Pick AWS if…
You already run on AWS and need production inference at scale. Trainium 2 is the cheapest serious AI accelerator if you can optimize for it. p5/p6 instances are list-priced highest but operationally simplest for AWS-native teams.
[Comparison] · Hyperscalers
AWS vs Azure vs Google
The three AI hyperscalers compared head-to-head — silicon roadmap, pricing, fabric, regions, and best-fit workloads in 2026.
[AI Summary]
AWS, Azure, and Google are the three AI hyperscalers. AWS offers the broadest silicon menu — H100 p5, Trainium2, and EFAv3 networking — plus the deepest enterprise stack. Azure is the default for OpenAI-style workloads on NVIDIA, with ND H100 v5 and ND GB200 v6 capacity tied to OpenAI's roadmap. Google leads on custom silicon with TPU v5p and v6 plus a tightly integrated Vertex AI stack.
AWS
Broadest cloud, Trainium silicon
- Founded
- 2006
- Best for
- Production inference at scale
Azure
OpenAI-aligned hyperscaler
- Founded
- 2010
- Best for
- OpenAI-style frontier training
Google Cloud
TPU-native AI cloud
- Founded
- 2008
- Best for
- TPU + Gemini-scale training
| Dimension | AWS | Azure | Google Cloud |
|---|---|---|---|
| Flagship AI silicon | NVIDIA H100/H200/B200 + Trainium 2 | NVIDIA H100/H200/B200 + GB200 NVL72 | TPU v5p / TPU v6 Trillium + NVIDIA B200 |
| H100 instance | p5.48xlarge (8× H100) | ND H100 v5 | A3 (8× H100) |
| H100 on-demand $/hr (8-GPU) | ~$32.77 | ~$29.36 | ~$29.39 |
| B200 instance | p6-b200 (8× B200) | ND GB200 v6 | A4 (8× B200) |
| Custom AI silicon | Trainium 2 / Inferentia 2 | Maia 100 | TPU v5p, TPU v6 Trillium |
| Network fabric | EFAv3 (RoCE) | InfiniBand NDR + Quantum-X800 | Jupiter (custom optical) |
| AI regions | 30+ regions, p5 in 10+ | 60+ regions, ND in 8+ | 40+ regions, A3 in 15+ |
| Anchor AI customer | Anthropic, Perplexity | OpenAI | DeepMind / Gemini |
| AI capex 2025 | ~$100B | ~$80B | ~$75B |
| Best for | Production inference + AWS-native teams | OpenAI-style training, enterprise GPT | TPU training, Gemini, search |
[Verdict]
Pick Azure if…
You're building on OpenAI APIs or want first access to OpenAI-aligned hardware (GB200 NVL72 capacity is concentrated on Azure). Best enterprise AI compliance posture (FedRAMP High, EU Sovereign Cloud).
Pick Google Cloud if…
You want TPU v5p / v6 for training, the lowest list-priced 8×H100 instances, or to fine-tune on Gemini. Google's Jupiter network is the most advanced optical fabric in production.
Cost note
Hyperscalers charge a 50–100% premium over neoclouds for the same GPUs. If raw $/GPU-hr is the constraint, see our Best GPU Clouds list. Hyperscalers earn their premium with integration, compliance, and 30+ region presence.
[FAQ]
Frequently asked questions
- Which hyperscaler has the cheapest H100?
- Azure (~$29.36/8-GPU instance/hr) and Google Cloud (~$29.39) lead on list pricing. AWS p5 is highest at ~$32.77. Reserved Savings Plans / Committed Use Discounts cut 40–60% from list.
- Which hyperscaler is best for OpenAI workloads?
- Azure — it runs OpenAI's production training and inference. Azure OpenAI Service is the only first-party access to GPT-4 / o-series models with enterprise SLAs.
- Is custom silicon (Trainium, TPU, Maia) cheaper than NVIDIA?
- Yes, 30–60% cheaper per useful FLOP for well-tuned workloads. Trade-off is engineering investment to port from CUDA. Google's TPU v6 has the most mature non-NVIDIA stack.
- Which has the largest AI infrastructure investment?
- AWS ($100B capex in 2025) leads, followed by Azure ($80B) and Google ($75B). All three are building gigawatt AI factories — Project Rainier (AWS), Mt Pleasant + Stargate (Azure), Red Oak (Google).
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