LLM Cost Optimization Hub
Reduce cost, keep quality: unit economics + eval proof
This hub focuses on unit economics: token and context optimization, routing, caching, batching, pruning, cost monitoring. Optimize cost while keeping quality—with eval to prove it.
Common ROI & cost pains
Tokens, context, retries, tools
Know where cost comes from before optimizing. Inference cost optimization starts with attribution.
Input tokens
Prompt + system + context. Long context = high cost. RAG retrieval often over-fetches.
Output tokens
Generation length. Verbose outputs, retries, tool-call chains add up.
Retries & failures
Failed requests still cost. Timeouts, rate limits, errors trigger retries.
Tool calls
Each tool invocation = extra round-trip. Multi-step reasoning multiplies cost.
Reduce token usage without losing accuracy
Token optimization patterns. Prompt optimization measurable via eval. When longer context reduces accuracy—right-size it.
Context compression
Summarize, truncate, or chunk smarter. When longer context reduces accuracy—don't blindly add tokens. Right-size context per query.
Prompt optimization (measurable)
Shorter prompts, clearer instructions. A/B test with eval—prove quality holds. Prompt optimization measurable via eval suite.
Retrieval pruning
Reduce top-k, filter irrelevant chunks. Fewer tokens to model without losing recall (measure it).
Output constraints
Max tokens, structured output (JSON), stop sequences. Prevent runaway generation.
Route to GPT-4 only when needed (with eval proof)
Model routing policies: small vs large. Prove quality holds with eval before rolling out.
Route to small model first
Use cheaper model for simple queries; escalate to GPT-4 only when needed. Classify by complexity, intent, or confidence.
Eval proof
Don't guess. Run eval suite before/after routing change. Prove quality holds or tradeoffs are acceptable.
Fallback policy
When to escalate: low confidence, specific intents, user tier. Document and version routing rules.
Cost per task
Measure cost per ticket resolved, cost per successful completion. Route to minimize cost per outcome, not per request.
Economics: caching reduces cost
Response cache, prompt cache, batching. Throughput economics: better utilization, lower cost per token.
Response cache
Cache identical or near-identical queries. High hit rate on FAQ, repeated intents. Reduces inference cost directly.
Prompt/context cache
KV cache, prompt caching. Reuse encoded context across requests. Caching reduces cost for long-context workloads.
Batching
Batch requests where latency allows. Throughput economics: higher utilization, lower cost per token.
Throughput economics
Higher throughput = better GPU utilization = lower cost per request. Balance with latency SLOs.
Scorecards, attribution, alerts
Cost per request, per outcome. LLM unit economics: tie cost to business results.
Cost scorecards
Dashboard: cost per request, per user, per use case. Token breakdown (input vs output). Trend over time.
Attribution
Which model, which prompt template, which cohort drives cost? Attribute to make optimization decisions.
Alerts
Alert on cost spikes, anomalous token usage, budget burn rate. Catch regressions early.
Unit economics
Cost per successful task, cost per ticket resolved. LLM unit economics: tie cost to business outcome.
For execs: cost per outcome, not per request
Cost per ticket resolved. Before/after with eval proof. Tradeoff transparency. Budget and forecast.
Cost per outcome
Don't report cost per request. Report cost per ticket resolved, cost per conversion, cost per successful task.
Before/after with eval
Show optimization didn't hurt quality. Eval suite proves it. Exec-ready: 'We cut cost 40% with no quality drop.'
Tradeoff transparency
When you trade quality for cost, say so. Document acceptable tradeoffs. Avoid surprises.
Budget and forecast
Project cost at scale. Model growth, traffic, and unit cost. Help execs plan.
Differentiator
Optimize cost but keep quality—with eval to prove it. No guesswork. Before/after metrics, regression gates, exec-ready narrative.
Deep dives on cost optimization
Production-first, measurement-first. What we actually change—and how we prove it.
Cost pillars need unit-economics proof, not just optimization advice
These proof assets show the exact narrative buyers want on a cost pillar: spend decomposed, quality held, and an artifact leadership can use in the next budget review.
Need cost optimization with quality proof?
We help teams reduce LLM cost while proving quality holds—with eval suite and before/after metrics.
Start → Fix → Govern
Enforce the Audit → Sprint → Retainer ladder
Enterprise outcomes require a baseline, shipped fixes, then governance. This is the shortest path to measurable quality, controlled cost, and regression prevention.