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About

How we rank & score

neocloud · kubernetes · Layer 2.5

Civo

Kubernetes-native teams who found the hyperscaler console exhausting.

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
A100, L40S, and NVIDIA datacentre parts
Regions
UK, US, EU
Pricing model
Hourly, published, with a strong simplicity pitch.
Getting started
Credit card.
Capacity
Modest.
Ownership
Private.

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

The counterparty

Small provider; the GPU line is one product among several.

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

Small catalogue of parts. If you need the newest silicon this is not where it lives.

The economics

Competitive on the parts it carries, and the pricing is genuinely simple to reason about — rarer than it should be.

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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