The 25 percent guarantee
If the teardown cannot identify at least 25 percent in credible savings, it is free. Unmetered LLM usage almost always hides more than that.
AI Cost Engineering
Your AI bill is an engineering problem.
The flat-rate era of AI pricing is over. We measure what every feature and user costs in tokens, then cut the bill with routing, caching, and right-sized models. You keep the product. You lose the waste.
THE PROBLEM
The Subsidy Ended in June 2026
For two years, flat-rate subscriptions quietly absorbed the real cost of inference. That stopped, on a schedule you can point to.
Flat rates became meters
GitHub Copilot moved every plan to usage-based credits on June 1, 2026, and heavy users reported bills jumping from $50 to $3,000. Anthropic split subscription usage into pools two weeks later. Budgets built on flat-rate assumptions broke in a single month.
Nobody knows the unit cost
Most AI products cannot answer a simple question: what does one active user cost us per month? Without per-feature and per-user metering, pricing is a guess and margin is a hope.
Everything runs on the expensive model
Routing every request to a frontier model is like running every query on your biggest database. Most requests do not need it, and the price gap between model tiers is 10x or more.
Self-hosting hype cuts both ways
Open-weight models on your own GPUs beat API pricing only above real volume thresholds, and the hidden operations cost is a multiple of the GPU price. Moving too early wastes money. So does moving too late.
Why Partner With Us
Measured First, Then Engineered
We do not start with opinions about models. We start by metering what you actually spend, then change what the numbers justify.
FinOps roots
We come from cloud cost optimization. Token bills behave like cloud bills did a decade ago: unmetered, untagged, and full of waste. The same discipline applies.
We run our own inference
Our own AI products run on the routing and caching patterns we sell, retuned as models and prices change.
Vendor-neutral
We do not resell any model provider or GPU cloud. If the answer is "stay on the API, it is cheaper", that is the recommendation you get.
Our Process
How We Cut an AI Bill
A paid teardown produces the plan and the numbers. The sprint implements them. Both are fixed price.
1
Teardown
One to two weeks, credited into the sprint. We instrument your LLM calls and break the bill down by feature, user tier, and model, ending in a savings plan with numbers attached.
2
Quick Wins
Caching, prompt slimming, output limits, and the obvious model downgrades. These usually pay for the engagement on their own.
3
Routing
A gateway that sends each request to the cheapest model that can do the job, with fallbacks and quality checks. Frontier models handle the fraction that needs them.
4
Right-Sizing & Batch
Background work moves to batch pricing and smaller models. Interactive requests keep their latency; overnight jobs take the discount.
5
Self-Host Where It Clears the Bar
If a workload clears the volume threshold, we deploy open-weight models on your cloud with vLLM, with the operations burden priced in honestly.
6
Meter It for Good
Dashboards for cost per user, per feature, and per model, with alerts before overruns instead of after. Your bill stops being a surprise.
Technology
Our Toolkit
We use best-in-class tools.
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Next Steps
Find Out What Your AI Actually Costs
Our proven migration framework delivers measurable results. By aligning technical execution with business goals, we ensure your cloud journey accelerates innovation, reduces costs, and minimizes risk—turning your infrastructure into a competitive advantage.
Get Started 01
Book a Bill Review
Free, 30 minutes. Bring last month's invoice and we tell you where we would look first.
02
Run the Teardown
One to two weeks, credited into the sprint. You get the breakdown and the savings plan either way.
03
Cost Sprint
Two to four weeks implementing the plan, fixed price quoted from the teardown findings.
FAQ
Frequently Asked Questions
Common questions about this service.
The teardown is credited into the sprint, and sprints are a fixed price agreed before we start, based on how many surfaces we touch. And the guarantee holds: if the teardown cannot identify at least 25 percent in credible savings, the teardown is free.
Not if routing is guarded properly. We measure output quality on your real tasks before and after every change, and anything that regresses goes back to the stronger model. The point is to stop paying frontier prices for work a smaller model does identically.
Usually not first. Managed APIs win below roughly 20 million tokens a month once you price in operations, and per-token prices at fixed capability keep falling. Above that threshold it depends on workload shape, privacy requirements, and latency. We give you the break-even math for your usage, not a slogan.
Yes. Coding assistants and internal agents are often the fastest-growing line item since the 2026 repricing. We set up usage visibility, sensible plan structures, and caching proxies where they help.
Yes. Credits run out, and commitments renew at whatever your usage looks like then. Optimizing now means your next contract is negotiated from a lean baseline instead of an inflated one.
Provider dashboards or billing exports to start, and ideally a proxy in front of your LLM calls for real metering. Read-only until we agree on changes.
The big cuts are. But models and prices change monthly, so most clients keep a light retainer for retuning; it is part of the Fractional Platform Team service.
Find Out What Your AI Actually Costs
Book a 30-minute bill review. Bring last month's invoice and we will tell you where we would look first.