Service
Audit, optimize, and govern your AI spending — from LLM APIs and GPU infrastructure to workflow automation and shadow AI — so every dollar of AI investment drives measurable business value.
Key capabilities
Overview
AI cost optimization is the practice of reducing the total cost of training, deploying, and operating AI workloads while preserving model performance and business value. It combines cost allocation, unit economics measurement, and targeted action on infrastructure, data, and token-level levers.
Most organisations treat AI costs as a single line item, making it impossible to know which models, teams, or features are driving spend — or whether that spend is justified. AI cost optimisation brings visibility, accountability, and continuous improvement to AI investment, so you can scale confidently without waste.
Audit
Map every AI cost across models, APIs, infrastructure, platforms, and teams — including shadow AI spend that escapes procurement
Optimise
Apply targeted levers — model selection, prompt engineering, caching, infrastructure right-sizing — to reduce cost without degrading quality
Govern
Establish FinOps practices, cost allocation models, budget guardrails, and ROI dashboards to sustain savings over time
Services
Every engagement is tailored to your organisation's AI maturity, stack, and spending profile. These are the core capabilities we bring to every cost optimisation engagement.
Comprehensive discovery of all AI-related spending across the organisation — LLM APIs (OpenAI, Anthropic, Google, etc.), cloud GPU instances, managed AI services, workflow automation platforms, and team-level subscriptions. We identify shadow AI spend that often accounts for 30-50% of total AI costs.
Right-size model selection so simple tasks use cheap, fast models and complex reasoning uses capable ones. Apply prompt compression, semantic caching, context window optimisation, and batching to reduce token consumption by 40-70% without changing output quality.
Analyse GPU instance types, region pricing, reserved vs spot/preemptible instances, and self-hosted vs managed API trade-offs. For suitable workloads, we recommend and implement self-hosted inference with Rust-based engines (Candle, xinfer) that eliminate per-token pricing and deliver predictable infrastructure costs.
Implement cost allocation tagging so every dollar of AI spend is attributed to a specific customer, feature, team, or model. Establish unit economics — cost per inference, cost per conversation, cost per user — and set budget guardrails with automated alerts so cost overruns are caught before they hit the bill.
Define KPIs that connect AI spend to business outcomes — revenue uplift, cost savings, productivity gains, customer satisfaction. Build dashboards that track value realisation over time and provide early warning when investments are underperforming. We help you answer: "Is this AI initiative worth what it costs?"
Automation platforms like Make.com, Zapier, and n8n can add 200-400% markup on AI API calls. We audit your automation stacks, identify hidden cost multipliers, and recommend direct-integration or self-hosted alternatives that eliminate platform overhead while maintaining or improving reliability.
Our process
We take you from spending blind to cost-confident — with full visibility, actionable recommendations, and ongoing governance. Every engagement produces measurable savings and a clear path to sustain them.
Map all AI spending across models, APIs, cloud infrastructure, automation platforms, and team subscriptions. Interview stakeholders to uncover shadow AI. Build a complete cost baseline with per-workload breakdowns.
Identify savings opportunities across model selection, token usage, infrastructure, and workflow automation. Rank by impact and effort. Model TCO scenarios for self-hosted vs API for each workload.
Deploy the highest-impact changes — model routing, prompt optimisation, caching, infrastructure migration, or self-hosted inference — with before/after measurements to validate savings. Establish cost allocation tags and budget guardrails.
Set up dashboards, automated alerts, and regular review cadences. We provide ongoing advisory to adapt optimisation strategies as usage patterns, models, and pricing evolve — ensuring savings compound over time.
Deliverables
Every engagement produces concrete, data-backed outputs. No generic reports — each deliverable is specific to your organisation's AI stack, spending profile, and business goals.
Complete inventory of all AI-related spending — LLM APIs, cloud GPU, managed services, automation platforms, subscriptions — with per-workload, per-team, and per-model cost breakdowns.
Prioritised list of savings opportunities ranked by impact and complexity, with estimated cost reduction for each. Includes model right-sizing, infrastructure changes, and workflow automation improvements.
Data-driven total cost of ownership analysis comparing current spend against self-hosted (Rust/Candle/xinfer), alternative API providers, and hybrid deployment options for each workload.
Cost allocation tagging strategy, unit economics definitions (cost per inference, cost per customer), budget policies, alert thresholds, and team-level accountability structures.
Live dashboard tracking AI spend against business outcomes — cost savings realised, revenue uplift, productivity gains, and customer satisfaction impact. Updated continuously after implementation.
Leadership-ready summary of findings, savings achieved, and ongoing governance recommendations. Includes benchmark comparisons against industry peers and clear next steps.
FAQ
Next step
We help you audit, optimise, and govern AI costs so every dollar of investment drives measurable business value — freeing up budget for the innovations that matter.