A capability of the Enterprise AI Platform that keeps enterprise AI spend predictable and attributable as adoption grows — without trading away the quality the spend was buying.
The first AI product costs little enough that nobody measures it. By the fifth, the invoice is material, nobody can attribute it to a team or a workflow, and the instinct is to slow adoption down. That is the wrong lever: the issue is not that AI is used too much, it is that nothing in the path from request to invoice was designed to be controlled.
Cost control belongs at the platform layer, where it applies to every team and every AI product by default. Applied there, efficiency is something teams inherit rather than something each one has to implement, and spend can be reported against the products and workflows that generated it.
The constraint that matters is quality. Any efficiency measure can save money by producing worse output, so measures are validated against agreed quality baselines before they are applied broadly. Cost and quality are reported side by side for the same reason.
Each workload runs on a model appropriate to its complexity, latency and quality requirements, without teams tuning that themselves.
The platform avoids paying twice for work it has already done.
Models process the information a task requires rather than everything available.
Cost ownership, unit economics and governance across AI products and teams.
For self-hosted workloads, better utilisation of the infrastructure you are already paying for.
Spend measured alongside latency, accuracy, safety and business outcome.
When spend is predictable and attributable, the answer to a new AI use case stops being “wait until we understand the bill”.
Cost per user, per workflow and per product — the numbers a business case needs and most organisations cannot produce.
Efficiency measures are validated against quality baselines, so savings do not quietly arrive at the expense of the output.
Budgets, quotas and policy enforce the limits that would otherwise be negotiated in a monthly review after the money is already spent.
Because it only works if it applies everywhere. Efficiency measures negotiated use case by use case are inconsistent and quickly abandoned. Applied at the platform layer, they cover every team and every AI product by default, and spend can be attributed rather than estimated.
Not if it is measured. Efficiency measures are validated against agreed quality baselines before they are applied broadly, so a change that saves money but degrades output is caught rather than shipped. Cost and quality are reported together for exactly this reason.
By product, team, workflow, tenant and model. That attribution is the precondition for control — budgets, quotas and showback all depend on knowing who incurred what, and most organisations discover they cannot answer that question until they try.
Yes. Self-hosted inference has a different cost shape from metered APIs — infrastructure utilisation rather than per-token charges — and the platform accounts for both, including the comparison between them for a given workload.
Yes. Budgets, quotas and policy-based controls can alert, throttle or block, per team, product or tenant. Which of those is appropriate is a decision per workload rather than a global setting.
It is a capability of the Enterprise AI Platform rather than a standalone tool, because attribution and enforcement both depend on platform-level telemetry. It deploys wherever the platform does: self-hosted, managed SaaS or hybrid.
A reusable platform for developing and operating enterprise AI solutions.
Orchestration of AI-driven workflows that carry real work to completion, safely.
Search and grounded, source-cited answers across your organisation's own information.
The cloud, delivery and security foundation the platform and your own workloads share.
Talk to us about AI Cost Optimization — what you are spending, where it goes, and which controls are worth applying first.