AWS vs GCP vs Azure: AI GPU Pricing 2026 Comparison

The three big hyperscalers — AWS, GCP, and Azure — all offer H100 GPUs in the same $11-13/hour range for AI workloads in 2026. The differences are in spot pricing, ecosystem integration, and how easy it is to access reserved capacity. This guide compares all three for typical AI workloads (training, inference, fine-tuning) and shows why hyperscalers cost 4-8× more than specialty GPU clouds. For real-time pricing across 12 GPU providers including hyperscalers, use our GPU Pricing Calculator.

If you're price-sensitive, hyperscalers are rarely the right answer. They win when you're already inside a VPC ecosystem and cross-cloud egress would dominate compute savings.

What does each hyperscaler charge for H100 in 2026?

H100 SXM5 80GB per-GPU pricing in us-east-1 equivalent region:

Cloud	Instance	On-demand	Spot	Reserved (1yr)
AWS	p5.48xlarge	$12.29	$6.40	$7.50
GCP	A3 (a3-highgpu-8g)	$11.06	$5.50	$7.20
Azure	ND H100 v5	$12.96	$6.80	$8.00
AWS	p5e.48xlarge (H200)	$14.25	$7.40	$8.60
GCP	A3 Ultra (H200)	$13.40	$7.00	$8.50

The 17% spread between cheapest (GCP) and most expensive (Azure) is real but small in absolute terms. Pick the cloud that minimizes your data egress cost, not raw GPU hourly.

For comparison, non-hyperscaler GPU clouds at the same hardware:

Provider	H100 SXM on-demand
Hyperbolic	$1.49
RunPod Community	$1.99
Vast.ai	$2.40
RunPod Secure	$2.99
Lambda Labs	$2.99
GCP (cheapest hyperscaler)	$11.06
AWS	$12.29
Azure	$12.96

That's a 6-8× gap. Non-hyperscalers charge less because they don't bundle enterprise networking, IAM, regional redundancy, or compliance certifications. For workloads that don't need those, the hyperscaler tax is pure waste.

When does AWS make sense for AI workloads in 2026?

AWS wins for:

• Bedrock managed models. Claude, Llama, Nova, Mistral all available through Bedrock with provisioned throughput. Pricing is competitive after the markup, and BYOK + private link options are unmatched.
• Enterprise compliance. HIPAA, FedRAMP, SOC 2, ISO 27001 — AWS has the broadest certification surface for regulated industries.
• Existing AWS data. If your training data lives in S3, ingesting to another cloud costs egress that often exceeds the compute savings.
• Custom Model Import. AWS Bedrock supports importing custom models (LoRA adapters, full fine-tunes). Useful for fine-tuned models you want to serve through a managed endpoint.

AWS pain points:

• p5 instances are 8-GPU minimums ($98/hour). You can't rent a single H100.
• Reserved capacity has 1-year minimum commits and 90%+ utilization to make economics work.
• Spot pricing fluctuates and can spike during demand peaks.

When does GCP make sense?

GCP wins for:

• AI Studio + Vertex AI integration. The best out-of-the-box experience for evaluating, deploying, and monitoring AI models. Gemini API is cheapest direct path to long-context (1M tokens).
• TPUs. If you're training research-scale models, Google TPU v5e/v6 are competitive with H100 at a fraction of the price.
• A3 Ultra (H200 cluster). Best access to H200 capacity in 2026. AWS p5e is supply-constrained; Azure ND H200 v6 still rolling out.
• Multi-region serving. GCP's networking for inference serving is cleaner than AWS for latency-sensitive workloads.

GCP pain points:

• Compute Engine GPU pricing has 8-GPU minimums similar to AWS p5.
• Preemptible (spot) GPUs have shorter median uptime than AWS spot.
• Vertex AI markups vs Compute Engine raw can be 30-50%.

When does Azure make sense?

Azure wins for:

• OpenAI Service integration. Azure OpenAI offers GPT-4o, GPT-5, o3 with Microsoft enterprise SLAs and provisioned throughput. Best path if you need OpenAI models on Azure VPC.
• Microsoft 365 + Copilot ecosystem. If your business is built on Microsoft stack, AI integration is tightest on Azure.
• Enterprise sales motion. Microsoft sales reps will negotiate enterprise pricing aggressively. AWS and GCP also do, but Microsoft is most flexible.
• Mistral, Cohere partnerships. Azure AI Foundry hosts Mistral models, Cohere Embed v3 with native Azure billing.

