marketplace · vm · Layer 2.5
TensorDock
Cheap VMs with a GPU attached for development and small training runs.
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
- Broad consumer and datacentre mix
- Regions
- Global
- Pricing model
- Marketplace hourly.
- Getting started
- Very low.
- Capacity
- 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
Marketplace 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
Host quality varies. Read the machine specs, not just the price.
The economics
Consistently under the neoclouds because the hardware is somebody else's capital.
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
- TensorDock vs RunPod — Two marketplaces, and how much curation is worth paying for.Compare with RunPod
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