LLM Observability: See Every Token, Every Dollar

What is LLM observability?

LLM observability extends traditional application monitoring with token-level metrics: input and output counts, per-request cost, cache hit rates, model selection, and prompt structure scores. It answers questions cloud APM tools were never designed for.

What you should be able to see

• Token throughput — tokens per second, by model and route
• Request traces — forensic-grade logs with cost, latency, and cache status
• Spend heatmaps — when your AI infrastructure is busiest and most expensive
• Workspace attribution — which team or service drives MTD spend
• Anomaly detection — alerts when spend deviates from baseline

Token monitoring at the edge

Provider dashboards show aggregates. Production debugging needs per-request granularity. Tokenistt intercepts prompts via MCP — between your editor or host and the Anthropic API — and analyzes every request in under 2ms without adding perceptible latency.

Metadata (token counts, model IDs, latency, cost) flows to your dashboard. Prompt content stays local by default — aligning with privacy requirements for engineering teams.

Claude cost dashboard for platform teams

If you operate multiple Claude workloads — agents, copilots, support AI, doc intelligence — you need a Claude cost dashboard that slices spend by workspace, not just by API key. Tokenistt auto-detects workspaces from MCP client metadata and supports manual tagging for finer control.

From observability to optimization

Visibility alone does not reduce bills. The best LLM observability platforms connect metrics to action: waste highlighting, cache recommendations, model routing suggestions, and eval-gated prompt rewrites. Tokenistt closes this loop in one install.

Read what LLM cost observability means, explore AI cost management, or see our architecture docs.

Built for production AI infrastructure

Tokenistt targets engineering teams who treat AI as infrastructure — not a demo. We are an AI infrastructure startup in private beta, building the observability layer every Claude team will need as spend scales.