afhubbard/gpu-prices

GPU Price Tracker

A continuously-updated dataset of cross-cloud GPU rental pricing covering 12+ public cloud providers (AWS, GCP, Azure, Lambda Labs, RunPod, Vast.ai, DataCrunch, Cudo Compute, TensorDock, Vultr, Oracle, Nebius, CloudRift). Snapshots are collected twice daily by scraping provider pricing surfaces via the gpuhunt library and published as Hive-partitioned Parquet files (prices/dt=YYYY-MM-DD/*.parquet).

The dataset is intended for:

• Researchers studying cloud-market microstructure, GPU price dynamics, and the spot–on-demand spread as a utilization proxy.
• Practitioners comparing GPU rental costs across providers for capacity planning, procurement, and ML-training cost estimation.

A full dashboard view is at the hosted Streamlit app (see the GitHub README for the deploy URL).

Quick start

from datasets import load_dataset

ds = load_dataset("afhubbard/gpu-prices", split="train")
print(ds[0])
# {'timestamp': '2026-05-07T09:17:00Z', 'provider': 'aws',
#  'instance_type': 'p4d.24xlarge', 'gpu_type': 'A100', 'gpu_count': 8,
#  'gpu_memory_gb': 40, 'vcpus': 96, 'ram_gb': 1152.0,
#  'region': 'us-east-1', 'price_per_hour': 32.7726, 'is_spot': False,
#  'available': True, 'availability_zone': None}

Or with DuckDB directly (no datasets install required):

import duckdb
con = duckdb.connect()
con.sql("INSTALL httpfs; LOAD httpfs;")
con.sql("""
SELECT gpu_type,
       AVG(price_per_hour / gpu_count) AS avg_price_per_gpu_hour,
       COUNT(*) AS listings
FROM read_parquet('hf://datasets/afhubbard/gpu-prices/prices/**/*.parquet',
                  hive_partitioning = true)
WHERE timestamp = (SELECT MAX(timestamp) FROM read_parquet(
                       'hf://datasets/afhubbard/gpu-prices/prices/**/*.parquet',
                       hive_partitioning = true))
  AND gpu_count > 0
GROUP BY gpu_type
ORDER BY avg_price_per_gpu_hour
LIMIT 10
""").show()

Schema

Column	Type	Description
timestamp	timestamp (UTC)	When the snapshot was taken
provider	string	Cloud provider id
instance_type	string	Provider SKU
gpu_type	string	Normalized accelerator family (H100, A100, …)
gpu_count	int32	GPUs per SKU
gpu_memory_gb	int32 (nullable)	VRAM per GPU
vcpus	int32	Host vCPUs
ram_gb	float32	Host RAM in GB
region	string	Provider's raw region (not canonicalized)
price_per_hour	float32	USD/hr for the full SKU
is_spot	bool	Spot/preemptible flag (semantics vary; see methodology)
available	bool (nullable)	Listed and offerable at scrape time
availability_zone	string (nullable)	Zone within the region, where applicable

Compute price_per_gpu_hour = price_per_hour / gpu_count for fair cross-SKU comparison.

Collection cadence

Twice daily (~09:00 and 21:00 UTC) via a GitHub Actions cron. Files are append-only — each run produces a new immutable Parquet file under prices/dt=<UTC date>/.

Limitations (read before modeling)

• Region strings are raw — not canonicalized across providers. Use a separate lookup if doing cross-cloud regional comparisons.
• Spot semantics differ by provider (AWS auction vs. Vast.ai P2P, etc.). See the methodology document.
• No customer telemetry — the data is supply/listing prices only.
• CPU/Unknown rows — a non-trivial fraction of upstream rows have gpu_count = 0 or gpu_type = 'Unknown'. Filter these out for most analyses.
• 12-hour cadence — too coarse for intraday auction analyses.

Full methodology, provider-by-provider notes, and a list of analytical questions the data does and does not support: methodology.md and MODELING_GPU_USAGE_TRENDS.md.

License

CC BY 4.0. Suggested citation:

@misc{hubbard2026gpuprices,
  author       = {Alex Hubbard},
  title        = {GPU Price Tracker},
  year         = {2026},
  howpublished = {\url{https://github.com/alex-hubbard/gpu_price_tracker}},
  note         = {Dataset and software, MIT (code) / CC BY 4.0 (data)}
}

Source code

Collection pipeline, dashboard, and migration scripts live at https://github.com/alex-hubbard/gpu_price_tracker.