Keep it saved
AI FinOps: keep the saving after the project ends
Cost reductions decay. Prompts grow, traffic shifts, models get deprecated and new features ship without a budget. The retainer keeps the measurement running and the backlog of savings moving.
Outcome: AI unit economics that stay flat or improve as you grow, and a monthly number your finance function trusts.
Talk about ai finops retainerEvery AI saving decays unless something maintains it
Six months after a successful optimisation project, the bill is usually climbing again — and rarely because anyone did anything wrong. Prompts accumulate instructions after each incident. New features ship pointing at whatever model the developer had open. Traffic mix drifts as the product finds new users. A provider deprecates a model and the migration lands on the nearest larger one.
None of that is visible in a per-token dashboard until it is already expensive. It is visible immediately in cost per successful task, which is why the retainer is built around keeping that measurement honest rather than around producing reports.
What the month looks like
Continuously
Budgets and anomaly detection run against the gateway telemetry. A feature that starts costing three times what it did yesterday raises an alert the same day, not in the next invoice.
Every deploy
The evaluation gate runs on your golden sets. Changes that regress quality fail before release, which is what stops a cost optimisation from becoming a quality incident three sprints later.
Monthly
A unit economics pack: cost per successful task per feature, the trend, what moved it, and the current ranked savings backlog. Written for both audiences — engineering gets the mechanism, finance gets the number.
As required
Model deprecations, pricing changes, new provider options, and new features that need a budget and a routing decision before they ship rather than after.
What this is not
It is not a dashboard subscription. There are good AI observability products and we will happily point you at one; the retainer exists because the hard part is not displaying the number, it is deciding what to do about it and having someone accountable for doing it.
It is also not open-ended staff augmentation. The scope is cost, and the quality guardrails that protect it. If the work turns into general platform engineering we will say so and scope it separately rather than quietly billing it here.
Deliverables
What you have at the end
- Monthly unit economics pack: cost per successful task by feature, and the trend
- Budget and anomaly alerting, tuned so the alerts stay worth reading
- Golden set and evaluation gate maintenance as prompts and models change
- Model deprecation and migration handling before the provider forces it
- A standing, ranked savings backlog with each item sized and risk-rated
- A named engineer in your channel, and a monthly review with engineering and finance
Everything on that list lives in your repositories and your cloud account. Ending an engagement does not take the capability with it.
FAQ
AI FinOps Retainer: common questions
Can we start with the retainer instead of the audit?
We would rather you did not. Without the audit there is no baseline, no golden set and no attribution, so the first two months of a retainer would be spent building them at a worse rate than the fixed-fee audit charges. Start with the audit; roll into the retainer if it earns its place.
Do you offer a share of savings instead of a fee?
For engagements above a certain size, yes — a reduced fee plus a share of verified savings over an agreed baseline, measured on cost per successful task rather than on the raw invoice so the incentive stays pointed at the customer outcome. It requires a jointly agreed measurement method up front, and we will not do it without one.
What is the minimum commitment?
Three months. Below that there is not enough signal to distinguish a trend from a busy fortnight, and neither of us learns anything useful.
Who owns everything you build?
You do. Gateway, dashboards, golden sets, evaluation harness and routing policies all live in your repositories and your cloud account. Ending the retainer does not take the capability with it — that is deliberate, and it is why the retainer has to keep earning renewal.
Related
Where this leads next
AI Spend Audit
A two-week, fixed-fee forensic review of your AI bill. We instrument your calls, attribute every pound to a feature and an outcome, and return a ranked list of savings with the expected quality impact of each one.
LLM Gateway Implementation
The single OpenAI-compatible endpoint every AI call in your estate goes through. It routes, retries, caches, enforces budgets and records what everything cost — deployed in your own cloud, not ours.
Model Routing & Evaluation
The largest single lever in most AI bills is that one model serves every request. We segment your traffic by task, find the cheapest model that still passes, and prove it on your own traffic before it reaches a customer.
Find out what your AI actually costs per completed task.
Two weeks, a fixed fee, and a ranked savings plan with the quality risk of every move stated up front. If the numbers say an audit is not worth it for you, we will say so on the first call.