README.md

---
license: cc-by-4.0
task_categories:
  - tabular-regression
language:
  - en
tags:
  - gpu
  - cloud-computing
  - pricing
  - market-microstructure
  - h100
  - a100
pretty_name: GPU Price Tracker
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: prices/**/*.parquet
---

# 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`](https://github.com/dstackai/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](https://github.com/alex-hubbard/gpu_price_tracker)
(see the GitHub README for the deploy URL).

## Quick start

```python
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):

```python
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](https://github.com/alex-hubbard/gpu_price_tracker/blob/main/methodology.md)
and
[MODELING_GPU_USAGE_TRENDS.md](https://github.com/alex-hubbard/gpu_price_tracker/blob/main/MODELING_GPU_USAGE_TRENDS.md).

## License

CC BY 4.0. Suggested citation:

```bibtex
@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>.