Clouded Judgement 10.31.25 - Cloud Giants Report Q3

Every week I’ll provide updates on the latest trends in cloud software companies. Follow along to stay up to date!

Cloud Giants

This week the 3 hyperscalers reported (AWS, Azure and Google Cloud). What did we learn? Most importantly - they ALL called out still being meaningfully capacity constrained. CapEx guides are going up, data center builds are going up, power constraints are meaningful. This isn’t the telecom bust where the world laid fiber that was “dark” (ie unused). GPUs are being used the second the come online…

Here are the numbers:

AWS (Amazon): $132B run rate growing 20% YoY (last Q grew 17%)

Azure (Microsoft): ~$93B run rate (estimate) growing 39% YoY (last Q grew 39%)

Google Cloud (includes GSuite): $61B run rate growing 34% YoY (last Q grew 32%, neither are cc)

Takeaways from AWS:

On capacity and constraints

• Added 3.8 GW of power in the last 12 months, at least 1 GW more in Q4

• On track to double overall capacity again by 2027

• Current industry bottleneck is power, chips may tighten later

• As capacity lands it is getting monetized immediately

• Bedrock positioned to become the largest inference engine over time, potentially as large as EC2

Custom silicon and chips:

• Trainium2 is fully subscribed and a multibillion dollar business, up 150 percent quarter over quarter

• Project Rainier for Anthropic at 500k Trainium2 chips, scaling to 1M by year end

• Majority of Bedrock token usage already runs on Trainium

• Trainium3 preview later this year, volume early 2026, Amazon reiterates 30 to 40 percent price performance advantage vs alternatives

One sentence takeaway: Capacity is the moat, silicon is the margin unlock, agents are the new workload that can make Bedrock feel like the next EC2 moment

Takeaways from Azure:

On capacity and constraints:

• AI capacity expanding 80%+ this year; total data-center footprint to double in two years.

• New 2-GW “Fairwater” site in Wisconsin billed as world’s most powerful AI data center.

• First hyperscaler to deploy NVIDIA GB300s; continuously modernizing a “fungible fleet” across all AI stages (training to inference).

• Software optimizations increased GPT-4.1 and GPT-5 token throughput 30%+ per GPU - efficiency is becoming the moat.

AI Demand

• Over 900M MAUs using AI features; 150M active Copilot users across M365, GitHub, and security.

• M365 Copilot adoption up 50% QoQ; 90% of Fortune 500 using it.

• GitHub Copilot at 26M users, 80% of new devs adopt it within a week.

• Enterprise customers (PwC, Accenture, EY, Lloyds) scaling seats fast and reporting tangible productivity gains.

OpenAI Relationship

• Microsoft has 10x’d its investment; OpenAI contracted $250B in new Azure spend

• Extends rev-share, IP, and API exclusivity through 2030–2032.

• Azure remains OpenAI’s exclusive cloud until AGI (or at least the end of the decade).

One sentence takeaway: Microsoft is short on capacity, not demand

Takeaways from Google:

On capacity and constraints:

• Still supply constrained

• Management expects a tight demand supply environment through Q4 and into 2026 despite speeding server deployments and data center construction.

• Cloud demand is the driver, with AI infra and Gemini solutions leading bookings and backlog

• 2025 CapEx raised to 91 to 93B (from 85B). 2026 CapEx will “increase significantly”

• Anthropic plans to access up to 1 million TPUs, reinforcing the need to keep adding capacity.

AI Demand

• Management explicitly tied the 34% YoY Cloud growth and 46% QoQ backlog jump to enterprise AI demand.

• Enterprise AI products are generating “billions in quarterly revenue” across infrastructure and Gemini-based solutions.

• Generative AI product revenue up 200% YoY, fueled by adoption of Gemini, Veo, Imagine, and Chirp.

• Nearly 150 enterprise customers each processed ~1 trillion tokens in the past year using Google models for tasks like marketing, personalization, and analytics.

• Gemini API usage is massive – 7 billion tokens per minute processed; Gemini app at 650M MAUs with 3x query growth since Q2.

• AI is expanding search demand, not cannibalizing it – AI overviews and AI Mode drove incremental total and commercial query growth.

Quarterly Reports Summary

Top 10 EV / NTM Revenue Multiples

Top 10 Weekly Share Price Movement

Update on Multiples

SaaS businesses are generally valued on a multiple of their revenue - in most cases the projected revenue for the next 12 months. Revenue multiples are a shorthand valuation framework. Given most software companies are not profitable, or not generating meaningful FCF, it’s the only metric to compare the entire industry against. Even a DCF is riddled with long term assumptions. The promise of SaaS is that growth in the early years leads to profits in the mature years. Multiples shown below are calculated by taking the Enterprise Value (market cap + debt - cash) / NTM revenue.

Overall Stats:

• Overall Median: 5.1x

• Top 5 Median: 24.9x

• 10Y: 4.1%

Bucketed by Growth. In the buckets below I consider high growth >25% projected NTM growth, mid growth 15%-25% and low growth <15%

• High Growth Median: 26.3x

• Mid Growth Median: 7.5x

• Low Growth Median: 4.0x

EV / NTM Rev / NTM Growth

The below chart shows the EV / NTM revenue multiple divided by NTM consensus growth expectations. So a company trading at 20x NTM revenue that is projected to grow 100% would be trading at 0.2x. The goal of this graph is to show how relatively cheap / expensive each stock is relative to its growth expectations.

EV / NTM FCF

The line chart shows the median of all companies with a FCF multiple >0x and <100x. I created this subset to show companies where FCF is a relevant valuation metric.

Companies with negative NTM FCF are not listed on the chart

Scatter Plot of EV / NTM Rev Multiple vs NTM Rev Growth

How correlated is growth to valuation multiple?

Operating Metrics

• Median NTM growth rate: 12%

• Median LTM growth rate: 14%

• Median Gross Margin: 76%

• Median Operating Margin (2%)

• Median FCF Margin: 18%

• Median Net Retention: 108%

• Median CAC Payback: 32 months

• Median S&M % Revenue: 37%

• Median R&D % Revenue: 24%

• Median G&A % Revenue: 15%

Comps Output

Rule of 40 shows rev growth + FCF margin (both LTM and NTM for growth + margins). FCF calculated as Cash Flow from Operations - Capital Expenditures

GM Adjusted Payback is calculated as: (Previous Q S&M) / (Net New ARR in Q x Gross Margin) x 12. It shows the number of months it takes for a SaaS business to pay back its fully burdened CAC on a gross profit basis. Most public companies don’t report net new ARR, so I’m taking an implied ARR metric (quarterly subscription revenue x 4). Net new ARR is simply the ARR of the current quarter, minus the ARR of the previous quarter. Companies that do not disclose subscription rev have been left out of the analysis and are listed as NA.

Sources used in this post include Bloomberg, Pitchbook and company filings

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