Azure is one of the main cloud platforms funding and serving AI workloads.
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Price
$402.29
1D change
+2.15%
Market cap
$2.99T
Sector
Technology
Oracle Cloud Infrastructure is increasingly used for large AI training and inference clusters.
Price
$121.38
1D change
-3.98%
Market cap
$349.63B
Sector
Technology
CoreWeave is a pure-play AI cloud focused on GPU capacity for training and inference workloads.
Price
$73.06
1D change
-0.20%
Market cap
$39.86B
Sector
Technology
AI applications depend on governed, usable enterprise data, which is Snowflake's core layer.
Price
$274.34
1D change
+2.02%
Market cap
$95.09B
Sector
Technology
| Metric | MSFT | ORCL | CRWV | SNOW |
|---|---|---|---|---|
| Price | $402.29 | $121.38 | $73.06 | $274.34 |
| 1D Change | +2.15% | -3.98% | -0.20% | +2.02% |
| Market Cap | $2.99T | $349.63B | $39.86B | $95.09B |
| Enterprise Value | $2.97T | $505.16B | $72.82B | $93.02B |
| Trailing P/E | 24.0 | 20.8 | -26.9 | -77.9 |
| Forward P/E | 20.8 | 11.2 | -49.9 | 101.9 |
| PEG Ratio | 1.21x | 0.71x | — | 6.95x |
| Price / Sales | 9.4 | 5.2 | 6.4 | 18.9 |
| EV / Revenue | 9.3 | 7.5 | 11.7 | 18.5 |
| Revenue Growth | 18.3% | 20.6% | 111.6% | 33.5% |
| Earnings Growth | 23.4% | 21.9% | — | — |
| Gross Margin | 68.3% | 65.8% | 69.4% | 67.1% |
| Operating Margin | 46.3% | 36.2% | -6.9% | -22.2% |
| Net Margin | 39.3% | 25.4% | -25.6% | -23.8% |
| ROE | 34.0% | 53.4% | -40.7% | -54.9% |
| Free Cash Flow | $37.01B | $-24.54B | $-8.56B | $1.74B |
| FCF Margin | 11.6% | -36.4% | -137.5% | 34.6% |
| Debt / Equity | 0.30x | 3.89x | 7.39x | 1.43x |
| Current Ratio | 1.28x | 1.11x | 0.32x | 1.05x |
| Dividend Yield | 0.90% | 1.65% | 0.00% | 0.00% |
| Next Earnings | Jul 29, 2026 | Sep 09, 2026 | — | Aug 26, 2026 |
| Quarterly Revenue | $82.89B | $19.18B | $2.08B | $1.39B |
| Revenue QoQ | +2.0% | +11.6% | +32.2% | +8.3% |
| Quarterly Net Income | $31.78B | $4.30B | $-740.0M | $-295.6M |
| Net Income QoQ | -17.4% | +15.7% | -63.8% | +4.5% |
MSFT thesis lens
Enterprise AI platform
Why it could benefit
- Azure is one of the main cloud platforms funding and serving AI workloads.
- Copilot can raise average revenue per user across Microsoft 365, GitHub, Dynamics, and security products.
- Its distribution into nearly every large enterprise makes AI attach rates especially valuable.
Moat / edge
- Massive installed base in productivity and enterprise infrastructure.
- Deep cloud stack plus model partnerships and proprietary tooling.
- Switching costs are high once AI workflows are embedded in daily software.
What to watch
- Azure growth excluding currency and one-time items.
- Copilot user adoption, pricing durability, and seat expansion.
- Capex efficiency versus AI revenue realized.
Key risks
- Capex could stay ahead of monetization for longer than the market expects.
- Competition from Google, Amazon, and specialized AI software vendors.
ORCL thesis lens
AI cloud infrastructure and databases
Why it could benefit
- Oracle Cloud Infrastructure is increasingly used for large AI training and inference clusters.
- Database and enterprise application customers create a natural path for AI agents and automation.
- Sovereign and regulated workloads can prefer Oracle's enterprise footprint.
Moat / edge
- Deep enterprise database installed base.
- Growing cloud infrastructure backlog.
- Strong relationships with regulated industries and governments.
What to watch
- OCI revenue growth and remaining performance obligations.
- Capex intensity versus cloud gross margin.
- AI infrastructure customer concentration.
Key risks
- Cloud buildout requires heavy capital spending.
- Competition from AWS, Azure, and Google Cloud remains intense.
CRWV thesis lens
GPU cloud infrastructure
Why it could benefit
- CoreWeave is a pure-play AI cloud focused on GPU capacity for training and inference workloads.
- It gives the dashboard direct exposure to neocloud demand outside the hyperscalers.
- Fast customer growth can signal whether specialized GPU clouds keep taking share.
Moat / edge
- Purpose-built GPU infrastructure and fast deployment model.
- Deep relationships with AI labs and enterprise AI buyers.
- Focused operating model compared with general-purpose clouds.
What to watch
- Customer concentration and contract duration.
- Debt, capex, and GPU utilization.
- Competitive response from hyperscalers.
Key risks
- High capital intensity can amplify execution risk.
- Demand visibility may change quickly if AI training economics shift.
SNOW thesis lens
Enterprise data cloud for AI
Why it could benefit
- AI applications depend on governed, usable enterprise data, which is Snowflake's core layer.
- Cortex and related AI services can help customers build AI directly against their data.
- Consumption growth can improve as AI workloads expand data usage.
Moat / edge
- Large enterprise data footprint.
- Cross-cloud data platform with strong ecosystem integrations.
- Consumption model can scale with workload growth.
What to watch
- Product revenue growth and net retention.
- Cortex AI adoption.
- Competition from Databricks, hyperscalers, and lakehouse tools.
Key risks
- Optimization by customers can pressure consumption.
- AI monetization may take longer than platform enthusiasm implies.