Azure pain points:

• ND H100 v5 has supply constraints in major regions (often waitlisted).
• Spot ("Low Priority") capacity is significantly less than AWS or GCP.
• Documentation quality lags AWS and GCP, especially for newer AI services.

What about cross-cloud egress fees?

The hidden cost that determines real total bill:

Source	Destination	$/GB
AWS → Internet		$0.05-0.09
AWS → GCP		$0.08
AWS → Azure		$0.08
GCP → Internet		$0.08-0.12
GCP → AWS		$0.08
GCP → Azure		$0.08
Azure → Internet		$0.05-0.08

For a high-throughput inference workload streaming 1KB responses to users:

100k requests/day × 1KB = 100MB/day
Monthly: 3GB
Egress cost at $0.09/GB: $0.27/month — trivial

But: large multimodal outputs (audio/video)
1 video file 10MB × 100k requests/day = 1TB/day = 30TB/month
At $0.09/GB: $2,700/month — significant

Egress matters for: file generation (images/video), large response payloads, cross-region replication, data warehouse exports. Egress doesn't matter for: chat/text-only outputs.

What is the cheapest hyperscaler total bill for typical AI workloads?

Three reference workloads with full hyperscaler bill (not just GPU):

Workload 1: B2B SaaS chatbot, 100k requests/day, AWS

GPU (Bedrock for Claude Sonnet 4.6): pay-per-token, ~$1,500/mo
Storage (S3 for embeddings): 50GB, ~$1/mo
Egress (1TB to internet): $90/mo
Lambda for orchestration: $50/mo
Total: ~$1,641/mo

Same workload on RunPod self-hosted Llama 4 70B:

GPU rental (H100 SXM ×2): $4,300/mo
Storage (Vast.ai S3 alternative): $5/mo
Egress: ~$50/mo (less aggressive pricing)
Orchestration: free or self-host
Total: ~$4,355/mo

For this workload, hyperscaler wins because managed inference is way more efficient per-token than running your own GPU 24/7.

Workload 2: Fine-tuning a 70B model from scratch, GCP

Training: 8× H100 SXM × 100 hours = $11,060
Spot 8× H100 × 100 hours = $5,500 (with checkpointing)
Cross-region transfer of training data: $200
Storage (1TB checkpoint): $20/mo
Total one-time: ~$5,800 with spot

Same workload on RunPod or CoreWeave:

Training: 8× H100 SXM × 100 hours = $2,392 - $2,640
Storage: $5/mo (Vast)
Total: ~$2,700

For training, specialty clouds win 2-3× because hyperscalers price compute as if you're using their full enterprise stack.

Workload 3: 24/7 production inference at scale (10M req/day)

AWS p5 with 8× H100, dedicated capacity (Provisioned Throughput): ~$11,000/mo
But: 10M req/day at $0.012/req via Bedrock = $3,600,000/mo

At this scale, neither bare GPU nor pay-per-token works — you need a custom enterprise contract. Both hyperscalers and specialty clouds will negotiate. AWS Bedrock often wins because of provisioned throughput economics; CoreWeave wins for self-serving Llama models.

What is the right architecture for 2026?

The mature pattern that minimizes cost while keeping operational sanity:

1. Storage in your primary cloud (AWS/GCP/Azure) for compliance and integration.
2. Inference for high-volume through managed APIs (OpenAI, Anthropic, Google direct, or hyperscaler Bedrock/Vertex/Foundry). Pay-per-token wins below ~500M tokens/month.
3. Inference for custom models or above 500M tokens/month on RunPod, Together, or Fireworks. Save 4-8× on raw GPU costs.
4. Training and fine-tuning on Crusoe, Lambda Labs, or CoreWeave. Save 2-3× on hyperscaler list price.
5. Egress paths minimized through region-local serving and aggressive caching.

For full cost forecasting across this multi-cloud architecture, use our Agent Dev Cost Calculator for the agent layer and GPU Pricing Calculator for the compute layer. For token pricing on managed inference, the Token & Pricing Comparator covers Bedrock + Vertex + AI Foundry rates alongside direct provider pricing.

The hyperscaler tax is real but worth paying when you genuinely need their ecosystem. The rest of the time, specialty GPU clouds offer the same hardware at a fraction of the price.