AI COMPUTE PRICING

GPU Rental Prices

Hourly GPU rental prices across every major cloud, normalized to canonical SKUs. A leading indicator of AI capex before it reaches earnings.

262,146 OBSERVATIONS · 40 PROVIDERSParquet · JSON · CSVHourly snapshots, daily feature build

262,146

Price observations

Providers

Canonical SKUs

Hourly

Collection cadence

Download Package Contents Build process Verification Audit trail Use cases Delivery Further reading Request a sample

01Download

Inspect a real sample

Representative records in the delivery format, ready to inspect before licensing the full dataset.

Datacenter SKU, on-demand

Representative, not a real quote. Schema is reconstructed from the variables the research paper measures; there is no published schema file. Price is per GPU-hour; the panel keeps price > 0.

gpu_prices.jsonlrepresentative

{
  "observed_at": "2026-05-14T08:00:00Z",
  "provider": "provider-a",
  "canonical_sku": "H100_SXM_80GB",
  "raw_instance_name": "8x H100 80GB SXM5",
  "region": "us-east",
  "price_type": "on_demand",
  "price_per_gpu_hour_usd": 2.05,
  "gpu_count": 8,
  "capacity_available": 12,
  "source": "live_poll"
}

Consumer SKU, marketplace/spot

Representative. Consumer RTX class is the long-continuous-history segment of the panel.

gpu_prices.jsonlrepresentative

{
  "observed_at": "2026-05-14T08:05:00Z",
  "provider": "provider-b",
  "canonical_sku": "RTX_4090_24GB",
  "raw_instance_name": "RTX 4090 24G",
  "region": "eu-central",
  "price_type": "spot",
  "price_per_gpu_hour_usd": 0.31,
  "gpu_count": 1,
  "source": "archived_snapshot"
}

02Schema

Record shape

Every field, its type, whether it can be null, and a representative value.

observed_at

required

When the price was polled or snapshotted.

Type: timestamp
Unit: UTC

Example 2026-05-14T08:00:00Z

provider

required

Cloud or marketplace provider the quote came from.

Type: string

Example provider-a

canonical_sku

required

Normalized GPU identifier after collapsing marketplace spellings.

Type: string

Example H100_SXM_80GB

raw_instance_name

nullable

Original provider listing string before normalization.

Type: string

Example 8x H100 80GB SXM5

region

nullable

Provider region or zone for the quote.

Type: string

Example us-east

price_type

required

on_demand or spot/marketplace term.

Type: string

Example on_demand

price_per_gpu_hour_usd

required

Normalized rental price per single GPU-hour. The panel keeps price > 0.

Type: float
Unit: USD/GPU-hour

Example 2.05

gpu_count

nullable

GPUs in the underlying instance, used to normalize to per-GPU.

Type: int
Unit: GPUs

Example 8

capacity_available

nullable

Currently available, unrented capacity for the SKU.

Type: int
Unit: instances

Example 12

source

nullable

live_poll or archived_snapshot (the panel backfills from archives).

Type: string

Example live_poll

Field	Type	Constraint	Description
observed_at	timestamp · UTC	required	When the price was polled or snapshotted. e.g. 2026-05-14T08:00:00Z
provider	string	required	Cloud or marketplace provider the quote came from. e.g. provider-a
canonical_sku	string	required	Normalized GPU identifier after collapsing marketplace spellings. e.g. H100_SXM_80GB
raw_instance_name	string	nullable	Original provider listing string before normalization. e.g. 8x H100 80GB SXM5
region	string	nullable	Provider region or zone for the quote. e.g. us-east
price_type	string	required	on_demand or spot/marketplace term. e.g. on_demand
price_per_gpu_hour_usd	float · USD/GPU-hour	required	Normalized rental price per single GPU-hour. The panel keeps price > 0. e.g. 2.05
gpu_count	int · GPUs	nullable	GPUs in the underlying instance, used to normalize to per-GPU. e.g. 8
capacity_available	int · instances	nullable	Currently available, unrented capacity for the SKU. e.g. 12
source	string	nullable	live_poll or archived_snapshot (the panel backfills from archives). e.g. live_poll

03What's included

Cross-Provider Price Surface

Normalized hourly spot and on-demand rates per SKU per region across 40 providers. One schema, every cloud, with the source URL retained on every row.

