Most companies pay far too much for their AI: the wrong model on simple tasks, no caching, no batch processing. We audit your usage, find the leaks and cut your bill — with no loss of quality. You only pay on the savings we deliver.
🔍 Free FinOps audit See the 5 levers →⚡ The essentials in 30 seconds
AI FinOps (or LLM FinOps) is the discipline of controlling and reducing the cost of generative AI in the enterprise. AI is billed on usage, per token (one token ≈ ¾ of a word). As a result, as soon as your agents, chatbots or workflows process volume, the bill climbs fast — and most often for the wrong reasons.
Here are the 5 leaks we find on almost every account we audit:
The first source of savings comes from the choice of model, because the price gap between top and bottom tier is massive. Here are the real orders of magnitude (price per million tokens, Anthropic's Claude models as an example — the logic is identical with OpenAI GPT and Mistral):
| Model | Input price /1M tokens | Output price /1M tokens | Best-fit use |
|---|---|---|---|
| Claude Opus (premium) | $5 | $25 | Complex reasoning, code, hard agents |
| Claude Sonnet (balanced) | $3 | $15 | Everyday production, good value for money |
| Claude Haiku (fast) | $1 | $5 | Extraction, classification, reformatting |
| Open model (Llama / Mistral / Qwen via aggregator) | ~$0.20 to $0.90 | ~$0.20 to $0.90 | "Commodity" volume at very large scale |
Moving an extraction task from the premium model to the right small model means dividing its cost by 5 for an equivalent result. Adding caching and batch processing on top compounds the saving further. We track these prices continuously in our AI FinOps Barometer.
With dozens of models released every month, choosing the right one is far from intuitive. Public benchmarks, for their part, don't reflect your real use case. So most teams use a single premium model by default — and massively overpay.
Our approach: we set up per-task routing that automatically sends each request to the cheapest model capable of handling it. Concretely:
You no longer have to know which model to use: you delegate the decision to us, and we keep it up to date with every new release. That's the whole point of our monthly watch.
Each lever is measurable and reversible. Above all, we never touch what your users see — only the back-end plumbing.
The biggest lever. Each task is routed to the smallest model that does the job. Simple tasks no longer pay premium rates. Typical saving: 50 to 80%.
Repeated context (large system prompt, reference documents) is cached and billed up to 90% less on each reuse.
Everything that isn't real time (enrichment, mass classification, reporting) goes in a batch and costs half price.
Structured outputs, bounded responses, non-redundant context. Since output costs up to 5× input, controlling it directly cuts the bill.
For large "commodity" volumes, switch to Llama / Mistral / Qwen via an aggregator — a fraction of the price of proprietary APIs, at equivalent quality.
Beyond tokens: spot Copilot / ChatGPT / Claude seats paid for but unused, and measure real adoption. Direct waste to recover.
Here is a typical scenario for a tech company spending €4,000/month on AI APIs without ever having optimized — an extremely common profile.
| Consumption item | Cost before | Lever applied | Cost after |
|---|---|---|---|
| Extraction / classification (high volume) | €1,600 | Routing → small model | €320 |
| Agent with large system prompt | €1,200 | Caching | €350 |
| Enrichment / mass reporting | €800 | Batch processing | €400 |
| Text generation (long outputs) | €400 | Bounded outputs | €250 |
| MONTHLY TOTAL | €4,000 | — | ≈ €1,320 |
The saving therefore represents ≈ €2,680/month, i.e. more than €32,000/year, at identical perceived quality. And the higher the starting bill, the greater the absolute gain.
Model prices, new releases, and above all: what it changes for your bill and your routing. The watch that turns AI news into concrete cost decisions. Free, one email a month.
Subscribe to the Barometer →AI FinOps follows exactly the trajectory of cloud FinOps: when cloud bills exploded, an entire discipline was born to control them — multi-billion acquisitions, certifications, a dedicated profession. LLM spending is now following the same curve, a few years behind.
