LLM Cost Optimization — Cut Your AI Bill 40–70%

Who this is for

• Your LLM API bill is past $5K/month and finance is asking pointed questions about runway impact.
• You raised a seed round and need to stretch runway — cutting inference cost in half is a real budget lever you control.
• You're paying GPT-4 or Sonnet prices for work a Haiku or 4o-mini could do, but you're afraid quality will drop if you switch.

What you get

• ✓Full cost audit: per-endpoint token usage, cost-per-conversation, top 5 spending hotspots.
• ✓Semantic cache layer that de-duplicates near-identical queries (typically 25–40% savings alone).
• ✓Prompt compression across your top 10 templates — fewer tokens, same output.
• ✓Model-routing logic that sends easy queries to cheap/small models and hard queries to frontier models.
• ✓Eval harness that proves nothing regressed — I won't ship a savings claim without a test score.
• ✓Before/after dashboard showing dollars saved per week.
• ✓Runbook for your team so the savings don't decay after I leave.

How I cut LLM cost

1. Week 1

   Cost audit

   Instrument the app, pull 7 days of traces, produce a 1-page report: top spend hotspots, expected savings per intervention, estimated engineering cost.

2. Week 2

   Quick wins

   Semantic caching and prompt compression — almost always the biggest unlock. Ship behind a feature flag.

3. Week 3

   Model routing

   Classifier routes easy queries to Haiku / 4o-mini, hard queries to Sonnet / 4o. Eval-gated rollout.

4. Week 4

   Hardening + handoff

   Dashboards, runbook, monitoring. Deliver the final savings report.

What I build with

LLM cost optimization is a precise discipline, not a heuristic. My standard three-layer stack: (1) pgvector-backed semantic caching with ~31% hit rate and sub-50ms latency, (2) prompt compression that prunes average prompt length by ~38% using lazy context loading, (3) intent-aware model routing that redirects ~62% of queries to cheaper models (Haiku, 4o-mini) while reserving expensive models for complex reasoning. Teams that implement systematic caching + multi-model routing cut average monthly API spend ~65% without dropping CSAT — but only if it's eval-gated. Without evals, you ship silent quality drops.

Cost optimization wins

How pricing works

What drives the price up

• Size of your current bill (bigger bill = bigger absolute savings = more work worth doing)
• Number of LLM endpoints and templates
• Whether I'm working alongside your team or solo
• Standalone vs bundled into a Retainer

Week-long cost audits start at $3,500 (same price as a Production Audit because the work is structurally similar). Full rollouts with caching, routing, prompt compression, and dashboards scale to $15K. On engagements over $10K I'll offer a savings-indexed fee: if I don't hit a pre-agreed % reduction, you only pay the floor.

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