CTOs and VPs of Engineering watching an AI/LLM bill grow faster than usage; CFOs and finance leaders who need AI spend they can forecast and defend, not one that only ever surprises them; and CEOs weighing whether the AI feature actually pays for itself. If your inference, API, or GPU spend has outrun the plan — or you simply can't explain cost per request, per user, or per feature — there is usually real money on the table.
Where the AI Bill Leaks
The usual suspects — and what each is worth
Token bloat
Long prompts, whole documents, and full chat history sent on every call — when the task needs a fraction of it. Trimming context and prompts is often the fastest, safest win.
Wrong-size models
Calling a frontier model for work a smaller, cheaper one does just as well. Right-sizing per task — and routing the hard cases up — cuts spend without cutting quality.
No caching
Paying full price for repeated or near-identical requests. Semantic and exact-match caching turns those into near-free responses that are faster too.
Runaway loops & retries
Agent loops, retries, and fan-out that silently multiply calls. Guardrails and budgets stop a single request from quietly costing 10x.
Idle GPU & inference
Over-provisioned GPUs and always-on endpoints running at low utilisation. Right-sizing, autoscaling, batching, and quantisation reclaim the waste.
No unit economics
Nobody can say what a request, a user, or a feature actually costs. Instrumenting spend per unit turns a surprising bill into a number you can forecast.
How an Engagement Works
1 · Audit
I map your AI spend against your architecture and usage — where the tokens, calls, and compute actually go — and rank the leaks by what each is worth to fix. You get an honest opportunity estimate, not a sales number.
2 · Optimise
I implement the highest-ROI fixes with production discipline — caching, right-sizing, prompt and context trimming, routing, batching, GPU right-sizing — each measured against a quality and latency baseline before it ships.
3 · Verify & monitor
We confirm the saving against the baseline and stand up per-request cost monitoring and budgets, so the bill stays down and the next regression is caught early — not three months later on the invoice.
Optimisation only counts if the experience holds. Every change is gated on a quality and latency baseline before it ships, so you see the saving and its impact side by side. Where a cheaper option would genuinely degrade the product, I tell you and leave it — a smaller bill on a worse product is not a win. Honest scoping is part of the value.
Cost engineering isn't a pitch for me; it's how I ship. On my own live AI product I run a semantic cache targeting roughly a 30% hit rate specifically to hold per-session AI cost under an economic ceiling, choose the smallest model that does each job, and monitor spend per request. Earlier, at CoinSwitch, I cut query latency 30% with Redis caching and a further 40% with connection pooling while holding the system stable from 600K to 10M users. Across 15+ years — Amazon Payments, Agoda, CoinSwitch — the discipline is the same: measure, engineer, verify. That is exactly what bringing an AI bill down takes.
Frequently Asked Questions
What is AI cost optimization?
Lowering what it costs to run your AI systems — LLM/API tokens, GPU and inference compute, and surrounding cloud — without hurting quality, latency, or reliability. The AI system itself is the line item being optimised.
Where does the money usually leak?
Token bloat, over-large models, no caching, runaway agent loops and retries, and idle over-provisioned GPU or inference endpoints. An audit finds which apply to you and what each is worth.
Can you cut costs without making the product worse?
Yes — that's the point. Every change is measured against a quality and latency baseline before it ships. Where a cheaper option would degrade the experience, I say so and leave it.
How much can we expect to save?
No honest engineer promises a fixed percentage sight unseen. I audit your actual usage and spend, estimate the opportunity, implement the highest-ROI fixes, and report the measured result against a baseline.
Is this for engineering or finance?
Both. Engineering gets prioritised fixes; finance and leadership get AI unit economics — cost per request, per user, per feature — they can forecast and defend.
How do we start?
Book a free call; we look at your current AI spend and architecture, find the biggest leaks, and you get an honest view of the opportunity.
Related
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AI Architecture & Design Review →
Audit an in-progress AI system for reliability, cost, and scale — with a prioritised action list.
Fractional CTO for Startups →
Part-time engineering leadership — keep cost, architecture, and delivery on track.