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TOKENTRIM
Free audit →
Token cost optimization

How much is your AI overspending?

Most companies pay 2–3× more per output than they need to. TokenTrim audits and rebuilds how you use LLMs — prompts, routing, caching, models — so every token earns its keep.

Get a free audit →Email us directly
A first read of your usage, no commitment.
Savings estimator30 seconds
Monthly AI spend$40,000/mo
Main workload
Estimated recoverable / year
$264k
Get the exact number →
Estimate based on typical audit findings. Your audit gives the real figure.
The problem

Where your tokens leak

Four places we find the waste, in nearly every audit. None of it is exotic — it just compounds, invoice after invoice.

No cost visibility

The bill arrives as one number. Nobody can say which feature, team, or customer is driving it — so nothing gets fixed, because nobody owns the number. You can't cut what you can't see.

Self-diagnosis

Sound familiar?

If you recognize three or more of these, an audit typically pays for itself within the first month.

AI spend, last quarter

+0%

$40k/mo
M1M2M3

AI bill grows faster than usage

Usage is flat. The invoice keeps climbing — nobody's sure why.

1
2
3

retry_attempt exceeded, retrying.

0callsuncapped

Retries and agent loops unmetered

No ceiling on retries, no cap on loop steps. Every failure just tries again.

prompt.md

Prompts nobody dares to touch

Written two years ago by someone who left. Nobody knows what half of it does, so nobody touches it.

default_model
One size, every task
classifysummarizetranslateextractrankdraft
up to 20× the cost some of these need

Everything runs on one big model

One frontier model handles every task — the ones that need it and the ones that don't.

Tokens loaded into context0Most of it never gets read. Stuffed in just in case.
full_chat_history.json

Context windows stuffed "to be safe"

Every call drags in the whole history, just in case. Most of it is never read.

By featureMonthly
Chat
Search
Agents
Support
Other

No per-feature cost visibility

The bill arrives as one number. Which feature drove it? Nobody can say.

Services

What we do

Three phases — visibility, optimization, and scale. Most engagements run all three.

Overview

We connect AI usage to products, teams, customers, and business outcomes so every cost has a clear source.

How we do it

MapModels, vendors, products, features, and teams
MeasureCost per task, customer, workflow, and outcome
PrioritizeSavings opportunities by impact, effort, and risk

Your engagement produces three concrete deliverables

You get

  • 01Cost Baseline

    Spend by model, product, team, and workflow

  • 02Savings Priorities

    Opportunities ranked by value, effort, and risk

  • 03Action Roadmap

    What to change, in what order, and why

Waste patterns

The waste we keep finding

The free audit shows which of these patterns are burning your budget — and what fixing them is worth.

up to
−30%
Default-model everything
Frontier models answering questions a far cheaper model handles just as well.
up to
−25%
Context bloat
Prompts hauling history and retrieval nobody reads.
up to
−20%
One-size-fits-all models
High-volume narrow tasks on general frontier models instead of fine-tuned small ones.
No client logos yet — we're earning them. The audit shows your own numbers first.
FAQ

Questions we always get

No — that's the deal-breaker we design around. Every change is gated behind evals built on your real traffic. If quality regresses, the change doesn't ship.

Paying too much per token?
Probably.

Get a free audit of your AI spend. No commitment — a clear report and a savings forecast, in one week.

Request your free audit →
or write to us: youcefbenab@gmail.com