LLM Cost Audit

Inference economics

yapayzeka.co LLM Cost Audit

Cut Your Inference Bill — Not Your Quality

Reduction Levers

0/7 applied

Prompt Caching−30%

Cache the static system + context prefix on every call.

Model Routing−26%

Send easy calls to a smaller model; reserve the frontier model for hard ones.

Semantic Response Cache−12%

Serve repeated / near-duplicate questions from cache — zero tokens.

Output-Token Discipline−10%

Schemas + max_tokens + concise instructions. Output tokens cost the most.

RAG Context Trimming−11%

Retrieve fewer, better chunks. Stop stuffing the whole knowledge base.

Batch & Off-Peak−7%

Route non-urgent jobs through the batch API at a discount.

Open-Model Offload−14%

Move high-volume, low-complexity workloads to a hosted open model.

Current Run-Rate

$42,800

/ month

1.9B tokens / mo · blended $22.50 / 1M

↓ 0%

cut from baseline

saved per year

Baseline$42,800

Optimized$42,800

Cache hit rate

Every call is paying full price for tokens it has seen before.

No optimization applied yet — this is the bill as it runs today. Flip levers on the left to see the cut.

How we cut itToken & cost auditPrompt cachingModel routingSemantic cacheRAG cost tuningUsage monitoring