June 10, 2026 · 12 min read
Cloud computing is not a theme — it's the operating substrate of the global economy. The migration from on-premise to cloud is roughly 30% complete globally, and every AI workload accelerates it. Here's a full-metrics guide to the best plays across the stack.
AI scores use BriMindInvest's composite signal (20–96 scale). Op. margin shown for cloud/tech segment where reported separately. Data June 2026.
| Ticker | Tier | AI Score | Fwd P/E | Rev Growth | Gross Margin | Op Margin | Buy% | Target ↑ |
|---|---|---|---|---|---|---|---|---|
| AMZN | Hyperscaler | 88 | 38x | +17% | 49% | 38% | 92% | +15% |
| MSFT | Hyperscaler | 85 | 34x | +16% | 70% | 43% | 90% | +12% |
| GOOGL | Hyperscaler | 83 | 20x | +14% | 58% | 30% | 85% | +18% |
| DDOG | Cloud Observability | 76 | 75x | +25% | 80% | 22% | 78% | +18% |
| SNOW | Cloud Data Platform | 72 | 160x | +26% | 67% | — | 65% | +22% |
| CRM | SaaS | 74 | 26x | +9% | 78% | 30% | 76% | +20% |
Azure (43%) best-in-class. AWS (38%) improving. Google Cloud (30%) expanding fastest. SNOW (—) still investing. Higher margins = more durable earnings in a downturn.
The three hyperscalers collectively spent $235B+ on AI-related capex in 2025 and are on track to spend $300B+ in 2026. Each runs a fundamentally different AI strategy:
AWS strategy: Vertical integration — Trainium chips for AI training, Inferentia for inference, Bedrock for model APIs. Broadest enterprise customer base; most AWS AI revenue comes from existing customers expanding workloads rather than new logos.
Azure strategy: OpenAI exclusivity. Azure OpenAI Service carries ~29% of all enterprise-deployed GPT-4/GPT-5 workloads globally. Copilot integration across 500M+ Microsoft 365 seats creates monetization leverage that no other cloud platform can match.
Google Cloud strategy: Gemini-native. GCP's differentiation is its Tensor Processing Units (TPUs) — the only hyperscaler with proprietary AI silicon competitive with NVIDIA at scale. Google Cloud's AI pricing is 15-30% below Azure for comparable inference workloads, driving share gains in price-sensitive enterprise segments.
Datadog (DDOG): The monitoring layer for cloud-native infrastructure. Its LLM Observability product captures AI model latency, cost, and error data — a high-demand product as enterprises struggle to manage AI inference costs. NRR consistently above 120%; 28 modules (vs. 16 three years ago) create continuous upsell surface within the existing base.
Snowflake (SNOW): The structured data layer for AI. Enterprises store their proprietary datasets in Snowflake to fine-tune models, run RAG pipelines, and feed AI applications. New CEO Ramaswamy is accelerating AI product velocity; the consumption-based model is volatile quarter-to-quarter but structurally sound. Highest risk/reward in this cohort.
Salesforce (CRM): Best value in this group at 26x forward earnings. Agentforce (AI agents for CRM automation) is generating strong early enterprise pipeline. At 30% FCF margin with a $5B+ annual buyback, Salesforce offers a rare combination of cheap multiple, AI option value, and shareholder return discipline.
Free side-by-side AI scores, operating margins, revenue growth, and analyst targets for any two cloud stocks.