fastgpu/cloud-gpu-prices

Cloud GPU Rental Prices (FastGPU)

What it costs to rent a GPU in the cloud, per GPU per hour, across marketplaces, neoclouds and hyperscalers. Mirrored here once a day from fastgpu.co/dataset, where the same files are served live and every price links to the provider it came from. Browse the prices themselves at fastgpu.co.

A free, openly licensed dataset of GPU cloud rental prices across the whole market: marketplaces, neoclouds, and hyperscalers. It contains a normalized snapshot of every current live offer (provider, GPU model, VRAM, offer type, region, per-GPU-hour price, availability) plus a daily price time-series with each day's floor, average, and high price per GPU, and a frozen daily fixing per GPU (floor, on-demand median, spot floor and hyperscaler median, never revised). Sourced from live provider feeds and refreshed daily, it is the reference dataset for tracking cloud GPU cost over time, benchmarking providers, and FinOps and AI-infrastructure research. Published by FastGPU under CC BY 4.0.

Snapshot of 2026-10-08 07:22 UTC: 396 offers (396 with a price) from 29 providers across 40 GPU models. Files generated 2026-10-08 07:30 UTC.

Files

Subset	File	Rows	Days	One row is
current	gpu-prices-current.csv	396		One live offer: provider, GPU model, offer type, region, price per GPU-hour, the GPUs you must rent for that price, stock, and a link to rent it.
daily	gpu-prices-daily.csv	22,700	2026-08-09 to 2026-10-08	One provider's price for one GPU, offer type and region on one UTC day: that day's lowest, average and highest observed price.
fixings	gpu-price-fixings.csv	160	2026-10-05 to 2026-10-08	One GPU model on one day at 07:00 UTC: the market floor, the on-demand median, the spot floor and the hyperscaler median, frozen and never revised.

The daily file is a rolling window of the last 60 days. The fixings file keeps every fixing since the first one: a fixing is frozen at 07:00 UTC and never revised, and a day whose run was missed stays missing.

Load it

from datasets import load_dataset

current = load_dataset("fastgpu/cloud-gpu-prices", "current", split="train")
daily = load_dataset("fastgpu/cloud-gpu-prices", "daily", split="train")
fixings = load_dataset("fastgpu/cloud-gpu-prices", "fixings", split="train")

import pandas as pd

df = pd.read_csv("hf://datasets/fastgpu/cloud-gpu-prices/gpu-prices-current.csv")
cheapest = df[df.gpu_model == "H100 SXM"].sort_values("price_usd_hr").head(10)

Prices are per GPU per hour in US dollars. min_gpu_count says how many GPUs you must rent to pay that rate (8 means the price is only sold as a whole 8-GPU machine), and providers that bill CPU, memory or storage on top of the GPU are named in the column notes below, so compare like with like.

Columns

current (gpu-prices-current.csv)

Column	Type	Description
provider	string	Provider slug, e.g. runpod, vast, lambdalabs.
provider_label	string	Human-readable provider name.
gpu_model	string	Canonical GPU model, e.g. H100 SXM, RTX 4090.
gpu_slug	string	URL slug for the model on fastgpu.co/gpus/{slug}.
vram_gb	integer	GPU memory in GB (blank if not exposed).
offer_type	string	on-demand, community, spot, reserved, or serverless.
region	string	Data-center region (blank when global/unspecified).
price_usd_hr	number	Per-GPU price in USD/hour (blank if listed but unpriced). Each price is the provider's own per-GPU rate. Most providers include the machine's CPU and memory in it (storage is often billed separately); Modal, Cudo Compute, TensorDock, Daytona price the GPU alone and bill CPU and memory on top.
min_gpu_count	integer	GPUs in the instance this per-GPU price is quoted for: to pay this rate you rent at least this many GPUs (1 = a single GPU; 8 = a whole 8-GPU machine such as a hyperscaler VM). The price stays per GPU-hour.
available_count	integer	Instances currently available (blank if not exposed).
interconnect	string	nvlink or infiniband when known (blank otherwise).
source_url	string	Deep link to the provider's rental page for this offer.
fetched_at	datetime	ISO 8601 timestamp when this offer was last observed.

daily (gpu-prices-daily.csv)

Column	Type	Description
day	date	UTC calendar day (YYYY-MM-DD).
provider	string	Provider slug.
gpu_model	string	Canonical GPU model.
offer_type	string	on-demand, community, spot, reserved, or serverless.
region	string	Data-center region (blank when global/unspecified).
min_price_usd_hr	number	Lowest per-GPU USD/hour observed that day.
avg_price_usd_hr	number	Average per-GPU USD/hour observed that day.
max_price_usd_hr	number	Highest per-GPU USD/hour observed that day.
observations	integer	Number of price observations backing the day.

fixings (gpu-price-fixings.csv)

