Azure is one of the main cloud platforms funding and serving AI workloads.
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Price
$497.75
1D change
-1.37%
Market cap
$3.70T
Sector
Technology
Oracle Cloud Infrastructure is increasingly used for large AI training and inference clusters.
Price
$150.59
1D change
+2.00%
Market cap
$455.34B
Sector
Technology
CoreWeave is a pure-play AI cloud focused on GPU capacity for training and inference workloads.
Price
$79.88
1D change
+3.00%
Market cap
$44.06B
Sector
Technology
AI applications depend on governed, usable enterprise data, which is Snowflake's core layer.
Price
$338.39
1D change
+2.49%
Market cap
$119.38B
Sector
Technology
| Metric | MSFT | ORCL | CRWV | SNOW |
|---|---|---|---|---|
| Price | $497.75 | $150.59 | $79.88 | $338.39 |
| 1D Change | -1.37% | +2.00% | +3.00% | +2.49% |
| Market Cap | $3.70T | $455.34B | $44.06B | $119.38B |
| Enterprise Value | $3.75T | $592.79B | $90.13B | $119.67B |
| Trailing P/E | 27.3 | 22.4 | -23.5 | -104.1 |
| Forward P/E | 21.1 | 13.7 | -41.0 | 112.3 |
| PEG Ratio | 1.59x | 0.80x | — | 8.32x |
| Price / Sales | 11.1 | 6.3 | 5.8 | 22.0 |
| EV / Revenue | 11.3 | 8.3 | 11.9 | 22.0 |
| Revenue Growth | 17.7% | 29.6% | 112.5% | 35.1% |
| Earnings Growth | 31.7% | 54.5% | — | — |
| Gross Margin | 67.9% | 64.0% | 67.4% | 67.0% |
| Operating Margin | 45.1% | 35.6% | -1.9% | -17.0% |
| Net Margin | 40.3% | 26.4% | -25.4% | -20.1% |
| ROE | 34.0% | 41.2% | -43.6% | -48.1% |
| Free Cash Flow | $16.55B | $-45.85B | $-9.09B | $1.74B |
| FCF Margin | 5.0% | -63.9% | -119.7% | 32.0% |
| Debt / Equity | 0.29x | 2.52x | 10.27x | 1.29x |
| Current Ratio | 1.23x | 1.17x | 0.46x | 0.94x |
| Dividend Yield | 0.73% | 1.33% | 0.00% | 0.00% |
| Next Earnings | Oct 28, 2026 | Dec 10, 2026 | Nov 11, 2026 | Dec 02, 2026 |
| Quarterly Revenue | $90.01B | $19.34B | $2.58B | $1.55B |
| Revenue QoQ | +8.6% | +0.8% | +23.9% | +11.2% |
| Quarterly Net Income | $35.77B | $4.76B | $-626.0M | $-191.7M |
| Net Income QoQ | +12.5% | +10.6% | +15.4% | +35.1% |
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.