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Service 02

AI Cost Optimization

Cut your AI costs without changing what the system does.

Up to 75%Reduction in AI costs
Where the cost goes−75%Per feature

Service 02 · AI Cost Optimization

A system already in production, running cheaper, with nothing different for the people using it. Every number is measured on a matched window in production, against a baseline taken before anything is touched, which is the difference between a saving and a story about one.

OutcomeThe same system in production, running cheaper, with nothing different for the people using it.

Who AI Cost Optimization is for

03 profiles
  • 01 / 03

    Engineering leaders watching LLM cost growth outrun usage growth.

  • 02 / 03

    Teams running a production stack where a premium model is called at every stage, whether or not that stage needs one.

  • 03 / 03

    Founders whose unit economics only work if cost per call comes down, and who cannot afford a user-visible change to get there.

How AI Cost Optimization works

04 steps
  1. Step 01

    Measure before touching anything

    A matched-window baseline, in production

    Instrument the current pipeline and establish a matched-window baseline: cost per call, cost per feature, and where in the path the money actually goes. Every later number is reported against that baseline, in production, not on a synthetic replay.

  2. Step 02

    Optimise the pipeline before the model

    Eliminating calls captures most of the saving

    Restructure the processing path first. Eliminating calls captures most of the saving; swapping models would only have made those same calls cheaper. On the voice AI stack this meant collapsing a fourteen-plus-step backend into a three-stage summarisation pipeline.

  3. Step 03

    Change models only where quality holds

    Stage by stage, each gated on an eval

    Open-source and smaller models are introduced stage by stage, each gated on an eval that shows output quality is unchanged. Where quality moves, the premium model stays. Caching is corrected and then monitored, not set once and assumed.

  4. Step 04

    One gateway, and the saving stays saved

    Cost per feature stays visible after handover

    Every AI call routes through a single gateway, so cost per feature is visible after handover and the reduction does not quietly erode as the product changes.

What you get

05 deliverables
  • D-01

    A matched-window before/after measurement of cost per call and cost per feature, taken in production.

  • D-02

    A restructured pipeline with the redundant stages removed.

  • D-03

    One AI gateway carrying every call, with prompt caching configured and monitored.

  • D-04

    Model-by-stage routing, with the eval evidence behind each substitution written down.

  • D-05

    A cost dashboard with per-task and per-feature breakdown, and budget alerts, that your team keeps.

Where we've shipped this

01 engagements

Frequently asked questions

04 questions

Will users notice anything?

No. That is the constraint the whole engagement is designed around, and it is why the pipeline gets restructured before any model is touched. On the voice AI engagement, running cost fell 75.6% on a matched window with no product change.

Where do the savings actually come from?

Mostly from calls that stop happening. Restructuring the pipeline captured the bulk of the saving before model migration began. Correcting prompt caching alone accounted for roughly 26% of the reduction. Open-source models then covered the stages where evals showed quality held. A further ~23% opportunity in speaker processing was validated and scoped separately.

How do you control LLM cost without hurting quality?

Smaller models for the easy work, bigger models gated behind cost-aware routing. Caching for repeat queries. Prompt audits to remove unnecessary context. Token budgets per request and per agent run. Cost-tracking dashboards so the team sees the bill move in real time. Cost discipline is design, not panic-cutting after the invoice arrives.

What stops the cost creeping back after you leave?

One gateway for every AI call, with cost attributed per feature. When a new feature lands, its cost shows up on the same dashboard the same week, so a regression is visible in days rather than at the next invoice. The gateway is in your accounts; nothing about it depends on us still being there.

48-hour reply

Let's build something that actually works.

Tell us where you are and what you need. We’ll come back with a clear, honest plan within 48 hours.