AI Cost Optimization

Overview

What we deliver

We audit your AI workloads and apply caching, model selection, and prompt changes to bring costs down while keeping output quality intact.

We help teams cut AI spend by finding waste in how prompts are written, how models are chosen, and how often calls are repeated. Our audit looks at every layer of your stack, including token usage per request, model mix, retry behavior, context bloat, and missed caching opportunities. We then propose changes ranked by savings and effort, from quick wins like shorter system prompts and response caching to deeper moves like routing simple tasks to smaller models and batching background jobs. We run controlled tests to confirm that quality holds, and we set up dashboards so the savings stay visible after we leave. Most engagements pay for themselves within the first month. We also build cost guardrails such as per user limits and alerting so a runaway agent or traffic spike does not result in a surprise bill at the end of the month.

Fit Check

Built for teams like yours

Who it's for

Companies with rising AI bills
Startups extending runway
Enterprises scaling AI usage
Product teams under budget pressure
Finance and engineering leads

Pain points we solve

Unpredictable monthly LLM bills
Overuse of premium models for simple tasks
Repeated calls for the same inputs
No visibility into per feature cost
Token bloat in prompts and context

What's included

Capabilities

Everything we cover in this engagement.

Cost audit and breakdown
Prompt compression
Response and embedding caching
Smaller model substitution
Batching for non interactive jobs
Per user and per feature limits
Cost dashboards
Alerting on spend spikes

How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Audit

Pull usage data and map cost by feature.

02

Prioritize

Rank changes by savings and effort.

03

Implement

Apply changes in a staging environment.

04

Validate

Confirm quality holds with test sets.

05

Monitor

Deploy dashboards and alerts.

What you get

Deliverables & outcomes

What you get

Cost audit report
Optimization backlog
Updated prompts and code
Caching layer
Cost dashboard
Alerting setup

Outcomes you can expect

Lower cost per active user
Reduced monthly LLM spend
Better cost visibility by feature
Protection from spend spikes
Maintained or improved quality

Timeline

3 to 6 weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

Helicone, Langfuse, Redis, OpenAI usage API, Anthropic usage API

KPIs we track

Cost per request, cost per user, cache hit rate, token per response, monthly spend

Client stories

What clients say

"

We had 14 cornerstone pages stuck on page two for 18 months. Their SEO crew rewrote the internal linking, cleaned up our schema, and shipped 22 supporting briefs over a quarter. Eight of those pages broke top three by month five. Organic pipeline went from a trickle to our second-largest source. Felt like watching interest compound.

James T.

"

We had been prototyping an AI quoting agent for nine months and could not get it past demo quality. They came in, scoped a real eval set, swapped our retrieval layer, and added guardrails for the edge cases that kept burning us. Went live in seven weeks. It now handles 41 percent of inbound quote requests without a human touching them.

Kyle A.

Proof