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About

How we rank & score

marketplace · vm · Layer 2.5

CUDO Compute

Buyers who want marketplace pricing with a single commercial relationship.

medium portability Movable with effort. Expect to redo images, storage wiring and networking.

What you are buying

Business model
Brokers capacity somebody else owns. Cheapest headline rates, and the machine you get is not the machine you chose.
How you reach it
You get virtual machines. The familiar cloud model.
Accelerators
H100, A100, V100, consumer parts
Regions
Global, distributed supply
Pricing model
On-demand and committed, aggregated from multiple suppliers.
Getting started
Self-serve.
Capacity
Aggregated, therefore variable.
Ownership
Private.

Verified 2026-09-09. Fields reading “not published” are exactly that — we do not estimate a figure a vendor withholds.

The counterparty

You are one layer removed from whoever owns the metal. Ask who that is.

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

Aggregation abstracts away the thing you most need to know for compliance: whose datacentre this actually is.

The economics

Competitive because it arbitrages underused capacity.

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 rental price index, where the spread between the cheapest and dearest seller of the identical chip runs to roughly 9x.

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

Related providers

Sources

Macrostackdoes not benchmark clusters and does not pretend to. For hands-on performance ratings of GPU clouds, SemiAnalysis’s ClusterMAX tests the hardware directly. This page covers what their scorecard does not: who owns the company and how you get out.

The Macrostack brief

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