AI Cost Optimization Checklist

AI spend grows quietly until it becomes a board question. It is optimized the way cloud spend is: make it visible per feature, then attack the largest lines in order, with quality measured at every step. This checklist covers the levers. It draws on llm api cost optimization, cost of running llms in production, and the AI total cost of ownership whitepaper.

Who should use this checklist?

Engineering owners of AI features, platform teams running the gateway, product owners, and finance partners tracking AI spend.

Is spend attributed?

1. Every request through the gateway carries feature, team, tenant, and purpose metadata.
2. Cost per request is computed from token counts and versioned pricing tables.
3. Retrieval, hosting, review time, and infrastructure costs are attributed alongside model spend.
4. Dashboards show spend by dimension with drill-down to expensive requests.
5. Computed cost reconciles to provider invoices.

Reference: how to build an ai cost dashboard.

Is caching in place?

Cache type	Applied where	Measured by
Exact-match prompt and response	Repeated identical requests	Hit rate
Provider prompt caching	Shared system prompts and context	Cached token share
Semantic caching with validation	High-volume near-duplicate queries	Hit rate and quality sample
Embedding cache	Repeated content and queries	Hit rate
Retrieval result cache	Popular queries	Hit rate and freshness

Invalidation is keyed to prompt, content, and model versions. Reference: what is prompt caching.

Is model routing by task?

1. Logical models map tasks to the cheapest model meeting quality thresholds.
2. Simple classification, extraction, and routing use small models.
3. Escalation to larger models occurs on low confidence or complexity, not by default.
4. Routing decisions are validated by evaluation per task.
5. Provider pricing changes trigger routing review.

Reference: what is an llm router and when to use small language models.

Is context disciplined?

1. Retrieval returns reranked, bounded chunks, not whole documents.
2. Conversation history is summarized and truncated deterministically.
3. System prompts are trimmed of redundancy and shared via caching.
4. Output length is bounded per task.
5. Tool results are summarized with references for large payloads.
6. Context sizes are monitored per feature with alerts on growth.

Reference: context engineering explained and what is a context window.

Are agent loops and retries bounded?

1. Iteration, tool call, token, cost, and time budgets per task are enforced.
2. Loop detection terminates repeated states.
3. Retries are bounded with backoff; schema failures do not retry indefinitely.
4. Multi-agent systems are justified by measured need, not defaults.

Reference: how to build a multi-agent system.

Is non-interactive work batched?

1. Workloads without real-time needs use batch processing at lower cost.
2. Scheduling shifts batch work to off-peak where pricing or capacity benefits.
3. Embedding and indexing run in batch.

Reference: batch vs real-time inference.

Are budgets enforced?

1. Budgets per application, team, and tenant with alert thresholds.
2. Hard limits with defined degradation (smaller model, reduced features, queueing) rather than silent failure.
3. Anomaly detection on spend spikes, context growth, and retry storms.
4. Alerts route to feature owners with offending requests linked.

Reference: how to build an llm gateway.

Is human review effort falling?

1. Review volume per workflow is tracked by autonomy level.
2. Autonomy graduation criteria are defined and pursued with evidence.
3. Routing thresholds send only uncertain and consequential items to review.
4. Review interfaces minimize time per item.
5. Review cost is attributed to workflows in unit economics.

Reference: how to build a human review queue and the Digital FTE economics whitepaper.

Are infrastructure choices right-sized?

1. Self-hosting is evaluated on sustained volume and utilization against per-token pricing, including operations cost.
2. Vector and search infrastructure is sized to actual scale; reranker cutoffs bound per-query cost.
3. Idle capacity is identified and released.

Reference: when to self-host llms and vector database cost.

Is quality protected?

1. Every optimization runs through the evaluation gate before release.
2. Cost per correct output, not cost per call, is the target metric.
3. Quality dashboards sit beside cost dashboards.
4. Optimizations that raise error rates are rejected or reversed.

Reference: how to build an ai quality gate.

How should optimizations be sequenced?

Attribution first, always. Then the largest lines revealed by attribution, which are usually oversized context and unbatched or looping workloads, followed by caching and routing. Human review efficiency through autonomy graduation is the largest long-term lever. Infrastructure changes come last, on evidence.

How FISTA Solutions optimizes AI cost

FISTA Solutions builds cost control into every system: mandatory attribution at the gateway, caching and routing by logical model, context discipline, bounded agents, batch processing where latency allows, enforced budgets, review efficiency through earned autonomy, and unit economics joined with quality. The AI enablement practice delivers the gateway and cost platform, AI agents are budgeted by design, and forward deployed engineers work with your finance and engineering teams. The record behind the approach is 150+ projects with 47% average efficiency gains.

To review AI spend against this checklist, message FISTA on WhatsApp, or read how to reduce ai costs for the practices in depth.