AI Cost Optimization Services
Your AI bill is growing faster than your usage. We find out why, fix the architecture, and keep it honest.
Start a projectYour AI bill is climbing faster than your usage, and nobody on the team can say exactly why. The invoice is one number. Underneath it sits a tangle of model calls, retries, oversized prompts, and real-time work that never needed to be real-time — and none of it is labeled. That is the problem this service solves: turning an opaque, growing line item into something you understand and control.
We start with attribution, because you cannot cut what you cannot see. Once every dollar maps to a feature and a model call, the expensive decisions become obvious. From there the levers are concrete and ordered by risk: cache the work that repeats, right-size the models that are over-served, trim the context you are paying for and not using, and move batchable work off synchronous endpoints. The savings that carry no quality risk go first. The ones that touch output quality run against evals on your own results before anything ships — a cheaper system that gives worse answers is not a win, and we will not pretend it is.
The part that makes this hold: we build and run what we tune. AI cost is not a fixed problem you solve once. It drifts the moment traffic shifts, a feature launches, or a provider reprices. A one-time audit hands you a PDF and walks away while the bill quietly climbs back. We keep the monitoring in place and catch the regressions before the next invoice does — people watching the system, AI handling the cadence, accountability that does not leave when the engagement does.
Built and run, end to end.
Spend audit and cost attribution
Most teams can read the total invoice and nothing underneath it. We instrument the system so every dollar maps to a feature, a route, and a model call. You see which prompts are expensive, which users drive volume, and where retries are silently doubling spend. That breakdown is the prerequisite for every other decision here — you cannot cut what you cannot see.
Model right-sizing
A lot of production traffic runs on a frontier model when a smaller, cheaper one would pass the same eval. We test the cheaper model against your real outputs, set the quality bar, and route by task. Classification and extraction drop to a small model. The hard reasoning stays on the expensive one. The judgment about where that line sits is ours to defend, not a default we inherit from a tutorial.
Prompt and context reduction
Token cost is mostly input tokens, and input tokens are mostly context you may not need. We trim bloated system prompts, cut redundant few-shot examples, and stop stuffing whole documents into the window when a retrieved passage would do. Smaller prompts are cheaper and usually faster, and they often read better too.
Caching and deduplication
Identical and near-identical calls get made over and over in systems nobody tuned for it. We add prompt caching where the platform supports it, cache stable responses, and dedupe repeated work inside a request. For workloads with steady reuse, this is the single largest line-item drop we see, and it requires no model change at all.
Retrieval and embedding cost control
RAG bills come from three places — embedding generation, vector storage, and the context you pass downstream. We right-size the embedding model, cut re-embedding churn, and tighten how many chunks actually reach the LLM. Retrieving less, but retrieving the right thing, lowers both the vector bill and the generation bill at once.
Batch and async routing
Not every call needs to happen in real time. Work that can wait — overnight enrichment, bulk classification, report generation — moves to batch endpoints and async queues that price well below synchronous traffic. We separate what is genuinely interactive from what only looks urgent, and route accordingly.
Questions, answered.
How much can you actually cut from our AI bill?
It depends entirely on what you started with, and we will not quote a percentage before we have seen your traffic. The largest, safest wins almost always come from caching reusable calls and moving over-served traffic to smaller models. The audit tells us which of those apply to you and how big each one is. If the honest answer after the audit is 'your system is already lean,' we will tell you that instead of inventing savings.
Will cutting cost make the output worse?
That is the real risk, and it is why right-sizing runs against evals on your own outputs rather than on vibes. We set a quality bar first, then find the cheapest configuration that clears it. A change that saves money but degrades the result does not ship. Some savings — caching, prompt trimming, batch routing — carry essentially no quality risk at all, so we sequence those first.
How do you find where the spend is going?
We instrument the system so each call is tagged by feature, route, model, and outcome, then read the cost against that breakdown. Aggregate invoices hide the answer; attribution exposes it. Often the surprise is a single endpoint, a runaway retry loop, or a debug path left in production — things that never show up until the spend is split apart.
Do we have to switch AI providers?
Usually not. Most of the savings live inside how you already use your current provider — model selection, caching, prompt size, batching. We are not here to sell you a migration. If a different provider or a self-hosted model genuinely changes the math for your workload, we will show you the comparison with real numbers, but that is a finding, not a foregone conclusion.
Is this a one-time audit or do you stay on?
We do both, and we prefer to stay. A one-time audit is a snapshot; AI cost drifts the moment traffic patterns shift, a new feature ships, or a provider changes pricing. Because we build and run the systems we tune, we keep cost monitoring in place and catch regressions before they reach the next invoice. A report you file away does not stop the bill from climbing again.
We are an LA company — does working locally matter for this?
For the work itself, no — this is engineering, and we do it wherever your systems live. But we are a Los Angeles agency and we work in person with mid-market teams here when it helps. What matters more than geography is that we operate what we optimize, so the savings hold instead of decaying after the engagement ends.