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What’s a Hyperscaler and Why AI Workloads Strain It
Hyperscalers offer scale and a wide range of services, but they often fall short for AI-specific tasks:
- Expensive GPUs (e.g., H200 instances over $12/hr)
- Shared environments that can lead to performance issues
- High egress fees for moving data
- Generic support without GPU specialization
They work well for general workloads, but AI needs more precision.
The Rise of Specialized GPU Clouds
Providers like CoreWeave, Lambda Labs, and Corvex focus solely on high-performance AI compute. Their benefits include:
- Bare-metal or virtual machines with NVIDIA H200, B200, and GB200 GPUs
- InfiniBand, NVLink, and optimized architecture
- Clusters tuned for LLMs and generative AI
- No hidden costs like egress fees
- Dedicated support teams with real AI expertise
These clouds are built for AI from day one.
Performance: What Actually Matters
To train modern models efficiently, you need:
- The latest GPUs (H200, B200, GB200)
- High-speed networking like InfiniBand
- Architectures that minimize latency and maximize throughput
Hyperscalers often use virtual networks that slow things down. AI-native clouds prioritize low-latency, high-speed designs.
Pricing: Look Beyond the Hourly Rate
Don’t compare only hourly costs—look at the total cost to train or serve models.
| Provider | H200 Price | Network | Data Egress | Support |
|---|---|---|---|---|
| AWS | ~$12/hr | EFA | Extra | Tiered support |
| Azure | ~$7/hr | InfiniBand (NDv5) | Extra | Standard tiers |
| GCP | ~$11/hr | Ethernet | Extra | Variable |
| Corvex | ~$3/hr | Rail-Aligned InfiniBand | None | White glove support |
Running faster with specialized infrastructure means fewer hours billed.
Support: You Need the Right Humans
Hyperscalers offer support tickets and documentation. When your training fails at 2am, that’s not enough.
AI-native GPU clouds offer:
- 24/7 access to AI infrastructure experts
- Setup help before jobs run
- Real-time support when things go wrong
This is crucial if your team lacks deep MLOps experience.
Security, Compliance, and Deployment Options
AI-native clouds often offer:
- Confidential compute (encrypted memory during runtime)
- SOC 2 and HIPAA compliance
- Fully dedicated clusters (no shared infrastructure)
- Hybrid deployments, including on-premise
Hyperscalers offer some of this—but often at extra cost or complexity.
When a Hyperscaler Still Makes Sense
Choose a hyperscaler if:
- You rely on their broader services (like S3 or BigQuery)
- Model size and speed aren’t critical
- You need global infrastructure for non-AI tasks
They’re still a strong default for general-purpose needs.
When to Use an AI-Native GPU Cloud
Choose an AI-native GPU cloud if:
- You train or serve large models
- You care about cost per token, not just hourly rates
- You want early access to the latest GPUs
- Egress fees are a pain
- You want real infrastructure partners, not just a login
Top AI teams are already switching.
TL;DR: Quick Decision Table
| Use Case | Best Fit |
|---|---|
| Training large language models | AI-Native GPU Cloud |
| Serving high-throughput inference | AI-Native GPU Cloud |
| Full-service enterprise workloads | Hyperscaler |
| Global multi-service infra | Hyperscaler |
| Hands-on infrastructure support | AI-Native GPU Cloud |
Looking Ahead
More articles coming soon on benchmarks, inference costs, and running LLMs without owning infrastructure. Stay tuned.
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