Most enterprises overspend on GenAI by 3-5x before any AI FinOps practice is in place. We fix that in weeks, not months.
Every team is using GPT or Claude Opus for everything - summarisation, classification, complex reasoning - all at top-tier pricing. There is no breakdown by team, use case, or model. Shadow AI deployments are multiplying ungoverned infrastructure and compliance gaps.
Finance asks questions IT cannot fully answer. Growing invoices with no clear owner. A CFO who wants numbers, not narratives.
What AI spend management actually requires
Six capabilities work together to bring financial control to GenAI - without replacing your stack. Model right-sizing, prompt compression, semantic caching, and context engineering typically deliver 40-60% cost reduction within 30 days, and scale without ongoing engineering effort.
What each team, model, and workload is spending - cost-per-token, cost-per-API-call, and cost-per-outcome by use case. Clear unit economics. No more estimates.
Model right-sizing, prompt compression, semantic caching, and context engineering. Typical result - 40-60% cost reduction within 30 days. Scales without engineering effort.
AI Gateway deployed in your AWS environment. Centralised routing, tagging, chargeback by team and project. Full audit trail maintained continuously - not assembled before each review.
Every token cost attributed to a team, project, and use case. Finance gets a cost breakdown they can report on directly. No chasing engineers, no manual reconciliation.
We commonly find enterprises running Claude Opus, GPT, or Gemini Pro on tasks where Claude Haiku, GPT mini, or Gemini Flash delivers equivalent output at a fraction of the cost. We map every use case to the right model tier and validate output quality before and after.
Continuous usage logs, cost records, and model decision audit trails. Built for EU AI Act compliance timelines and regulated-industry reporting requirements. Audit reviews run cleanly - no scramble before every submission.
Not sure where to start? AI Spend Forensics gives you a clear cost baseline in weeks.
Spend map, unit economics, savings roadmap, and a board-ready summary. First insights in 2 weeks.
No new tooling to procure. No restructuring of your team.
A complete baseline of GenAI usage across teams and models. Token costs, inference spend, API volumes, broken down by use case and business unit. You stop estimating and start knowing.
Routing policies and usage guardrails go directly into your environment through an AI Gateway. Model right-sizing, prompt caching, and context engineering reduce spend in line with business value. It stays there without ongoing manual effort.
Reporting, chargeback, and optimisation cadence are in place. As GenAI usage grows, so does financial visibility and control. For organisations running AI in production, AI Operations extends this into full managed operations.
Clients typically see 40-60% cost reduction. One engagement reduced a $4,800/month audio-text analytics workload to $1,300/month. It paid for itself in two months.
When cost visibility is built into how AI operates, the outcomes compound. AI FinOps deploys inside your infrastructure and connects directly to broader governance initiatives. Starting with AI Spend Forensics means measurable value from week one - before any long-term commitment.
You gain:
Illustrative savings from real engagement patterns - ask us for sector-specific references.
73% cost reduction, zero quality impact. Before $4,800/month, after $1,300/month. High-volume automated audio content across multiple languages; AWS Transcribe replaced with open-source model, auto-scaling, semantic caching, and prompt compression. About $42,000/year savings; engagement paid for itself in two months.
Before $8,200/year, after $3,600/year (56% reduction). OpenSearch to S3 Vector migration, model swap from Claude Opus to Claude Haiku, chunk optimisation. About $4,600/year saved.
Before 2 million tokens per run, after 500k (75% reduction). Prompt caching, context management, usage controls. About $27,600/year saved.
Before $180k/year, after $72k/year (60% reduction). AI Gateway deployment, model choice optimisation, infrastructure optimisation. About $107k/year saved.
Customer results
One real metric does more than any number of claims. Ask us for references in your sector.
How autonomous cloud cost optimisation cut a Nordic firm's AWS bill by 22% in a single dev and QA account, every figure cross-verified against the live estate.
Bringing forecasting accuracy to a multi-model GenAI environment. We can share relevant outcomes on request.
Contact usPassing AI spend audits cleanly, without a manual scramble. Contact us for sector-specific examples.
Contact usFAQ
AI FinOps (sometimes called FinOps for AI) is a service that gives your organisation clear visibility and disciplined control over AI spend. As teams adopt more models, tools, and agents, costs grow faster than expected and harder to attribute. AI FinOps tracks where spend is going, which use cases are generating value, and where there is waste - then acts on it.
Standard cloud FinOps covers compute, storage, and networking. AI introduces different cost drivers - model inference costs, API call volumes, agent runtime, and prompt token usage. These do not map neatly to existing cloud cost categories. The FinOps Foundation's framework for AI identifies token-based pricing, GPU scarcity, volatile model costs, and cross-functional spend attribution as the key differences from traditional cloud FinOps. Our service addresses all of these, tracking spend at the model, use case, and team level.
AI FinOps measures spend per model, spend per use case, spend per team, and cost-per-outcome for each AI workload. It identifies underused deployments, over-provisioned inference infrastructure, and use cases where cost has grown beyond the value being generated.
AI FinOps sits at the intersection of IT, finance, and the teams building or using AI. It works best when there is a named owner - typically a head of AI or IT operations or a technology finance lead - supported by accurate data from an AI Gateway. Firemind provides that gateway and the ongoing service to act on what it surfaces.
AI Spend Forensics delivers a complete cost baseline, savings roadmap, and board-ready summary within 2 weeks. Optimisation sprints deliver measurable cost reduction within 30 days. No long-term commitment required to start.
