AI COMPUTE PRICING
GPU Spot Pricing Intelligence
Hourly GPU rental prices across every major cloud, normalized to canonical SKUs. A leading indicator of AI capex before it reaches earnings.
See the data
Representative records in the exact shape we deliver. Real provenance and full slices are shared under license.
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.
{
"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.
{
"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"
}Record shape
Every field, its type, whether it can be null, and a representative value.
| 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 |
Cross-Provider Price Surface
Normalized hourly spot and on-demand rates per SKU per region across 25+ providers. One schema, every cloud.
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.
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.
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.
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).
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.
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.
Request access.
Restricted-scope evaluation access for qualified teams. We share real samples, full schema, and provenance under a mutual NDA.