Visibility
One view of spend across model providers, cloud services, GPUs and tooling.
Services / Run
Know what your AI costs.
Know what it earns.
AI spend is spread across models, infrastructure, teams and experiments. We give you visibility, attribution and optimization, so cost becomes something you manage rather than something you discover.
- Best when
- AI spend is growing and nobody can explain it by team or use case.
- You bring
- Billing data, access to usage logs and the people who own the workloads.
- You leave with
- A cost baseline, an owner for each cost we can trace and a ranked set of savings and controls.
- Shape
- A baseline assessment, then ongoing governance if you want it.
You might be here if
Does any of this
sound familiar?
- The bill for model APIs or GPUs doubled and nobody can say why.
- Several teams run pilots on separate accounts with separate budgets.
- Finance asks for the return on AI and engineering cannot produce a number.
- You are choosing between models and the real cost differences are unclear.
Why it needs its own discipline
Tokens are the
new cloud bill.
AI cost is driven by choices engineers make every day: which model, how long the prompt, how many retries, whether results are cached. Unlike servers, spend scales with usage and with how the system is built, so the people controlling cost and the people spending it are rarely the same people.
See the spend. Attribute it to a team, an application and a use case. Then optimize it, with quality checks so that a saving never quietly becomes a worse answer.
What you get
From a bill to a managed cost.
We work from the data you already have, then add the instrumentation that is missing.
Attribution
Costs tagged to teams, applications and use cases, so each has an owner who can answer for it.
Unit economics
Cost per document processed, per answer, per task, set against the value of the work.
Optimization that protects quality
Model selection and routing, prompt and context trimming, caching, batching and right-sizing, each tested against evaluation results before it ships.
Guardrails
Budgets, alerts, usage limits and approval paths, so spend cannot surprise you.
A review rhythm
A regular cost and quality review that brings finance and engineering into the same conversation.
How it works
One clear step at a time.
Baseline
Collect billing and usage data and build the first clear picture of what is being spent and where.
Attribute
Tag spend to teams, applications and use cases. Find the unowned costs.
Optimize
Rank savings by size and risk, test them against quality measures and apply the ones that hold up.
Govern
Set budgets, alerts and a review rhythm, so the discipline continues after we step back.
What changes
From where you are to where you want to be.
From: A bill nobody can explain
To: Spend attributed to teams and use cases
From: Cost per token
To: Cost per outcome
From: Savings that quietly hurt quality
To: Optimizations tested against evaluation
From: Surprises at month end
To: Budgets and alerts
Before you start
What to expect.
Missing logs come first.
Where usage is not recorded, we add the logging before the analysis, so the numbers rest on real data.
We start from metadata, not your customers’ data.
Cost analysis begins with usage records such as model, volume and tags, not the content of your prompts.
Questions
Common questions.
Which providers and platforms do you cover?
The major model APIs and cloud platforms, and self-hosted models. We work from your billing and usage data.
Is this only about cutting cost?
No. It is about knowing what you spend, who is accountable and what it returns. Sometimes the right answer is to spend more on something that works.
Can we start small?
Yes. A baseline assessment is a good first step. It often shows where the largest and least understood costs sit.
How do you avoid hurting quality?
Every optimization is tested against evaluation sets built from your real tasks, and the result is reviewed before it ships.
Do you need access to our prompts and data?
Usually not. We need usage records and metadata. Where deeper analysis helps, we agree access and handling first.
Understand the bill before it grows.
Tell us where your AI and cloud spend sits today. We will suggest a baseline that fits.