Your AI training job is queued on AWS. You're waiting. The bill is climbing. Meanwhile, a team at CoreWeave just provisioned 512 H100s in under 15 minutes — paying 40% less per GPU-hour.
That gap is real, and it's why more engineering teams are rethinking their AI infrastructure stack.
What's a Neocloud?
Neoclouds are GPU-first cloud providers — CoreWeave, Nebius, Lambda Labs, Voltage Park — built exclusively for AI workloads. No managed databases, no serverless functions, no CDN. Just bare-metal GPU compute at scale, fast.
The Core Tradeoffs
| Hyperscaler | Neocloud | |
|---|---|---|
| GPU availability | Waitlisted | Fast provisioning |
| Pricing | Complex, bundled | Transparent per-GPU-hour |
| Cost vs baseline | — | 30–60% cheaper |
| Service breadth | Thousands of services | Compute-focused |
| Compliance | Extensive | Growing |
When to use a Neocloud
- Pure AI training / fine-tuning / inference workloads
- When GPU availability is blocking your team
- When you want bare-metal performance without virtualization overhead
When to stick with a Hyperscaler
- AI workload is tightly coupled with managed services (RDS, Lambda, etc.)
- Multi-region compliance requirements today
- Team bandwidth is limited
Most mature teams in 2026 are running hybrid — neocloud for training, hyperscaler for the application stack.
I wrote a full breakdown with provider comparisons (CoreWeave vs Nebius vs Lambda vs Nscale) here:
Neocloud vs Hyperscaler: 2026 Enterprise Guide
Top comments (0)