What you get
| Component | What it monitors | Problems solved |
|---|---|---|
| GenAI / LLMs | Response times, token usage, costs, error rates | Track LLM performance and spending. |
| GenAI evaluations | Hallucination detection, toxicity, bias | Ensure AI output quality and safety. |
| Vector databases | Query performance, operations, resource usage | Optimize RAG pipelines. |
| MCP (Model Context Protocol) | Tool analytics, session health | Monitor AI agent integrations. |
| GPU infrastructure | Utilization, temperature, memory, power | Prevent 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
| Problem | Solution |
|---|---|
| LLM costs unpredictable and untracked | Real-time cost tracking per model/provider |
| No visibility into AI model quality | Automated hallucination and safety detection |
| RAG pipeline is a black box | Vector DB query metrics |
| GPU resources wasted | Utilization and temperature monitoring |
End-to-end AI stack visibility
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 are flying blind.
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.