Now taking the first cohort of audit engagements Limited slots Free audit · No savings, no fee Reconciled to raw provider invoices Targeting 40–60% reduction
$ LLM CFO Est. 2026 · AI FinOps
Managed AI cost optimization · Pay on results

Your AI bill,
cut in half.

LLM CFO is managed AI cost optimization for engineering teams spending $20K+/month on LLMs. We audit your OpenAI, Anthropic, Bedrock, Vertex AI, and Azure OpenAI spend, implement the fixes, and reconcile every dollar against your raw provider invoices. You only pay on the savings we deliver.

Targeting 40–60% reduction 3–5 week kickoff No savings, no fee
// Reconciled against every major provider & gateway
OpenAIAnthropicGeminiBedrockAzureGroqTogetherMistral
№ 01 — What we target

Targets we commit to at kickoff.

We're an early-stage service taking our first cohort. These are targets sourced from public benchmarks and our own engineering work — not customer claims.

4060%
Reduction range

Architecture-dependent. Confirmed against your invoices at audit.

35 wks
Time to savings

Kickoff → first reconciled provider invoice.

7-day
Quality SLO

Every change A/B tested 7 days minimum. Auto-rollback on regression.

$0
Audit fee

Free. No savings, no fee — performance pricing.

№ 02 — The levers

Six levers we pull, every engagement.

Most engagements combine three or four of these. The audit tells us which dominate your spend surface.

01

Model routing

Classify each request and route to the cheapest model that passes your quality bar. Reasoning goes to flagships; extraction and classification go to small fast models.

30–50%of lever scope
02

Semantic cache

Fingerprint prompts with embeddings and serve identical answers from sub-10ms cache. Similarity thresholds and invalidation tuned per feature.

20–40%of lever scope
03

Prompt compression

Audit system prompts for redundancy. Deduplicate examples. Compress retrieved context with LLMLingua-style techniques. Every change A/B tested.

15–30%of lever scope
04

Batch & async

Route background jobs to batch endpoints (up to 50% discount) with SLO-aware queueing for anything time-sensitive.

10–50%of lever scope
05

Provider arbitrage

Identical tasks often cost 2–3× more at one provider. We route by capability-per-dollar, not by the SDK your team happened to start with.

20–35%of lever scope
06

Fallback chains

Smart retries and tiered fallbacks beat worst-case over-provisioning. Maintain SLO without paying flagship prices on every call.

5–15%of lever scope
№ 03 — The engagement

Audit. Implement. Reconcile. Repeat.

Every engagement follows the same four-phase shape. Most customers are on month-end reconciliation by week five.

Week 01
Phase i — Audit

Map every dollar.

Ingest invoices and gateway logs. Baseline locked and countersigned.

Week 02
Phase ii — Plan

Rank by waste.

Optimizations ranked by dollar impact. Quality SLOs agreed per endpoint.

Weeks 03–05
Phase iii — Ship

Ship & A/B.

Feature-flagged, tested 7 days vs. baseline, graduated to 100%.

Ongoing
Phase iv — Reconcile

Signed, monthly.

Statement of Savings reconciled to raw provider invoices. Every month.

№ 04 — Pricing

We only get paid when you save.

Performance pricing, tied to your provider invoices. No savings, no fee. No multi-year lock-ins.

Free audit, then
15–25%
of verified savings · measured vs. locked baseline
  • Audit is free and credited against implementation.
  • Savings measured vs. locked pre-engagement baseline, reconciled to raw provider invoices.
  • Quality SLOs enforced; regressions auto-rollback.
  • Minimum engagement: $20K/month LLM spend.
  • Cancel for any reason; no multi-year lock-ins.
Book free audit →
Statement of Savings
Illustrative · $165K baseline · 50% reduction
Baseline AI spend$165,000
After optimization (≈50%)$82,500
Monthly savings$82,500
LLM CFO fee (20%)− $16,500
Customer keeps$66,000 /mo
$792,000
Illustrative annual net · not a customer figure
*** Mid-range example only. Your audit produces a real number. ***
№ 05 — Questions

Common questions.

Missing something? Write to hello@llmcfo.com — we respond within one business day.

How much can I reduce my OpenAI or Anthropic bill? +
Published research and our engineering work suggests 40–60% reduction is achievable for most production workloads. The biggest savings usually come from semantic caching (20–40%), model routing (30–50%), and prompt compression (15–30%). We commit to a target after the audit. If we don't deliver savings, you don't pay.
How are savings verified? +
We lock a pre-engagement baseline from your provider invoices. Each month, our reconciliation report compares optimized spend to that baseline — traffic-adjusted, reconciled to raw billing. Your finance team can audit the math.
Will optimization hurt output quality? +
Every change is A/B tested against your production baseline for seven days minimum. Regressions roll back automatically. Quality SLOs are agreed per endpoint and monitored continuously alongside cost.
What's the minimum monthly spend? +
$20K+/month on LLM APIs, or teams projected to cross that within a quarter. Below that, self-serve observability tooling (Helicone, Langfuse) is a better fit.
Are you SOC 2, GDPR, or HIPAA certified? +
Not yet — we're being honest. We're an early-stage service and don't claim certification. We follow read-only-by-default access for billing and gateway data, and can sign a mutual NDA and DPA before any engagement.
Which providers do you support? +
OpenAI, Anthropic, Gemini & Vertex AI, AWS Bedrock, Azure OpenAI, Groq, Together AI, Mistral, Cohere, Fireworks, OpenRouter, and most OSS endpoints — plus LiteLLM, Helicone, and Langfuse gateways. Multi-provider setups typically have the largest savings surface.
№ 06 — Start here

Book the audit.
Keep the savings.

Two weeks. Read-only access. We return with a full map of your spend, ranked by waste, and a baseline our platform can track against. Free. No implementation commitment.

30-min discovery · read-only by default · NDA + DPA on request · no commitment