Companies that get ahead early therefore gain an edge: they install a culture of AI cost control before the bill becomes a major line item. Acting now means positioning yourself at the right point in the cycle.
AI FinOps delivers the most value to companies billed on usage (per API, per token), not simple subscribers. Here are the three profiles where the saving is greatest.
These products run AI on every user action. Consequently, cost per user explodes as the base grows. Routing and caching are the most profitable here.
Agencies that resell AI automation see their margin eaten by API costs. Indeed, optimizing consumption directly improves their profitability — or lets them lower their prices.
These volumes are perfectly suited to batch processing and open models. Moreover, they are often the least monitored items, so where the biggest leaks are found.
Typical scenarios. Detailed quantified references provided on request as part of an audit.
A structured method, designed to prove the saving before any commitment: you see the figure before you decide.
We analyze your real consumption: which models, what volumes, which items. You leave with a precise costing of the achievable saving, item by item. No commitment.
Our team implements the validated levers: routing, caching, batch processing, model substitution. All with no visible change for your users.
We keep your AI cost at the optimal level continuously, via a dedicated layer and monitoring. As model prices change constantly, this watch keeps your bill at the floor over time.
Our conviction is simple: if we don't save you anything, you owe us nothing. That's why we offer shared-savings pricing rather than a blind fixed fee. The audit is free, and our interests are perfectly aligned with yours.
Our France team scopes the audit and the architecture. You talk to experts who understand your stack and your legal context.
Our Madagascar team implements the re-architecture. You get quality execution at an optimized cost.
We're paid on the savings delivered. The risk is on our side, not yours.
Beyond optimization, we are a dev and AI automation studio. We can implement, maintain and evolve your entire AI stack.
We don't sell any particular model. We always choose the most cost-effective for you: OpenAI, Anthropic, Mistral or open source.
The initial optimization, then a continuous watch that keeps your cost at the floor as the AI market evolves.
Every need is different. We therefore offer several modes, from the free audit to continuous managed optimization.
Diagnosis of your AI bill
Paid on what we save you
France lead + Madagascar team
AI cost kept at the floor continuously
AI FinOps is the discipline of controlling and reducing the cost of generative AI in the enterprise (OpenAI, Claude, Mistral API calls). Concretely: route each task to the cheapest suitable model, cache, batch, and measure spending — without degrading quality.
On a company that has never optimized, we typically see a 50 to 70% reduction, at identical perceived quality. The more intensive and unoptimized the usage, the greater the gain.
It depends on the task. For simple tasks (extraction, classification), a small model like Claude Haiku (~$1/M tokens) or an open-source model (Mistral, Llama) is more than enough. Premium models (GPT-4, Claude Opus, ~$5–25/M) are only justified for complex reasoning. The right reflex: route each task to the cheapest one that does the job.
No. We only use a cheaper model where it gives an equivalent result. Complex tasks stay on premium models. Your users see no difference — only the bill changes.
The audit is free. We offer shared-savings pricing: we only earn if you save. Fixed-fee or time-and-materials formats are also possible.
Not necessarily. Most of the savings come from how you use the models, not the provider. For some tasks, switching to an open-source model can make sense, only if the gain is real.
Below a few hundred euros a month, it's not worth the effort. As soon as your monthly bill runs into thousands of euros, optimization becomes very profitable. The free audit determines this immediately.
Yes. Beyond per-token cost, we also optimize licenses: spotting Copilot, ChatGPT or Claude seats paid for but unused, and measuring your teams' real adoption. It's a frequent and easily recoverable source of waste.
To go further:
→ AI automation & custom agents · DevOps & cloud FinOps · AI FinOps Barometer (monthly watch)
Free FinOps audit: we analyze your OpenAI / Claude / Mistral consumption, identify the leaks and quantify the achievable saving. No commitment — and you only pay on what we actually save you.
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