neocloud · kubernetes · Layer 2.5
CoreWeave
Teams that already run Kubernetes at scale and are buying committed capacity rather than experimenting.
high portability Portable. The workload is standard enough to move to another vendor without a rewrite.
What you are buying
- Business model
- Purpose-built GPU cloud. Owns or leases its own datacentre capacity and sells it directly.
- How you reach it
- You get a cluster. Assumes you already run Kubernetes at scale.
- Accelerators
- H100, H200, GB200 NVL72, B200, A100, L40S
- Regions
- US, EU, UK
- Pricing model
- On-demand and reserved. Reserved is the real product; on-demand list price is high enough that it reads as a discouragement.
- Getting started
- No formal minimum on-demand, but the commercial motion is contracts, not credit cards.
- Capacity
- Large and contracted years ahead. Most capacity is spoken for by a small number of very large customers.
- Ownership
- Public (IPO completed). NVIDIA is an investor.
Verified 2026-09-09. Fields reading “not published” are exactly that — we do not estimate a figure a vendor withholds.
The counterparty
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.
A multi-year GPU commitment is a credit decision wearing a cloud contract. This is the section no benchmark covers and the one that decides what happens to your workload in 2028.
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.
The 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.
No rate is quoted on this page on purpose. Published list prices at this layer move weekly and essentially nobody signing a real contract pays them. Dated, sourced figures live in the GPU cost calculator, where the cheapest and dearest published on-demand price for the identical H100 sit 5.6× apart.
The layers underneath this one
Whatever you rent here is a chip in a building that needs power. In 2026 megawatts, not silicon, are the binding constraint on the whole industry — a frontier rack draws 120–200 kW against a 2026 average near 27 kW, and the US interconnection queue exceeds 2,600 GW.
Layer 2 — Silicon · Layer 1 — Energy · The interconnection queue · Tokens per watt
Compared against
- CoreWeave vs Lambda — Contracted scale against credit-card speed — and a list price roughly three times higher.Compare with Lambda
- CoreWeave vs Nebius — Two public companies at this layer, so for once you can read both sets of numbers before signing.Compare with Nebius
- CoreWeave vs AWS (P5, P6, G6) — The 30-40% discount that is the neocloud pitch, against counterparty certainty.Compare with AWS (P5, P6, G6)
- GMI Cloud vs CoreWeave — Roughly $2.60 against roughly $6.31 on an H200 — a $2,664 monthly gap per GPU at continuous run.Compare with GMI Cloud
- Oracle Cloud (OCI) vs CoreWeave — Bare-metal RDMA clusters from a hyperscaler against a specialist.Compare with Oracle Cloud (OCI)