Compare H100 GPU Cloud Providers – AWS, GCP, Azure

In the age of AI-driven enterprises, the demand for high-performance cloud infrastructure has soared. According to industry research, the global GPU‑cloud market is expected to grow by more than 25 % annually through 2028, led by deep‑learning, large language models, and real-time analytics workloads. India, in particular, is rapidly adopting cloud hosting, server‑based GPU deployments, and hybrid architectures to power next‑gen services. Within this backdrop, the NVIDIA H100 GPU has emerged as a game‑changer, offering world‑class performance for AI training and inference.

If you’re evaluating where to deploy H100‑powered cloud instances — whether on Amazon Web Services (AWS), Google Cloud (GCP) or Microsoft Azure — this blog will help you compare how they stack up on pricing, performance, infrastructure, and suitability for enterprise workloads. We'll keep things conversational and practical to help you pick the right fit for your cloud‑hosting and server GPU strategy.

The Big Three: AWS vs GCP vs Azure – H100 Support & Infrastructure

When comparing these major cloud providers for H100 GPU deployments, a few key dimensions matter: availability of H100 instances, regional presence (particularly relevant for India), performance infrastructure (network, interconnect, memory), pricing, and enterprise readiness (SLA, security, compliance).

AWS

AWS offers the P5 instances powered by NVIDIA H100 Tensor Core GPUs. These instances are designed for large‑scale AI and HPC workloads.

AWS announced a price reduction of up to 45% on NVIDIA H100 and related GPU‑accelerated EC2 instance types recently.

Advantages: vast global region coverage, strong enterprise ecosystem, hybrid cloud support, rich managed services for AI and inference pipelines.

Considerations: Pricing can still be high, and for Indian users latency + data‑egress costs must be factored in.

Azure

Microsoft Azure provides the ND H100 v5 / H100 series VM sizes built for AI and HPC. For example, the ND H100 v5 series starts with eight H100 GPUs per VM and supports up to thousands of GPUs with high‑speed interconnect.

One quoted price: the Standard_ND96isr_H100_v5 instance (96 vCPUs, 1900 GiB RAM) starts at ~$102.74/hour.

Advantages: strong enterprise security/compliance posture (important for Indian regulated industries), deep integration with Microsoft’s Azure AI stack.

Considerations: Higher cost per hour, and Indian pricing and regional availability might lag slightly compared to US/EU regions.

GCP

Google Cloud shows H100 models in its GPU pricing guides, with spot or on‑demand price references for H100 instances. For example, spot H100 (A3-HIGH) listed at ~$2.25/hr.

Advantages: Good value, strong data and analytics integrations, flexible pricing options.

Considerations: H100 availability in specific Indian region may need checking; pricing for sustained enterprise workloads can vary widely.

Pricing Comparison – What to Expect

Let’s look at pricing insights across the providers, focusing on H100 GPU instances, and what it means for enterprises in India.

Key Takeaways for Indian enterprises

If you aim to deploy large scale H100 GPU clusters, AWS and Azure offer enterprise‑grade infrastructure, but cost is higher.

If you have lighter or burst‑trained workloads, GCP or specialised cloud GPU providers may offer better cost‑efficiency.

While Indian region pricing may differ (due to power, tax, network, import costs), these rates provide useful benchmarks.

Performance & Infrastructure: Beyond Pricing

Pricing is one dimension — performance and infrastructure quality are equally crucial when using H100 GPUs for enterprise cloud hosting or server workloads.

Interconnect and Multi‑GPU Scaling

Azure’s ND H100 v5 series supports up to thousands of GPUs with dedicated 400 Gb/s InfiniBand interconnects.

Multi‑GPU setups demand high bandwidth, low latency and orchestration support — this is particularly important for deep‑learning training or distributed inference.

AWS P5 instances similarly provide high‑density GPU packs and support for generative AI workloads.

Enterprise‑Grade Compliance & Cloud Hosting Readiness

For Indian enterprises in regulated sectors (finance, healthcare, government), provider support for security, data‑localisation, compliance, backup/disaster‑recovery is non‑negotiable.

AWS, Azure and GCP all provide strong enterprise features — but local data‑centre presence, regional SLA, data‑transfer rates and latency should be checked.

Egress charges and data‑transfer latency can add non‑obvious cost and performance penalties.

Workload Fit: Training vs Inference

If you are training large AI models (LLMs, vision models), the best value comes from cluster‑scale H100 use — multi‑GPU nodes, high memory, strong interconnect.

If you are deploying inference at scale, you may not need full H100 power per instance; mixing GPU types or using smaller config GPUs may be more cost‑effective.

Some cloud GPU benchmarking shows that smaller specialised providers can offer H100 at ~$2‑3/hr in certain markets.

Which Provider Should You Choose? Decision Factors

When trying to pick among AWS, Azure, GCP for H100 GPU cloud instances, evaluate based on these factors:

1. Regional Availability & Latency

If your users are in India or South‑Asia, pick a region close by to reduce latency. Confirm H100 instance availability in that region (availability zones, GPU quotas).

2. Workload Scale & Duration

Short‑term burst training? Use on‑demand smaller config (GCP or specialised provider) to reduce cost.

Long‑term enterprise production? Use reserved or multi‑GPU cluster nodes on AWS or Azure for scale and reliability.

3. Budget and Pricing Model

Consider on‑demand vs reserved vs spot/commitment options. AWS and Azure may offer strong enterprise features but higher baseline cost; GCP may offer better value at smaller scale.

4. Ecosystem & Managed Services

If you need managed ML workflow, model pipelines, inference scaling, integrated analytics — AWS and Azure may offer more features. GCP is strong in data/analytics integration too.

5. Flexibility & Future‑Proofing

Consider whether you might scale up to hundreds or thousands of GPUs. Is the provider prepared for this? Does the infrastructure support multi‑GPU cluster builds, advanced interconnect, orchestration?

6. Hidden Costs

Don’t forget to include data egress, storage, networking, support, and region‑specific power/cooling overhead. A provider may appear cheaper but have higher associated costs.

Conclusion

When it comes to renting H100 GPU cloud servers for cloud hosting and server‑based AI infrastructure, each of the big players — AWS, GCP, and Azure — brings strengths and trade‑offs.

Choose AWS if you want global scale, strong enterprise support, and are ready to invest in high‑end infrastructure; the price per H100 GPU may be higher, but you get full enterprise‑grade features and broad region coverage.

Choose Azure if you need tightly integrated enterprise services, heavy compliance/security posture, and large‑scale GPU clusters; the cost is premium, but the infrastructure and feature set match top‑tier needs.

Choose GCP (or a specialised GPU cloud provider) if you’re working on smaller scale, want better value per GPU, or are running variable workloads where cost‑efficiency matters most.

Ultimately, the right choice depends on your workload profile (training vs inference), scale, budget, regional latency needs, and long‑term vision. With the right strategy, you can harness the full power of H100 GPUs in a cloud‑hosting/server framework, accelerate your AI initiatives, and optimize costs for business impact.