Capacity & Utilization

Provider capacity snapshots - total, available, and rented instances per SKU - a direct read on supply tightness.

Equity & Token Overlay

GPU-hour pricing joined to AI-infrastructure equities, options implied volatility, and DePIN compute tokens.

04Methodology

How it is built

01
Collection
Poll each provider public pricing surface, hourly where available, and backfill from archived snapshots.
02
SKU normalization
Map heterogeneous instance names to a canonical SKU set so prices are comparable across providers - the many marketplace spellings of an 80GB SXM H100 collapse to one identifier.
03
Per-hour price basis
Normalize every quote to USD per GPU-hour and filter the panel to price greater than 0.
04
Fixed-set point-in-time reading
Read trends within a fixed provider set. The panel composition changes over time, so a recent median up-tick can be an artifact of new, pricier providers entering rather than a real price rise; this is treated explicitly.
05
Coverage-depth handling
Consumer GPUs have the longest continuous history; datacenter SKUs are broad in the current cross-section but thin historically. The asymmetry is handled explicitly throughout.

05Evals

How we validate

What each evaluation measures and how it is run. Where no benchmark is published, we show the methodology and say so.

GPU-price to AI-equity signal

Measures

Whether the cross-provider price and dispersion panel predicts the AI-infrastructure equity complex.

Method

Build price and dispersion features; train walk-forward models against forward equity returns; validate with purged k-fold cross-validation and embargo, tested against a label-shuffled null and a naive-momentum baseline.

Result

Honest published null. Under naive evaluation the backtest looked strong; under rigorous evaluation the edge disappears - shuffle-test p-value near 1.0. Verdict: not_ready. We would rather report a null than sell false alpha.

Cross-provider price dispersion

Measures

How widely the same accelerator is priced across the market.

Method

Take the latest price per provider per SKU for SKUs listed by at least three providers; compute the priciest-to-cheapest ratio and coefficient of variation.

Result

Descriptive and real: order-of-magnitude dispersion - 18.3x for H100 SXM, 96.5x for A100 SXM, 100x for RTX-4090, with coefficients of variation between roughly 0.5 and 2.6.

06Graders

Ground truth

What correct means for this data, and how it is established.

Ground truth

Forward realized equity returns of the AI-infrastructure complex for the predictive test; the observed cross-provider quotes themselves for the descriptive layer.

How it is established

Walk-forward modeling of price and dispersion features against forward returns, scored under purged k-fold cross-validation with embargo and benchmarked against a label-shuffled null and a naive-momentum baseline.

Agreement

Not a human-rater task. Significance is measured against the null, and the signal fails it (shuffle-test p near 1.0).

07Application

AI Capex Leading Indicator

Spot-price moves and capacity exhaustion across providers front-run datacenter buildouts and chip demand, visible before guidance or earnings.

Supply-Chain Positioning

Map compute scarcity to the public AI supply chain - GPUs, networking, power, memory - without waiting on quarterly disclosures.

Compute Cost Benchmarking

Track the true market price of every GPU class over time. Benchmark internal compute spend or model the cost curve of a training run.

08Environment & integration

How you load it

Delivery

S3, REST API, Parquet

Formats

Parquet, JSON, CSV

Auth

Licensed for internal research and model development; redistribution requires a separate agreement. Sourced from public pricing surfaces; no PII or MNPI.

Cadence

Hourly snapshots with a daily feature build.

09Related research

The GPU Rental MarketRead →

Request a sample