As generative AI scales across your organisation, we bring visibility, governance and optimisation to token spend.
If any of these sound familiar, it's time to bring the same rigour to AI that you already apply to cloud and software.
Monthly token and API costs across providers are climbing, and nobody can confidently predict next quarter's bill.
Teams have adopted different AI tools and model providers independently, with no consolidated view of usage or cost.
There's no reliable answer to who across the organisation has AI access, at what level, or for what purpose.
Proof-of-concept projects showed promise, but cost-per-outcome became untenable once usage moved into production.
Leadership wants a clear answer on AI return on investment, and nobody can provide one with confidence.
Every engagement is built around board-ready deliverables.
A consolidated picture of current AI and LLM spend by provider, by team, and by trend.
Who across the organisation should have AI access, at what level, and why, aligned to role, risk, and value.
AI spend made visible, attributable, and controlled, without slowing teams down.
A prioritised set of cost-reduction opportunities, each with estimated savings, sequenced for impact.
A handover that keeps working as usage scales, so this doesn't go stale in six months.
Rigorous enough for enterprise governance, fast enough to keep pace with how quickly AI usage changes.
We map your current AI footprint: tools, providers, spend, and usage patterns, into a single source of truth.
We define who should have access to what, at what level, and build the case for where AI investment goes next.
We put in place the visibility and controls that make AI spend attributable to teams and accountable to leadership.
We identify and prioritise cost-reduction opportunities, sequenced by impact and ease of implementation.
We hand over an operating model, and the capability, so your organisation keeps managing this as usage grows.
Six questions, about a minute, no email required. The result is directional, not a diagnosis.
AI FinOps applies the same principles that brought visibility and accountability to cloud spend (tagging, attribution, forecasting, and optimisation) to generative AI and LLM usage. Token economics refers to understanding how usage (tokens consumed per request, per model, per use case) translates into cost, so spend can be forecast, attributed, and managed rather than discovered after the fact.
Enterprises that successfully control AI/LLM costs typically start with visibility, a consolidated view of spend across every tool and provider in use, followed by access governance (who can use what, at what level) and ongoing optimisation as usage patterns evolve. Without this foundation, AI spend tends to grow in line with adoption with no corresponding cost discipline.
There is no universal figure. It depends on the number of use cases in production, the models in use, and how usage scales with adoption. What matters more than a fixed budget number is having a forecasting model tied to your actual usage drivers, so spend is predictable rather than a surprise on next month's invoice. An AI Spend Diagnostic establishes this baseline for your specific environment.
An AI token strategy is a structured approach to how an organisation plans, allocates, governs, and optimises its spend on generative AI and LLM usage, covering which teams and use cases get access, how that access is governed, how spend is tracked and attributed, and how cost-optimisation opportunities are identified and prioritised over time.
General AI consulting typically focuses on use cases, capability building, or model selection. AI Token Strategy is narrower and more operational. It focuses specifically on the cost, governance, and operating model side of AI adoption, in much the same way a FinOps engagement is distinct from a broader cloud strategy engagement.
Cloud cost management tools are typically built around infrastructure spend (compute, storage, networking), and often have limited visibility into API-based AI/LLM usage across multiple providers, or into how that usage maps to teams, use cases, and business value. AI Token Strategy complements existing cloud cost tooling by addressing the AI-specific layer that usually sits outside it.
A software license audit looks backward, establishing whether you're compliant with existing licence terms for tools you already own. AI Token Strategy looks at consumption-based spend, which has no licence position to audit; instead it's about ongoing visibility, access governance, and optimisation of usage-based costs that change every month.
This service is designed for enterprise organisations, typically 1,000+ employees, where AI/LLM usage has moved beyond a single pilot and now spans multiple teams, tools, or providers, and where leadership needs a clear, governed view of cost and value. It is most relevant for CIOs, CTOs, CFOs, and Heads of AI or Data.
Start with an AI spend diagnostic: a focused first conversation about where your AI spend stands today, and where the opportunity is.