FinOps and cost optimisation for UK organisations running on Microsoft Azure. Right-size, commit where commitment pays, build the guardrails — and keep your monthly invoice explainable to finance.
Most "Azure cost optimisation" projects deliver a one-off saving and a deck. The bill creeps back inside two quarters because nothing changed about how new spend gets approved, tagged, or sized. Our work is structured to deliver an immediate cut and the operating model that prevents the next bloat cycle.
Phase 1 — quick wins. Idle and oversized resources, orphaned disks and snapshots, untagged spend, dev environments running 24×7, log retention defaults eating Sentinel quota. These are mechanical to find and fix; we typically clear them inside the first two weeks and bank a measurable saving before doing anything structural.
Phase 2 — commitments. Reservations and Azure Savings Plans are powerful but easy to mis-buy. We build a commitment model based on your actual stable baseline (not your peak), stagger expirations, account for migration plans, and document who owns the buy/renew decision. Wrong commitments are worse than no commitments.
Phase 3 — guardrails. Tag taxonomy enforced via Azure Policy, budgets and alerts at subscription and resource-group level, scheduled deallocation for non-production, Azure Advisor recommendations triaged, and a monthly review cadence with a clear owner. Cost becomes a property of the platform, not a quarterly fire-drill.
Phase 4 — accountability. Showback or chargeback to teams or product lines, with the data quality good enough that engineers trust their own numbers. The engineers who own a workload should also see what it costs.
Itemised waste inventory with effort and saving per item, plus the changes already made and the savings already booked during the engagement.
Reservation and Savings Plan recommendations sized to your stable baseline, with a renewal calendar, breakeven analysis, and ownership documented.
Azure Policy for tag enforcement, budgets and alerts, automated dev-environment shutdown, and an Advisor triage workflow your team will actually run.
Cost allocation per team, product, or environment, surfaced in a dashboard your engineers and your CFO can both trust.
The brief. Azure spend had grown 4× year-on-year alongside the product. Finance flagged it; engineering had no time to investigate; nobody knew which features were profitable on a per-tenant basis. Six-week engagement to land a defensible plan.
The work. Two weeks of quick-wins remediation cleared idle VMs, oversized SQL elastic pools, and Log Analytics tables retaining at the maximum tier by default. Reserved capacity and an Azure Savings Plan placed on the production baseline; non-production moved onto auto-deallocate schedules. Tag taxonomy enforced via Policy, with Cost Management chargeback configured per product line. Monthly FinOps cadence handed over to the platform team.
The result. Run-rate cut by roughly a third within the engagement window, with another ~10% projected as commitments matured. Per-tenant gross-margin reporting enabled — finance now signs off Azure spend monthly without escalation. Net engagement payback inside the first month.
Anonymised illustrative engagement. Numbers reflect typical scope and outcomes for an engagement of this size; specifics vary by environment.
For an environment that has never had a serious cost review, fifteen to thirty-five percent of run-rate is typical inside a focused engagement. Environments that have already been optimised once will see smaller absolute savings but larger benefits from guardrails preventing future drift. We will be honest after the discovery phase about what the real ceiling looks like for your estate.
Only if you commit to capacity you do not actually use. We build the commitment model on your stable baseline, not your peak — and stagger maturities so you never have one large renewal cliff. Microsoft's exchange flexibility for reservations also limits downside materially compared to other clouds.
No. We work on a fixed scope and fixed fee. Percentage-of-savings models incentivise the wrong behaviours — chasing visible cuts at the expense of guardrails and architectural fixes that pay back over time.
Yes — that is the only kind of optimisation worth doing. Right-sizing is data-driven (we use Azure Monitor metrics, not guesswork), staged through environments, and reversible. Anything performance-sensitive is profiled before and after.
The engagement focus is Azure (IaaS, PaaS, data, analytics, AI services). We will identify obvious M365 and Power Platform inefficiencies in passing — license-tier mismatches, dormant accounts, unused capacity — but a deep optimisation of those licence estates is a separate engagement.
That is the test. The guardrails — Policy, budgets, scheduled deallocation, Advisor triage, tagging enforcement — are the deliverable. If the bill creeps back within six months, the engagement was an expense, not an investment. We design for the opposite outcome.
A 30-minute call to scope your estate, current invoice, and the question your CFO is asking.
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