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GenAI observability and evaluations

Complete monitoring and evaluation for your generative AI applications, covering both performance observability and quality assessment.

Overview

GenAI monitoring provides two complementary 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

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.