What metrics matter most in LLM observability?
Start with request logs, latency, error rate, retries, token usage, and cost per successful request. These metrics connect reliability and spend decisions.
llm observability
LLM Observability: Logs, Latency and Cost Control
Use LLM observability to track requests, costs, latency, errors, and provider performance in one dashboard for production teams.
Built for teams comparing observability, cost control, and provider operations workflows before rolling out production AI features.
What to measure
| Metric | Why it matters |
|---|---|
| Request logs | Debug prompts, payloads, retries, and model routing decisions. |
| Latency by endpoint/model | Protect UX and SLOs for AI-powered workflows. |
| Errors and retries | Find instability that increases cost and incident load. |
| Usage + cost together | Avoid optimizing reliability and spend in separate tools. |
Proof from the product
Real UI snapshot from AI Cost Board used in production workflows.
Dashboard view for model, provider, and workspace spend tracking.
When to choose this workflow
- • Platform teams centralizing AI API telemetry across products.
- • Engineering teams debugging expensive prompts and retry loops.
- • Operations teams monitoring provider performance and incident patterns.
Feature pages to review
Comparison pages
Use-case pages
RAG Cost Monitoring
Track retrieval + generation cost, latency, retries, and error patterns for RAG pipelines in production.
AI Copilot Cost Monitoring
Monitor AI copilot usage, cost per user, latency, and error rates across product surfaces and cohorts.
Customer Support AI Cost Monitoring
Track AI support automation cost, latency, errors, and resolution quality by queue, channel, and team.
Track real AI API operations with AI Cost Board
Monitor cost, usage, latency, errors, request logs, and provider performance in one operational dashboard.
FAQ
Is LLM observability only for tracing and debugging?
No. Production teams also need spend visibility, budget alerts, and provider-level analytics to run AI systems safely at scale.
How is AI Cost Board positioned in LLM observability?
AI Cost Board focuses on observability plus cost control and governance, combining request evidence with budget and project-level monitoring workflows.