How Cloud Costs Impact EBITDA: A Guide for PE Investors
For many PE-backed software and technology companies, cloud infrastructure is the second or third largest expense line -- right behind payroll and often ahead of office space and sales commissions. Yet cloud costs receive a fraction of the financial scrutiny applied to headcount or go-to-market spending. This disconnect represents both a risk and an opportunity.
Let me walk through exactly how cloud costs flow through the financial statements of a typical portfolio company, why the classification matters, and how targeted optimization can meaningfully improve EBITDA.
Where Cloud Costs Sit on the P&L
The first question PE investors need to answer is deceptively simple: are cloud costs classified as Cost of Goods Sold (COGS) or as Operating Expenses (OpEx)? The answer has significant implications for margin analysis and valuation.
For SaaS companies, the portion of cloud spend that directly supports the delivery of the product to customers -- production hosting, databases, CDN, customer-facing APIs -- should be classified as COGS. This spend directly impacts gross margin, which is one of the most closely watched metrics in SaaS valuation. A SaaS company with 65% gross margin will be valued very differently than one at 75%, even with identical revenue.
The remaining cloud spend -- development environments, CI/CD pipelines, internal tooling, data analytics platforms -- is typically classified as OpEx under R&D or G&A. This spend affects operating margin and EBITDA but does not impact gross margin.
The classification problem: Many early-stage and growth-stage companies do not rigorously separate these categories. They report a single AWS bill as a blended expense line, which makes it impossible to accurately calculate gross margin at the product level. During diligence, I always recommend decomposing the cloud bill into COGS and OpEx components. This exercise alone often reveals that reported gross margins are 3-8 percentage points lower than the company claims.
The Direct Path from Cloud Optimization to EBITDA
Here is where cloud economics becomes genuinely exciting for PE investors. Unlike many cost reduction initiatives -- which can take quarters to implement, may require headcount reductions, or risk disrupting operations -- cloud optimization can deliver measurable EBITDA improvement within 30-90 days.
Consider a concrete example:
- Annual cloud spend: $2,000,000
- Achievable optimization: 30% (conservative for a company that has never been optimized)
- Annual savings: $600,000
- EBITDA impact: $600,000 improvement (cloud savings flow directly to EBITDA)
At a 12x EBITDA multiple, that $600K annual savings translates to $7.2M in enterprise value creation. From a cloud optimization engagement that might cost $75K-$150K in advisory fees and take 90 days to implement.
The return on investment is extraordinary, and yet most PE firms do not pursue it systematically across their portfolios.
The 30% Number: Where Does It Come From?
You might reasonably ask: is 30% optimization realistic? Based on my experience across hundreds of engagements, here is the typical breakdown:
Immediate wins (Days 1-30): 10-15% savings
- Terminate zombie resources (instances, load balancers, and storage volumes that are running but serving no traffic or purpose)
- Delete unattached EBS volumes and old snapshots
- Remove unused Elastic IPs and idle load balancers
Medium-term improvements (Days 31-60): 10-15% additional savings
- Right-size over-provisioned instances (most companies run instances 2-4x larger than needed)
- Implement commitment-based pricing (Reserved Instances or Savings Plans)
- Optimize data transfer costs and storage tiers
Structural changes (Days 61-90+): 5-15% additional savings
- Implement autoscaling for variable workloads
- Migrate to graviton (ARM-based) instances for 20% price-performance improvement
- Refactor data storage architecture (move infrequently accessed data to cheaper tiers)
For a company that has never undergone systematic optimization, 30% is actually conservative. I have seen savings exceed 50% in environments with significant zombie resources and zero commitment-based pricing.
Margin Impact by Company Type
The EBITDA impact of cloud optimization varies significantly by business model:
B2B SaaS (cloud as COGS): Cloud typically represents 15-25% of revenue. A 30% reduction in cloud COGS for a company at $20M ARR could improve gross margin by 4-7 percentage points. At SaaS valuation multiples, this is transformative.
Data/Analytics platforms: Cloud often represents 25-40% of revenue due to heavy compute and storage requirements. Optimization opportunities are larger in absolute terms but may require more architectural work.
E-commerce/Marketplace: Cloud costs are typically 5-10% of revenue but are highly variable with traffic. Autoscaling optimization and commitment-based pricing for baseline capacity can yield significant improvements.
Common Financial Modeling Mistakes
When PE firms do model cloud costs, I see several recurring errors:
Mistake 1: Assuming cloud costs scale linearly with revenue. They do not. Well-architected cloud environments exhibit economies of scale -- cost per transaction should decrease as volume increases. If a target's cloud costs are growing faster than revenue, that is a red flag indicating architectural inefficiency.
Mistake 2: Ignoring cloud cost inflation. AWS and Azure periodically reduce prices on existing services, but the overall cloud bill for most companies increases 15-25% annually due to increased usage, new services, and data growth. Your financial model should account for organic cloud cost growth even after optimization.
Mistake 3: Treating optimization as a one-time event. Cloud optimization is not a project; it is a discipline. Without ongoing governance (FinOps practices, regular reviews, automated policies), savings erode within 6-12 months as new resources are provisioned without oversight. Budget for ongoing FinOps tooling and practices, not just a one-time optimization sprint.
Mistake 4: Not modeling the tax implications. Cloud cost savings reduce taxable income. Depending on the entity structure and jurisdiction, the after-tax EBITDA benefit may be different from the gross savings. Work with your tax advisors to model this correctly.
Building Cloud Economics into Your Investment Thesis
For PE firms looking to systematically capture cloud optimization value across their portfolio, I recommend a three-step approach:
Pre-acquisition: Include cloud infrastructure assessment in your standard due diligence process. Identify the optimization opportunity and factor it into your valuation model as a post-close value creation lever.
Post-acquisition (first 90 days): Execute a rapid optimization sprint focused on the quick wins. This generates immediate EBITDA improvement and builds credibility with the management team.
Ongoing governance: Implement FinOps practices and tooling to maintain savings and capture incremental optimization opportunities throughout the hold period.
The firms that treat cloud economics as a core competency -- not an afterthought -- consistently outperform on returns.
Ready to evaluate cloud economics in your next deal? Book a free discovery call to discuss your specific situation.