Section 061 · Chapter 8, Operating AI: Observability, Relevance, and Economics
Token Efficiency, Model Choice, and Business Value
The best AI system is not the biggest model or the cheapest model. It is the model path that creates the most trustworthy value for the risk, cost, latency, and business constraints.
What to do
- Measure first-token latency, full-response latency, p95 and p99 latency, queueing delay, retry delay, and tool-call delay.
- Compare marginal quality gain against marginal cost, latency, privacy exposure, security risk, regional availability, and continuity risk.
- Track cost per successful outcome, not cost per request.
Evidence to preserve
- Include retrieval, embeddings, reranking, tool calls, judge passes, caching, storage, human review, failed attempts, retries, monitoring, and incident response.
- Track cost per successful outcome, not cost per request.
- Preserve the inputs, versions, configurations, raw outcomes, and results for latency, model choice, token efficiency model choice value needed to reproduce work on Token Efficiency, Model Choice, and Business Value.
- Report results for latency, model choice, token efficiency model choice value by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
In a real release review, build an efficient frontier for AI quality. Compare marginal quality gain against marginal cost, latency, privacy exposure, security risk, regional availability, and continuity risk. Track cost per successful outcome, not cost per request. Maintain fallback models, provider substitution tests, cached-path tests, and region-aware deployment checks so the business can keep operating when a model, vendor, region, or policy changes.
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Cite this page
Jason Arbon. "Token Efficiency, Model Choice, and Business Value." Testing AI Knowledge Edition, section 61.
https://jarbon.ai/testing-ai/knowledge/ch061-token-efficiency-model-choice-value.html