FinOps & Cloud Cost Optimisation

Why are most businesses overspending on Azure?

Cloud spend grows in predictable ways:

Oversized VMs — During migration, VMs are provisioned to match or exceed on-premises specs. In practice, most VMs use a fraction of their allocated resources. Without right-sizing, you pay for capacity you never use.

Orphaned resources — Test environments, old disks, unused public IPs, and decommissioned resources that nobody deleted. They accumulate charges month after month.

Pay-as-you-go pricing — Predictable workloads running 24/7 on pay-as-you-go rates instead of reserved instances, which offer 30-72% discounts.

No cost governance — No budgets, no alerts, no regular reviews. Spend grows without anyone noticing until the monthly invoice arrives.

What does our FinOps service include?

We implement a structured approach to cloud cost management:

• Azure cost audit — Full analysis of current spend by resource, service, and resource group
• Right-sizing recommendations — VM and storage tier adjustments based on utilisation data
• Reserved instance planning — Identification of workloads suitable for one-year or three-year commitments
• Orphaned resource cleanup — Removal of unused resources that still incur charges
• Budget and alert configuration — Azure Cost Management set up with thresholds and notifications
• Monthly reporting — Spend trends, savings achieved, and new optimisation opportunities
• Tagging strategy — Resource tagging for cost allocation by department, project, or environment

What outcomes should you expect?

Businesses that implement FinOps with Prexiam typically see:

• 20-40% reduction in Azure monthly spend within the first quarter
• Clear visibility into what drives cloud costs
• Predictable monthly budgets with alert thresholds
• Finance team confidence in cloud cost forecasting
• Ongoing optimisation that prevents cost creep

How do we maintain cost control over time?

FinOps is continuous:

1. Monthly reviews — We analyse spend trends and identify new optimisation opportunities
2. Reservation management — We track reserved instance utilisation and adjust as workloads change
3. New deployment governance — We set sizing standards for new resources to prevent oversizing
4. Quarterly reporting — Detailed cost analysis presented during business reviews

What does a typical FinOps engagement look like?

For a Melbourne business running ten to thirty Azure resources, a typical engagement follows this pattern:

Week one — We connect to your Azure environment and analyse 90 days of cost and utilisation data. We identify oversized VMs, orphaned resources, and reserved instance opportunities.

Week two — We present findings with specific dollar amounts attached to each recommendation. You approve which changes to implement.

Weeks three to four — We implement right-sizing, clean up orphaned resources, and configure reserved instances where approved. We set up Azure Cost Management dashboards, budgets, and alerts.

Ongoing — Monthly reviews track spend against baselines. New resources are governed by sizing standards. Reserved instance utilisation is monitored. Quarterly reports show cumulative savings and new opportunities.

How do we prevent cost creep after optimisation?

Optimisation without governance is temporary. Costs creep back as new workloads are deployed without oversight, test environments are left running, and VM sizes drift upward during troubleshooting.

We prevent this through:

• Tagging policies — Every resource must be tagged with owner, environment, and cost centre. Untagged resources are flagged for review.
• Sizing standards — Default VM sizes and storage tiers are documented. Deviations require justification.
• Automated alerts — Budget thresholds trigger notifications before costs exceed planned levels.
• Monthly accountability — Spend is reviewed against budget with variance explanations for any overruns.

This governance framework ensures that the savings we achieve during the initial optimisation are sustained over time rather than eroding back to pre-optimisation levels.