Layer 2.5 · head to head
Oracle Cloud (OCI) vs CoreWeave
Bare-metal RDMA clusters from a hyperscaler against a specialist.
| Oracle Cloud (OCI) | CoreWeave | |
|---|---|---|
| Can you leave | Portable. The workload is standard enough to move to another vendor without a rewrite. | Portable. The workload is standard enough to move to another vendor without a rewrite. |
| Counterparty | Effectively none. | Publicly reporting, which is the point — you can read the filings. Carries substantial GPU-collateralised debt with maturities in the 2026-2028 window, and revenue is heavily concentrated in a few customers. Concentration cuts both ways: it funds the buildout and it is the risk. |
| What it is | A general cloud that also rents accelerators. Most expensive per hour, and the one your compliance team has already approved. | Purpose-built GPU cloud. Owns or leases its own datacentre capacity and sells it directly. |
| How you reach it | You get the physical machine. Maximum control, and every layer above it is yours to run. | You get a cluster. Assumes you already run Kubernetes at scale. |
| Accelerators | H100, H200, B200, GB200, A100 | H100, H200, GB200 NVL72, B200, A100, L40S |
| Regions | Global | US, EU, UK |
| Pricing model | On-demand and heavily discounted committed contracts. Bare metal is a first-class product rather than an afterthought. | On-demand and reserved. Reserved is the real product; on-demand list price is high enough that it reads as a discouragement. |
| Getting started | Account, with a sales motion for clusters. | No formal minimum on-demand, but the commercial motion is contracts, not credit cards. |
| Capacity | Aggressively expanded; OCI has won several very large AI contracts. | Large and contracted years ahead. Most capacity is spoken for by a small number of very large customers. |
| Ownership | Oracle. | Public (IPO completed). NVIDIA is an investor. |
Oracle Cloud (OCI)
For: Large training runs that want bare-metal RDMA clusters with hyperscaler counterparty certainty.
The catch: The cheapest hyperscaler on paper and the one whose commercial relationship people most often regret not reading carefully. Read the commit terms.
Economics: Consistently undercuts AWS and Azure on comparable GPU instances, and low egress pricing is a genuine structural difference rather than a promotion.
CoreWeave
For: Teams that already run Kubernetes at scale and are buying committed capacity rather than experimenting.
The catch: List on-demand pricing is among the highest of the neoclouds — widely reported around $6/hr for an H100 where specialists sit near $2. You are not meant to pay list; if you are paying list, you are using it wrong.
Economics: Priced 30-40% under the hyperscalers on comparable committed terms, which is the entire pitch. Against the cheaper specialists it is not a price play at all — it is a scale, network and support play.
Neither table row is a price
Deliberately. Published on-demand rates at this layer move weekly, and essentially nobody signing a real contract pays them — every serious buyer pays less than every list figure either of these companies publishes. Quoting one here would date this page within a month.
The GPU rental price index carries dated, sourced figures instead, and the durable finding there is the spread: the identical H100 rents from roughly $1.38 to $12.29 an hour depending only on who you rent it from.
The layers underneath both
Whichever you pick is renting you chips in a building that needs power. In 2026 that is the constraint that binds: Microsoft has disclosed an Azure backlog it cannot fill for want of megawatts rather than accelerators, and the US interconnection queue exceeds 2,600 GW with roughly 80% of projects withdrawing before they energise.
Layer 2 — Silicon · Layer 1 — Energy · The interconnection queue