AI Cost Optimisation

AI cost is the fastest-growing line on most infrastructure bills, and the least understood. Token spend, GPU hours, inference profiles, coding agents and shadow AI usage all compound quickly — and cloud bills rarely show which team, application or model is driving the number. We help organisations bring AI spend under control with a FinOps-grade approach purpose-built for AI: full visibility across models, tokens and infrastructure; systematic reduction of waste; and evaluation harnesses that lift output quality so smaller, cheaper models can safely do more of the work. The result is materially lower AI run-cost, better-performing AI in production, and governance that keeps both on track as adoption scales.

How we help

AI spend visibility and FinOps

Unit-economics view of AI cost across teams, applications, models, tokens, GPUs and inference endpoints — the granularity your cloud bill will never give you.

Model right-sizing and routing

Match each workload to the cheapest model that meets the quality bar, with intelligent routing between frontier, mid-tier and open-source models.

Prompt and token optimisation

Systematic reduction of input and output tokens through prompt engineering, structured outputs, response shaping and context pruning.

Caching, batching and retrieval

Semantic caching, prompt caching, batch inference and tuned RAG pipelines that eliminate repeat spend and cut latency at the same time.

GPU and infrastructure efficiency

Right-sized training and inference infrastructure, spot and reserved capacity strategies, autoscaling and workload consolidation to lift utilisation.

Agent and copilot cost control

Guardrails on coding agents, tool-calling loops and autonomous workflows so runaway agent behaviour stops silently burning your budget.

Evaluation and quality uplift

Evaluation harnesses, golden datasets and continuous benchmarks that prove smaller, cheaper models can meet or beat what you run today.

AI governance and policy

Model policies, spend caps, approval workflows and reporting that let leaders scale AI adoption responsibly instead of blindly.

What you can expect

30–70% reduction in AI run-cost without cutting scope or quality
Full unit-economics visibility across models, teams, applications and workloads
Higher-quality, more reliable AI outputs in production — measured, not assumed
Governance and policies that keep AI spend and quality on track as usage grows
A prioritised, evidence-based roadmap for the next wave of AI cost and quality wins

Paying too much for too little from AI?

Book an AI cost and quality review. We'll baseline your current AI spend, quantify the waste, and show you where to cut cost and where to lift output quality.

Book an AI cost review