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

"

Two weeks before our seed round we still did not have a defensible model. Their fractional CFO rebuilt our three-statement forecast, pressure-tested the assumptions, and walked me through every line before the partner meeting. We closed 1.4M on the terms we wanted. The investor specifically called out how clean the financials looked compared to the last five decks she had seen.

Hannah B.

"

We were paying three agencies and a lifecycle freelancer to argue over attribution. RevoraOps absorbed all of it in 30 days, killed our worst-performing Meta ad sets, and rebuilt the welcome flow from scratch. CAC dropped 31 percent in the first full month. Honestly the relief of having one weekly call instead of four was worth it alone.

Megan W.

Proof