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About

How we rank & score

hyperscaler · vm · Layer 2.5

Google Cloud (A3, A4)

Teams already on GCP, and anyone who wants TPUs — which exist nowhere else.

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

What you are buying

Business model
A general cloud that also rents accelerators. Most expensive per hour, and the one your compliance team has already approved.
How you reach it
You get virtual machines. The familiar cloud model.
Accelerators
H100, H200, B200, plus TPU v5e/v6/v7
Regions
Global
Pricing model
On-demand, spot, committed use discounts, Dynamic Workload Scheduler.
Getting started
Existing GCP account.
Capacity
Constrained on newest parts; DWS is the queueing mechanism.
Ownership
Alphabet.

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

The counterparty

Effectively none.

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

TPU is the real differentiator and the real lock-in. Choosing a TPU is choosing Google Cloud for the life of that workload; there is no second supplier.

The economics

Spot B200 reported around $6.69/GPU-hour. Committed-use discounts change the picture substantially and are where the actual negotiation happens.

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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