LLM observability
See what your AI is doing.
Most LLM apps are a black box in production. I add tracing so you can open any request, read the prompt and the response, and know exactly what your cost and latency are doing.
Every call is traceable
Open any request and read the full trace. Prompt, retrieved context, tool calls, and the response, all in one place.
Cost stops being a surprise
Token usage and spend tracked per request, per feature, and per user. You find the expensive prompt before the bill does.
You catch failures first
Alerts on errors, timeouts, and latency spikes, so you hear about a broken flow from your dashboard, not from an angry customer.
Tracing your team will actually read.
I instrument your app with OpenTelemetry style tracing and a tool built for LLM calls. The goal is a dashboard where anyone on your team can answer why a specific answer was slow, wrong, or expensive without digging through raw logs.
- One trace per request, from user input to final answer
- Cost and token counts attached to every model call
- Latency broken down by retrieval, model, and tool steps
Tracing
Langfuse, Helicone, or a self hosted setup, wired into your OpenAI and Anthropic calls.
Cost tracking
Spend per feature and per user, with alerts before a runaway prompt drains the budget.
Latency
P50, P95, and P99 tracked per step, so you know whether the model or retrieval is the slow part.
Handover
Dashboards, alerts, and instrumentation in your repo. Your team owns it after I leave.
Ways I usually start.
Pick the pressing problem and we scope from there on the call.
Instrument from scratch
Add tracing, logging, and cost tracking to an app that ships blind today.
Fix a cost blowup
Find where the spend goes, cut the waste, and put alerts in place so it stays down.
Chase a latency problem
Trace the slow requests, find the real bottleneck, and make the app feel fast again.
Stop flying blind.
Fifteen minutes on a call and I come back with a plan to make your LLM app visible, plus a price and a date.