AI Inference Cost & Reliability Operating Checklist (2026)

Use this checklist to move an AI feature from prototype to profitable production. Target outcome: you can explain cost-per-successful-task, enforce budgets in code, and meet p95 latency SLOs with safe fallbacks.

1) Define the task contract (Product + Eng)
- Write a one-sentence task definition (what the AI does).
- Define success criteria (e.g., “customer gets a correct answer with a cited source”).
- Define failure criteria (timeouts, hallucinations, policy violations, wrong tool actions).
- Set SLOs: p95 end-to-end latency (e.g., 6s), task success rate (e.g., 99%), and allowed degradation modes.

2) Instrumentation (Platform + SRE)
- Log per step: model name, prompt version, input/output tokens, latency, and tool calls.
- Add task-level tracing (OpenTelemetry): one trace ID spanning all model/tool steps.
- Capture “cost per task” as a first-class metric (tokens + tool + retries + overhead).
- Add dashboards: p50/p95 latency, retry rate, fallback rate, cache hit rate, and cost/task.

3) Budget enforcement (App Eng)
- Enforce hard limits in code:
  - max model calls per task
  - max input tokens (context window budget)
  - max output tokens
  - max tool execution time
- Add circuit breakers: if provider errors spike, automatically switch route or degrade.

4) Routing strategy (Platform)
- Default to a smaller/cheaper model for routine steps (classification, extraction, drafting).
- Define escalation rules using measurable signals:
  - low confidence
  - high-value customer tier
  - high-risk category (legal, security, financial)
- Set a target ceiling for premium-model usage (e.g., <15% of tasks) and review weekly.

5) Caching & context management (Platform + App Eng)
- Implement semantic caching for repeated questions (set similarity threshold + TTL).
- Cache tool results with TTLs (search results, DB lookups, enrichment calls).
- Implement context pruning: summarize-to-memory, store full logs outside prompts.
- Track average context length and its correlation with cost/task and p95 latency.

6) Tool security & governance (Security + Platform)
- Treat the agent like a production identity:
  - least privilege tokens per tool
  - allowlists for destinations/actions
  - schema validation before execution (SQL AST validation, JSON schema checks)
- Separate untrusted content from system instructions; label retrieved text as untrusted.
- Redact secrets/PII before prompts; define retention and deletion policies.

7) Evaluation in CI (App Eng + Product)
- Maintain a fixed evaluation set (50–200 representative tasks).
- On every change (prompt/model/router), record:
  - task success rate
  - cost/task
  - p95 latency
  - policy/safety outcomes
- Block deploys that regress cost/task or p95 latency beyond thresholds (e.g., +10%).

8) Pricing & finance alignment (Founder + Finance)
- Decide how variable compute maps to revenue:
  - credit-based usage
  - plan limits (“AI included up to X tasks/month”)
  - paid add-ons for premium quality/latency
- Review monthly: gross margin impact, provider mix, and growth sensitivity to cost.

Weekly operating rhythm (15 minutes)
- Review 5 charts: cost/task, p95 latency, premium route %, retry rate, cache hit rate.
- Pick one lever to improve this week: routing thresholds, budgets, caching, or prompt length.
- Document changes with versions so incidents are replayable.