Layer 2.5 · head to head
CoreWeave vs Lambda
Contracted scale against credit-card speed — and a list price roughly three times higher.
| CoreWeave | Lambda | |
|---|---|---|
| Can you leave | Portable. The workload is standard enough to move to another vendor without a rewrite. | Movable with effort. Expect to redo images, storage wiring and networking. |
| 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. | Private, so you are diligencing a company that does not have to tell you anything. Reported among the group carrying GPU-collateralised debt maturing 2026-2028. |
| What it is | Purpose-built GPU cloud. Owns or leases its own datacentre capacity and sells it directly. | 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. | You get virtual machines. The familiar cloud model. |
| Accelerators | H100, H200, GB200 NVL72, B200, A100, L40S | H100, H200, B200, GH200, A100 |
| Regions | US, EU, UK | US |
| Pricing model | On-demand and reserved. Reserved is the real product; on-demand list price is high enough that it reads as a discouragement. | On-demand by the hour, plus reserved clusters. Pricing is published on the website, which is rarer at this layer than it should be. |
| Getting started | No formal minimum on-demand, but the commercial motion is contracts, not credit cards. | Credit card, single GPU, minutes. |
| Capacity | Large and contracted years ahead. Most capacity is spoken for by a small number of very large customers. | Frequently sold out on the newest parts. Availability is the constraint, not price. |
| Ownership | Public (IPO completed). NVIDIA is an investor. | Private, widely reported as IPO-track. |
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.
Lambda
For: Researchers and small teams who want a real GPU in the next ten minutes without a procurement conversation.
The catch: The thing you want is often unavailable. Reserved capacity solves it and turns the credit-card product into a contract.
Economics: Among the lowest published rates on the newest silicon — reported lowest on B200 in an August 2026 comparison. Cheap when you can get it.
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