Slide 6 of 8

AI/LLM observability overview

What you get

ComponentWhat it monitorsProblems solved
GenAI / LLMsResponse times, token usage, costs, error ratesTrack LLM performance and spending.
GenAI evaluationsHallucination detection, toxicity, biasEnsure AI output quality and safety.
Vector databasesQuery performance, operations, resource usageOptimize RAG pipelines.
MCP (Model Context Protocol)Tool analytics, session healthMonitor AI agent integrations.
GPU infrastructureUtilization, temperature, memory, powerPrevent GPU bottlenecks.

Questions answered

With AI Observability, you can answer…
How much are we spending on LLM tokens this month?
Is our AI model hallucinating or producing toxic content?
Why is our RAG pipeline returning slow or irrelevant results?
Are our GPUs being underutilized or thermal throttling?
Which AI agents are using tools most frequently?

Problems solved

ProblemSolution
LLM costs unpredictable and untrackedReal-time cost tracking per model/provider
No visibility into AI model qualityAutomated hallucination and safety detection
RAG pipeline lacks transparencyVector DB query metrics
GPU resources wastedUtilization and temperature monitoring

End-to-end AI stack visibility

AI stack pipeline showing User Request through AI Agent, LLM, Vector DB, to GPU with metrics at each stage

Script

If you're building AI applications, anything using large language models, vector databases, or GPU infrastructure, you need AI Observability. This is relatively new territory, and most teams lack clear visibility.

For LLMs, you can track response times, token usage, and most importantly, costs. LLM APIs charge by the token, and those costs can surprise you quickly if you’re not watching.

For quality, there’s hallucination detection, toxicity scoring, and bias evaluation. You need to know when your model is producing problematic outputs before your users do.

If you’re building RAG pipelines, where you retrieve context from a vector database before generating responses, you can see query performance and understand why some retrievals are slow or returning irrelevant results.

And for the infrastructure layer, you get GPU monitoring: utilization, temperature, memory. Are your expensive GPUs actually being used effectively, or are they sitting idle?

This gives you end-to-end visibility across your entire AI stack. From user request through agent orchestration, LLM calls, vector retrieval, all the way down to the GPU.

/
Previous Next