afhubbard/gpu-prices

GPU Price Tracker

A continuously-updated dataset of cross-cloud GPU rental pricing covering 13 public cloud providers (AWS, GCP, Azure, Lambda Labs, RunPod, Vast.ai, DataCrunch, Cudo Compute, TensorDock, Vultr, Oracle, Nebius, CloudRift): 3M+ listing observations, 70+ GPU types, collected twice daily since January 2026 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.

Explore it interactively at the hosted dashboard: <https://gpu-price-trends.streamlit.app/>.

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, 'quality': 'ok',
#  'region_canonical': 'us-east-virginia', 'country': 'US',
#  'region_lat': 38.13, 'region_lon': -78.45,
#  'region_group': 'North America East'}

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 quality = 'ok'
GROUP BY gpu_type
ORDER BY avg_price_per_gpu_hour
LIMIT 10
""").show()

Schema

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

The quality and region columns were added in schema v1.1 (July 2026); all earlier snapshot files were upgraded in place, so every file in the tree has the same schema. Each Parquet file also embeds provenance metadata (schema_version, row_count, quality_summary, and backfilled = true on upgraded files).

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 canonicalization is best-effort — region_canonical and friends come from a hand-maintained lookup and are NULL for raw regions not yet mapped; fall back with COALESCE(region_canonical, region).
• —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.
• —Noisy rows are tagged, not dropped — historical snapshots contain cpu_only and unknown_gpu rows. Filter to quality = 'ok' 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/gpupricetracker>.