Column	Type	Description
fixing_date	date	UTC day of the fixing (YYYY-MM-DD). One row per GPU model per day; a day whose run was missed has no rows and is never backfilled.
gpu_model	string	Canonical GPU model, e.g. H100 SXM (the form factor is part of the name).
gpu_slug	string	URL slug for the model on fastgpu.co/gpus/{slug}.
floor_usd_hr	number	Cheapest posted on-demand price per GPU-hour outside the hyperscalers at the fixing snapshot, in-stock offers first. Blank when no such offer existed.
floor_provider	string	Slug of the provider that set the floor.
floor_provider_label	string	That provider's name.
floor_offer_type	string	on-demand, or community (a marketplace's shared-host tier, rented the same way).
floor_min_gpu_count	integer	GPUs in the instance the floor price is sold as (1 = a single GPU). The price stays per GPU-hour.
floor_billed_separately	string	What the floor's provider bills on top of its per-GPU rate (CPU, memory, storage). Blank when the rate includes CPU and memory.
floor_availability	string	Availability of the floor offer: in-stock, low, out or unknown. out means every eligible offer was sold out, so the floor is the cheapest sold-out price.
on_demand_median_usd_hr	number	Median of each provider's lowest on-demand price per GPU-hour, over the same providers the floor is drawn from (hyperscalers excluded). For an even count, the mean of the two middle prices.
on_demand_providers	integer	Providers behind the floor and the on-demand median.
spot_floor_usd_hr	number	Cheapest interruptible (spot) price per GPU-hour on any provider, hyperscalers included.
spot_floor_provider	string	Slug of the provider that set the spot floor.
spot_floor_provider_label	string	That provider's name.
spot_floor_min_gpu_count	integer	GPUs in the instance the spot floor is sold as (hyperscaler spot is often a whole multi-GPU machine).
spot_floor_billed_separately	string	What the spot floor's provider bills on top of its per-GPU rate. Blank when the rate includes CPU and memory.
spot_floor_availability	string	Availability of the spot floor offer: in-stock, low, out or unknown.
spot_providers	integer	Providers behind the spot floor.
hyperscaler_median_usd_hr	number	Median of each hyperscaler's lowest on-demand price per GPU-hour (AWS, Azure, Google Cloud, Oracle Cloud).
hyperscaler_providers	integer	Hyperscalers behind that median.
on_demand_offers	integer	Eligible on-demand offers outside the hyperscalers, sold-out ones included.
stock_known_offers	integer	Of those, the offers whose availability the provider exposes: the denominator of the in-stock share.
in_stock_offers	integer	Of those, the offers in stock (low stock counts as in stock): the numerator of the in-stock share.
carried_providers	integer	Contributing providers whose price was not observed within 30 minutes of the snapshot and was carried from an earlier observation (0 on a clean run).
degraded_providers	string	Providers the feed flagged as degraded at the snapshot, comma separated (blank on a clean day).
snapshot_at	datetime	ISO 8601 time the snapshot behind the fixing was observed.
method_version	string	Version of the published method the row was computed under (fastgpu.co/methodology/fixings).

How the data is collected

• Each provider is read from its own API, price feed or published rate card. Sources, collection method and terms per provider: data provenance.
• How offers are normalized, compared and ranked: methodology. How a daily fixing is taken: fixings method.
• Known errors are corrected in the published files and listed with their dates: corrections.
• Dated list-price changes: price-change log. Monthly figures: State of GPU prices report.

Updates and live access

This repository is updated once a day, after the 07:00 UTC fixing, by one commit that replaces all three files. For prices that move during the day, filters and the workload-to-GPU matcher, use the free REST API and MCP server. A longer history and raw observations are part of the paid plans.

License and citation

Creative Commons Attribution 4.0 International (CC BY 4.0). Use it in products, research or reporting, commercially too; credit FastGPU with a link:

Data: FastGPU GPU cloud pricing dataset (https://fastgpu.co/dataset), CC BY 4.0.

Archived, versioned copies have a permanent DOI on Zenodo: 10.5281/zenodo.22842387 (it always resolves to the latest version).

@misc{fastgpu_gpu_cloud_pricing,
  author    = {{FastGPU}},
  title     = {GPU Cloud Pricing Dataset},
  year      = {2026},
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.22842387},
  url       = {https://fastgpu.co/dataset}
}

Providers in this snapshot

AWS, Azure, Baseten, CloudRift, CoreWeave, Crusoe, Daytona, DeepInfra, fal.ai, GMI Cloud, Google Cloud, Hyperstack, Jarvis Labs, Lambda, Modal, Nebius, Oracle Cloud, Paperspace, QuantaCloud, Replicate, RunPod, TensorDock, TensorWave, Thunder Compute, Together AI, Vast.ai, Verda, Voltage Park, Vultr