Cost Optimization Due Diligence

Spending Patterns, Unit Economics, and Scaling Costs

Cloud cost efficiency is a direct indicator of operational discipline. We assess three dimensions: are resources right-sized for actual workloads, are pricing models optimized (reserved vs on-demand), and does the architecture itself drive unnecessary costs?

Unit economics matter most for investors. Cost per customer, cost per request, and cost per transaction reveal whether the business model scales profitably. A company spending $5 per customer on infrastructure with $10 ARPU has different economics than one spending $0.50. We calculate these metrics from actual cloud spend and business data.

Cost trajectory at scale is critical for growth-stage companies. Some architectures scale linearly - 2x customers means 2x infrastructure cost. Others have step functions - 2x customers means 4x cost because of architectural bottlenecks. We model cost at different growth scenarios to identify scaling economics.

Architectural Cost Traps and Runaway Spend

The highest risk: architecture that doesn’t scale cost-efficiently. A monolithic database that must scale vertically (bigger, more expensive instances) instead of horizontally creates a cost cliff at each scaling step. Services that transfer large amounts of data between regions accumulate data transfer costs that grow faster than revenue.

Over-provisioning is a quantifiable waste. We measure actual utilization against provisioned capacity and calculate the dollar value of the gap. This isn’t theoretical savings - it’s money currently being wasted that can be recovered immediately.

Missing FinOps practices indicate ongoing cost risk. Teams without tagging, budgets, or cost review processes will continue to over-provision. The waste we find today will regenerate without process changes. FinOps maturity predicts whether savings are sustainable.

We also evaluate architectural patterns that silently drive cost. Data pipeline architectures are a frequent culprit - unbounded Lambda invocations processing streaming data, oversized EMR clusters running batch jobs that could execute on a single instance, or Kinesis streams provisioned for peak throughput that occurs once a month. NAT Gateway charges are another common surprise, sometimes exceeding the cost of the compute resources behind them. We assess whether traffic could be routed through VPC endpoints instead. Storage costs are audited across S3 lifecycle policies, EBS snapshot retention, and CloudWatch log retention periods. Organizations routinely retain debug-level logs for 12 months at significant expense when 30 days would serve every operational need. Each of these findings is quantified with monthly dollar impact so that remediation can be prioritized by financial return rather than abstract best practices.

Savings Projections and FinOps Roadmap

The report quantifies current cloud waste, projects cost at growth scenarios, and identifies optimization opportunities with dollar values. Investors get clear input for unit economics and post-investment cost expectations. Acquirers get an infrastructure cost optimization plan with projected savings. Each recommendation includes effort required and expected monthly savings.