AI Cost Control · FinOps · gain-share available
Azure & AI cost management
North Peak Cloud controls the cost of AI and cloud on Azure so it never becomes bill shock. We build the FinOps discipline that AI workloads need — the token-versus-provisioned-throughput decision, model right-sizing, reservations and cost-anomaly detection — across your Azure OpenAI usage and your classic Azure estate. Most organisations are carrying 20–40% avoidable spend; we find it and remove it.
- Token vs PTU (provisioned throughput) cost strategy
- Model right-sizing — frontier vs small models by workload
- Reservations, savings plans and Hybrid Benefit
- Cost anomaly detection and tagging for allocation
- Classic Azure estate optimisation, not just AI
- Fixed-price audit, then monthly — gain-share option
AI on Azure, done right.
AI-ready foundations
Azure AI Foundry hubs and projects, private networking to your models, identity and policy guardrails — the secure base every AI workload needs before it scales.
Cost control for AI & cloud
FinOps for the AI era: token and PTU strategy, model right-sizing, reservations and anomaly detection. We find the waste your invoice is hiding.
Platform engineering
Landing zones as code in Bicep or Terraform, GitOps, policy-as-code and golden paths. Enterprise-grade Azure delivered in weeks, not quarters.
Security & responsible AI
Content filtering, evaluations, data-residency and audit-ready guardrails aligned to the EU AI Act and ISO 42001 — trustworthy AI you can put in front of customers.
Migration & modernisation
Cloud Adoption Framework migrations and Well-Architected reviews that leave you cheaper, faster and ready for AI — not just lifted-and-shifted.
Senior engineers, direct
You work with the architect who does the build — no account managers, no offshore hand-off, no gatekeeping. Answers in plain English.
Three routes to AI on Azure.
| Route | Typical cost | Speed to production | Who does the work | Price certainty |
|---|---|---|---|---|
| Build in-house | Engineer salaries + the learning curve | 3–6 months to first production use case | Your team, learning as they go | Open-ended |
| Large MSP / SI programme | Typically six figures | Quarterly roadmaps, rotating teams | A delivery team and an account manager | Quoted after discovery calls |
| North Peak Cloud | Fixed-price packages | Weeks to production | The architect who scoped it | Fixed and agreed before work starts |
Three ways to start.
An AI-ready Azure landing zone, delivered in weeks.
- Azure AI Foundry hub & projects
- Private networking to model endpoints
- Identity, RBAC & Azure Policy guardrails
- Responsible-AI baseline & content filters
- Infrastructure as code you own
- Handover & runbook
One AI use case, production-grade, on AI Foundry.
- RAG, agent or document-intelligence build
- Evaluation & guardrails wired in
- CI/CD and observability
- Cost model before you commit
- Knowledge transfer to your team
- Reusable accelerator IP
Stop AI and cloud bill shock — with a gain-share option where our fee aligns to the savings we deliver.
- Token / PTU vs pay-as-you-go strategy
- Model right-sizing & reservations
- Cost anomaly detection
- Classic Azure estate optimisation
- Monthly reporting & reviews
- Gain-share option available
for AI.
Asked and answered.
How much does Azure AI cost management cost?+
It starts with a fixed-price AI Cost Audit — a short, full review of your Azure OpenAI and cloud spend with a prioritised savings plan — then an optional monthly FinOps engagement, cancel any time. A gain-share option is available, where our fee is a share of the savings we deliver, so the work pays for itself.
How do I reduce Azure OpenAI costs?+
Choose the right pricing model (pay-as-you-go vs provisioned throughput/PTU) for your traffic, right-size the model to the task, cache and batch where possible, and monitor token usage for anomalies. North Peak Cloud builds this into an ongoing FinOps practice across your Azure estate.
What is FinOps for AI?+
FinOps for AI applies cloud financial management to AI workloads — making token, PTU and model-selection costs visible, forecastable and optimised, so AI spend is a controlled investment rather than an unpredictable bill.
Do you offer gain-share pricing?+
Yes. For cost-optimisation engagements we can align our fee to a share of the savings we deliver, so the work pays for itself.