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Decouple tech cost from revenue

AI Cost Management and Business Value

Unlock your technology edge

We are now in a new technology cycle with AI-related compute increasing exponentially. As AI adoption accelerates, commercial value often lags behind cost and complexity. Too often, experimentation lacks discipline. Costs rise, value is unclear, and leadership confidence erodes.

Capacitas ensures that AI ambitions translate into measurable business value. We bring costs under control and align AI for value acceleration: governed, optimised and intentionally aligned to your business goals.

The elusive promise of AI ROI

AI is now ubiquitous, but sustainable returns remain rare.
What begins as targeted innovation frequently becomes a source of:

  • Unpredictable and escalating LLM and model-inference costs
  • Fragmented experimentation disconnected from enterprise priorities
  • Limited visibility of whether AI is driving efficiency, growth, or margin improvement

Capacitas works with executive teams to turn AI into an economic asset — not a science project — ensuring that every AI investment is transparently linked to value creation.

From AI potential to engineered value

We connect AI strategy, LLM economics, and delivery execution into a single value-driven model. Drawing on our deep expertise in cloud economics, FinOps and technology optimisation, we help you:

  • Optimise the cost-performance trade-offs of LLMs and AI architectures
  • Translate AI ambition into a clear, business-aligned strategy
  • Embed the operating model needed to ensure AI value is repeatable, measurable and sustained

The result is AI that compounds advantage over time — rather than generating episodic wins or runaway costs.

$1m+

annual saving opportunity identified by correcting LLM scaling approach, preventing cost growth on the wrong optimisation vector.

— a $2bn SaaS firm

66%

improvement in LLM cost‑efficiency achieved by optimising inference and serving architecture, whilst improving LLM performance.

— a consumer platform with ~50m users

$400k

p.a. optimisation of LLM inference workload

— a US-based B2C firm with 45m users

Edge unlocked

AI that delivers value, not just output
Every AI and LLM use case is explicitly linked to measurable business outcomes across cost reduction, efficiency, revenue and risk.

Make AI economics predictable
We give you forward-looking visibility of AI and LLM spend — including inference, tooling, data and platform costs — enabling confident funding, forecasting and investment decisions.

Make AI value sustainable, not one-off 
We embed governance, cost control and value-tracking into day-to-day delivery so optimisation continues as AI scales.

How we deliver: four stages to sustained AI value

Our delivery model mirrors the proven Capacitas approach used across cloud and technology transformation — ensuring AI strategy, cost optimisation and execution are tightly integrated.

1. Discover — quantify cost leakage and savings potential
We establish a clear, evidence-based baseline of how AI and LLMs are being used today and the economic upside available.

  • Assess AI and LLM use cases, models, tooling and vendors
  • Analyse cost drivers including inference, training, data pipelines and third-party platforms
  • Quantify current run-costs and inefficiencies
  • Model achievable savings scenarios from optimisation, governance and architecture changes
  • Clarify how existing AI efforts align, or do not align, to strategic priorities

This stage produces a quantified view of AI value and savings potential — including a clear estimate of the dollar impact achievable, not just diagnostic insight.

2. Realise — deliver savings and define AI strategy for scale
We convert quantified opportunity into fully delivered financial impact, while setting the strategic direction for AI.

  • Execute the optimisation actions required to realise the identified cost savings in full
  • Define the role AI should play in achieving your business goals
  • Establish target economics for AI initiatives, including guardrails for LLM consumption
  • Create a value-led roadmap that aligns leadership, technology and delivery teams

This stage ensures savings move from analysis to reality, while AI strategy, governance and economics are put in place to support controlled, value-led scale.

3. Transform — optimise and embed value-led delivery

We embed engineering cost control and value discipline directly into AI delivery.

  • Embed governance, ownership and accountability for AI usage and spend
  • Integrate value metrics into product, engineering and data decision-making
  • Align operating models so teams actively manage AI economics rather than react to cost overruns

AI becomes a managed capability — not an uncontrolled expense.

4. Support — sustain optimisation as AI scales

We ensure AI value does not degrade over time as adoption grows.

  • Ongoing monitoring of AI and LLM cost, usage and value
  • Continuous optimisation as models, providers and business needs evolve
  • Support leadership with decision-grade insight on AI investment trade-offs
  • Embed sustainable practices so optimisation is organisational muscle, not consultant-led dependency

THE TECHNOLOGY EDGE: AI ECONOMICS RE-ENGINEERED FOR COST REDUCTION, TOPLINE GROWTH AND ENDURING ENTERPRISE VALUE.

THE TECHNOLOGY EDGE: AI ECONOMICS RE-ENGINEERED FOR COST REDUCTION, TOPLINE GROWTH AND ENDURING ENTERPRISE VALUE.

Our Clients
Fanatics
Bread-financial
Match-group
Concert-Ai

Your AI value blueprint 

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A unified view of AI cost and value

Across models, LLMs, tools and use cases, we give clarity to where money is spent, how usage grows, and what value is delivered.

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Eliminate uncontrolled LLM spend

We identify and stop cost leakage caused by unmanaged inference, inappropriate model selection and ungoverned experimentation.

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Engineer value into AI decisions

Cost, value and performance intelligence is embedded directly into AI design and deployment — ensuring commercial outcomes are considered early, not after the fact.

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Scale AI without losing control

Clear ownership, guardrails and operating models ensure AI and LLM adoption can scale safely across products and operations.

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Drive enterprise and portfolio value

We optimise AI for value creation across enterprises and private equity portfolios — positioning AI as a lever for EBITDA improvement and durable growth.

Before working with Capacitas, we knew cloud cost optimisation was important, but we saw it mainly as a financial exercise. They changed that. By diving into our data, they showed us how performance, reliability, and efficiency are all connected – and how improving one improves the others. That perspective has reshaped how we think about architecture and operations at Qualtrics.

Steve Jang

Distinguished Software Engineer – Qualtrics

With Capacitas, this was not a consultant dropping a set of recommendations on an engineering team and then leaving. This was different. We work hand in hand with the Capacitas team.

James Griffith

Global Head of Engineering, Archer

Capacitas helped us connect the dots between infrastructure usage, cost, and performance. That gave our engineers the confidence to refactor, optimise, and experiment - and that’s where the real transformation starts.

Nik Sathe

CPO & CTO Blackhawk Network; ex-CTO of PayPal, AMEX, VP at Google

FAQs

Why is AI and LLM cost optimisation now critical?

As LLMs move from experimentation to enterprise-scale usage, small design decisions can drive disproportionate cost impact. Without control, AI quickly becomes a material and unpredictable operating cost. Capacitas brings discipline, transparency and economic rigour to ensure AI remains value-accretive.

How is this different from a one-off cost reduction exercise?

We don’t just optimise spend — we embed ownership, governance and value measurement into how AI is delivered day to day. This creates a lasting capability rather than a temporary saving.

What outcomes do organisations typically see?

Clients typically achieve:

  • Reduced AI and LLM run costs by eliminating inefficient usage

  • Clear enterprise-wide visibility of AI spend

  • Strong alignment between AI investment and business priorities

  • More predictable, controlled scaling of AI capabilities 

Does this apply across sectors?

 Yes. We work across financial services, consumer, public sector, technology, infrastructure and private equity environments — tailoring AI economics to sector-specific drivers. 
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