterns and efficiency metrics
- Usage insights - User interaction patterns and trends
GenAI Evaluations
Assess the quality and safety of your AI model outputs:
- Quality assessment - Hallucination detection and factual accuracy
- Safety monitoring - Toxicity and bias detection
- Evaluation scoring - Confidence levels and quality gates
- Compliance tracking - Safety and regulatory compliance
GenAI Agent Observability
Monitor the operational aspects of your AI agent applications:
- Invocation tracking - Total invocations, distribution by source, and usage patterns
- Cost management - Real-time agent spend tracking and per-agent cost breakdown
- Performance analytics - Operation duration, latency percentiles, and throughput rates
- Operational logs - Agent interaction logs with trace context and message details
Supported technologies
- LLM Providers - OpenAI, Anthropic, Google, AWS Bedrock, Cohere and a lot more
- Frameworks - LangChain, LlamaIndex, CrewAI, LiteLLM and a lot more
Getting started
GenAI Observability
Monitor LLM performance, token usage, costs, and user interactions with comprehensive AI metrics and analytics.
GenAI Evaluations
Evaluate AI model quality with hallucination detection, toxicity analysis, and safety assessments for responsible AI deployment.
GenAI Agent Observability
Monitor AI agent systems with invocation tracking, cost analysis, and performance metrics across your agentic AI applications.
