SpecialisedUser storyConsultative playbook

We are paying retail for workloads that have been running for two years

A platform team's stable workloads have been running for years on pay-as-you-go pricing. Reservations and savings plans have been discussed but never executed because no one owns the decision. A FinOps practitioner has been hired; the first deliverable is a defensible reservation strategy.

Trigger: Renewal opportunity; FinOps practitioner onboarded.
Good outcome: Reservation strategy live, idle resources cleaned up, 15–30% first-year savings without architectural change.

Diagnostic discovery

Signals this story fits

Observable cues that confirm the conversation belongs here.

·Stable workloads on pay-as-you-go for 12+ months
·Renewal moment or budget cycle approaching
·FinOps practitioner hired or assigned
·No documented reservation strategy
·Right-sizing and idle cleanup pending

Questions to ask

Open-ended, SPIN-style — each one has a reason it matters.

1.Which workloads have been running stably for 12+ months?
WhyIdentifies reservation candidates.
2.What's your current commitment level — none, 1-year, 3-year?
WhyEstablishes the gap between pay-as-you-go and optimised state.
3.Who has authority to commit to reservations?
WhyProcurement / finance ownership question — often the bottleneck.
4.What's the over-provisioning baseline — VMs running at <30% CPU?
WhyRight-sizing opportunity. Often the biggest single lever.
5.How do you track idle and orphaned resources today?
WhyCleanup is the cheapest savings. Surfaces whether it is operationalised.

Baseline → target architecture

TOGAF-style gap framing — what we typically see today, and what the proposed end state looks like. The gap between them is the engagement.

Baseline architecture

Pay-as-you-go dominant. No reservation discipline. Right-sizing ad-hoc. Idle resources accumulating without a cleanup cadence. Defender for Cloud may surface them but no remediation backlog.

Typical concerns

·Paying retail for stable workloads
·Over-provisioning on VMs and managed services
·Idle resources surviving for months
·No documented reservation decision authority
·Renewal opportunities missed

Capability gaps

·Reservation strategy with named owner
·Right-sizing as platform discipline
·Idle resource cleanup cadence
·Decision authority for commitments
·Quarterly optimisation review

Target architecture

Reservation strategy aligned to forecast: 3-year reserved instances or savings plans for the most-stable tier; 1-year for moderately predictable; pay-as-you-go for variable. Right-sizing automated where signal is clear; reviewed where ambiguous. Idle and orphaned resources flagged weekly with a cleanup backlog. FinOps cadence runs the quarterly optimisation review.

Key capabilities

Reservation portfolio aligned to forecast
Right-sizing as platform discipline
Weekly idle-resource flagging
Quarterly optimisation review cadence
Decision authority documented

Enabling SKUs

Resolved in the ‘Recommended cards’ section below.

Architecture decisions

Each decision is offered as explicit options with trade-offs — Hohpe's “selling options” principle. A safe default is noted where one exists.

Decision 1.Reservation term — 1-year vs 3-year vs mix
3-year
When it fitsWorkload demonstrably stable over multi-year horizon.
Trade-offsLarger commitment with less flexibility.
1-year
When it fitsModerate predictability; growth uncertainty.
Trade-offsLower discount tier.
Mixed by tier
When it fitsMature FinOps; differentiated workload patterns.
Trade-offsMore portfolio management overhead.
Default recommendationMixed by tier. 3-year for production database VMs; 1-year for steady-state compute; pay-as-you-go for variable.
Decision 2.Right-sizing — automated vs platform-team reviewed
Automated
When it fitsWorkloads with clear utilisation signal; tolerant of size changes.
Trade-offsRisk of unintended impact on latency-sensitive workloads.
Platform-team reviewed
When it fitsWorkloads with ambiguous signal; high blast-radius.
Trade-offsSlower cadence; relies on platform-team bandwidth.
Default recommendationAutomated for low-blast-radius; reviewed for high.
Decision 3.Reservations vs Savings Plan vs Spot
Reservations (per-VM-family)
When it fitsStable workload pinned to specific VM family.
Trade-offsFlexibility limited if family changes.
Savings Plan (compute hours)
When it fitsCompute usage stable but mixed family.
Trade-offsSlightly lower discount than RIs.
Spot
When it fitsInterruptible workloads (batch, big data, dev/test).
Trade-offsEviction risk; not for production-critical.
Default recommendationSavings Plan as the base; Reservations for very stable workloads; Spot for interruptible.

Low-risk trial — proof of value

45-day FinOps optimisation — reservations + right-sizing + idle cleanup

6 weeks

Reservation analysis on stable workloads with named owners. Right-sizing review on top ten spend lines. Idle and orphaned resource cleanup pass. First quarterly optimisation cadence run with platform and finance.

Success criteria

Realised first-month savings of 10%+ on the trial scope
Reservation portfolio sized to forecast and committed
Idle and orphaned resources flagged and 50%+ actioned
Quarterly cadence runbook authored and trialled

InvestmentCost Management free for Azure. Reservation commitments are cost-positive from day one. Advisory engagement only.

Proof metrics

·15–30% first-year savings on the trial scope
·Reservation coverage above 60% on stable workloads
·Idle-resource count trending down quarter-over-quarter
·Right-sizing actions delivered without production impact

Recommended cards

The SKUs and capabilities most likely to be part of the solution, with the editorial rationale for each in the context of this story. Add the ones that fit your situation.

SKUMicrosoftSaaS

Microsoft Cost Management

Azure's first-party FinOps console — budgets, anomaly alerts, rightsizing recommendations, showback / chargeback, and exports to data warehouse. Free for Azure-only estates; paid connectors extend it to AWS and GCP for multi-cloud FinOps.

In: Azure consumption

Cloud Foundation

Why for this story

Reservation recommendations + utilization reporting. The source of truth for which workloads are reservation candidates.

SKUMicrosoftAzure

Azure Monitor

Azure's first-party observability platform — metrics, logs, traces, and alerts across Azure resources, hybrid workloads (via Azure Arc), and other clouds. The substrate platform teams use to know what's actually happening across the estate.