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AlexPersonal Operating System
Alex OS Online
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AI Observability & Cost Governance

Making AI features auditable: tracing every model call with tokens, prompts, responses and USD cost, and surfacing it where owners can act on it.

Problems I solve
Unknown or surprising AI API bills, No record of what prompt produced a bad answer, AI behavior changing silently between releases
Approach
Trace at the model-router layer so nothing escapes instrumentation, Store full prompt/response pairs, not just counts, Compute cost per call, not per invoice, Pair tracing with evaluation cases for behavioral regression
Strengths
100% call coverage by construction, Cost visibility down to a single feature interaction

Full description

AI features fail quietly — costs drift, prompts regress, and nobody can reconstruct why an answer looked the way it did. I instrument every LLM call at the routing layer so each request records its model, token usage, full prompt and response, and computed cost. On this platform that trace log powers an admin dashboard where any AI interaction can be audited end-to-end, and evaluation cases keep reasoning behavior honest over time.

Capability statement

I can make an AI product’s spend and behavior fully transparent — per-call tracing, cost attribution and an audit dashboard.

Current focus

Extending traces into automatic anomaly flags — surfacing cost or behavior regressions without manual review.

Explore with Alex OS

Every LLM call is traced — model, tokens, full prompt, response, and USD cost — so AI spend and behavior are auditable by construction.

AI Observability & Cost Governance

AI features fail quietly: costs drift, prompts regress, and there is no record of why a response looked the way it did. Alex instruments every LLM call at the routing layer so nothing escapes capture — model identity, token counts, full prompt and response, and computed cost per call. The result is an audit trail that powers a dashboard where any AI interaction can be inspected end-to-end.

How the instrumentation works

Why per-call cost matters more than per-invoice cost

An invoice tells you what you spent last month. Per-call attribution tells you which feature, which user action, and which model choice drove that spend. Without that granularity, cost reduction is guesswork. With it, a single expensive interaction pattern is identifiable and addressable before it compounds.

What this delivers in practice

On the platform Alex built this for: any AI interaction is auditable end-to-end through an admin dashboard; cost is visible down to a single feature interaction; and evaluation cases keep reasoning behavior honest across releases. Current work extends traces into automatic anomaly flags — surfacing cost or behavior regressions without requiring manual review.