Neocloud vs On-Prem: When to Build Your Own GPU Cloud

Neoclouds — specialized GPU-as-a-service providers like CoreWeave, Lambda, Nebius, and Crusoe — rent GPUs 50–75% cheaper than hyperscalers, making them the pragmatic choice for bursty or early-stage training. On-prem wins when you sustain high utilization for years or must keep data in-country under the DPDP Act. And "build your own GPU cloud" makes sense when you want to control the whole stack — or become a neocloud yourself, serving GPUs to others.

TL;DR — the decision

• Neocloud (rent): cheapest per-hour for bursty/short work — 50–75% below hyperscalers (Spheron, 2026).
• On-prem (own): wins at >50–60% sustained utilization over 2–3 years, or when DPDP requires in-country data.
• Build your own cloud: when you want full control of the stack, predictable capacity, or to be a GPU provider (neocloud/sovereign).
• Many teams do both: burst to a neocloud, run the steady base on-prem.

What this covers

The three ways to get GPU capacity — rent from a neocloud, own on-prem, or build your own GPU cloud — and when each is right. It extends the on-prem-vs-cloud TCO guide with the neocloud and build-your-own options.

The three models

Neocloud (rent). Specialized GPU clouds — CoreWeave, Lambda, Nebius, Crusoe, Together AI — focus purely on AI infrastructure and price 50–75% below hyperscalers, with H100 available near $1.03–$3/GPU-hour on spot/marketplace (Spheron, 2026). Best for bursty, experimental, or spiky demand where you don't want capex.

On-prem (own). Buying wins when GPUs run at >50–60% utilization over 2–3 years, or when data residency (DPDP, sector rules) requires compute to stay in-country and under your control. → *On-Prem vs Cloud GPU: True TCO for AI Training in India.*

Build your own GPU cloud. A middle path: own the hardware but operate it cloud-style (multi-tenant, scheduled, API-driven). Right when you want predictable capacity and full-stack control, run a large internal platform, or intend to become a provider — a sovereign or commercial neocloud serving others (as India's IndiaAI-backed operators are doing).

The decision, in one table

Table 1 — Rent vs own vs build.

Model	Best for	Economics	Data control
Neocloud (rent)	bursty / early-stage	lowest per-hour, no capex	provider-controlled
On-prem (own)	sustained, regulated	wins >50–60% duty cycle	full, in-country
Build your own cloud	platform / provider	capex + ops, best at scale	full, and you serve others

Assumptions & scope

Prices are 2026 market figures and move quickly; utilization is the dominant TCO variable. A strategy guide, not a quote. Validate against your workload and DPDP obligations.

Where RDP GPU Mart fits

RDP GPU Mart supplies the hardware for the own and build-your-own paths — from single servers to DRACO Scalable-Unit clusters for an internal or sovereign GPU cloud — India-built, INR-transparent, and DPDP-aware. For teams growing past bursty cloud use, that's how you take control of capacity and cost. *(Design an on-prem or private-cloud GPU platform at RDP GPU Mart.)*

FAQ

What is a neocloud? A specialized GPU-as-a-service provider (e.g. CoreWeave, Lambda, Nebius, Crusoe) focused purely on AI infrastructure — typically 50–75% cheaper than hyperscalers.

When should I own instead of rent? When GPUs run at >50–60% utilization for 2–3 years, or when DPDP/data-residency requires in-country, in-control compute.

Why build my own GPU cloud? For full-stack control and predictable capacity, to run a large internal platform, or to become a GPU provider (sovereign/commercial neocloud).

Can I combine models? Yes — a common pattern is running the steady base load on-prem and bursting to a neocloud for peaks.

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Related

• On-Prem vs Cloud GPU: True TCO for AI Training in India
• Sovereign AI in India: Building In-Country GPU Infrastructure
• Reference Architecture: Sovereign AI Cluster (Scalable Unit)

Research log (Rule #1)

1. Spheron (2026) — GPU cloud pricing (neocloud vs hyperscaler, H100 rates). https://www.spheron.network/blog/gpu-cloud-pricing-comparison-2026/ 2. CloudZero (2026) — H100 cost buy/rent/cloud. https://www.cloudzero.com/blog/h100-gpu-cost/ 3. dasroot (2026) — cloud vs owning hardware cost analysis. https://dasroot.net/posts/2026/03/cloud-gpu-rentals-vs-owning-hardware-cost-analysis-2026/ 4. IntuitionLabs (2026) — GPU/system pricing (on-prem capex). https://intuitionlabs.ai/articles/nvidia-ai-gpu-pricing-guide 5. explainX (2026) — India sovereign AI / neocloud operators. https://explainx.ai/blog/india-sovereign-ai-status-indiaai-mission-2026