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Data & Citation

The packaged Scrutica datasets and how to cite them. Each bundle ships as a CSV with a paired JSON Schema, methodology Markdown, SHA-256 checksum, and Croissant JSON-LD descriptor; the Citation view carries the reuse forms for the datasets and for any number rendered on the site.

Compute Cost Index

Cloud GPU pricing normalized to $/petaFLOP-day across on-demand, spot, and reserved tiers, using dense BF16 throughput from Epoch AI ML Hardware (no 2:4 structured sparsity). Coverage: AWS, Azure, GCP, Oracle OCI, CoreWeave, Lambda.
Version 2026.3508 rowsReleased 2026-07-17License CC-BY-4.0SHA-256 9d4ad1306206
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ZenodoDeposition pending — a versioned Zenodo record with a resolvable DOI is in preparation. Until it lands, cite by version, URL, SHA-256, and retrieval date.
Pending
Hugging FaceMirror pending — a Hugging Face dataset mirror is in preparation. The canonical download above is complete and hash-verifiable in the meantime.
Pending
Citation

Gringras, D., & Scrutica. (2026). Scrutica Compute Cost Index (Version 2026.3) [Data set]. Zenodo. (DOI pending deposition)

BibTeX
@dataset{scrutica_compute_cost_index_2026_3,
  author    = {Gringras, David and {Scrutica}},
  title     = {Scrutica Compute Cost Index},
  year      = {2026},
  version   = {2026.3},
  publisher = {Zenodo},
  note      = {DOI pending Zenodo deposition; cite by version + SHA-256 hash from /datasets/compute-cost-index until assigned},
  url       = {https://scrutica.com/datasets/compute-cost-index}
}
Methodology
# Scrutica Compute Cost Index — v2026.3

**Release date:** 2026-07-17
**License:** CC-BY-4.0
**Suggested citation:** see `CITATION` block at the bottom.

## Coverage

508 (instance × pricing-tier) rows across 6 providers and 9 accelerator types. Pricing tiers covered: on-demand, spot, reserved 1-year, reserved 3-year.

## Normalisation

Each row's hourly instance price is divided by the instance's dense BF16 throughput to give a $/petaFLOP-hour figure, multiplied by 24 for the day figure. Throughput uses Epoch AI's ML Hardware corpus (e.g., 989.5 dense BF16 TFLOP/s on H100 SXM); **no 2:4 structured sparsity** factor is applied — the index is intended to be comparable with dense training cost. Reserved-tier rows are amortised at the provider's published commitment-term pricing; we do not impute hidden discount tiers.

## Sources and authority tiers

| Source | Authority tier | What it contributes |
| --- | --- | --- |
| AWS pricing page | 1 | On-demand and reserved hourly rates |
| Azure pricing page | 1 | On-demand and reserved hourly rates |
| GCP pricing page | 1 | On-demand and reserved hourly rates |
| Oracle OCI pricing page | 1 | On-demand hourly rates |
| Lambda pricing page | 2 | On-demand hourly rates |
| CoreWeave pricing page | 2 | On-demand hourly rates |
| ec2.shop JSON API | 2 | Cross-verification of AWS rates and spot pricing |
| Epoch AI ML Hardware | 1 | Per-accelerator dense BF16 throughput denominator |

## Refresh path

Pricing is refreshed via the scripts that produce `data/processed/cloud_pricing.json`; this dataset is regenerated by `scripts/package-datasets.ts`.

## Known limitations

- Spot pricing is a snapshot; real-world spot prices fluctuate intraday.
- On-prem TCO (build-vs-buy) is rendered alongside on `https://scrutica.com/cost-index` but is *not* in this CSV — it is a derived view, not a primary measurement.
- Commitments that require enterprise procurement (private negotiated rates, GPU reservation deals) are out of scope; this index is the public-rack-rate floor.

## Citation

```bibtex
@dataset{scrutica_compute_cost_index_2026_3,
  author       = {Gringras, David and {Scrutica}},
  title        = {Scrutica Compute Cost Index},
  year         = {2026},
  version      = {2026.3},
  publisher    = {Zenodo},
  note         = {DOI pending Zenodo deposition; cite by version + SHA-256 hash from /datasets/compute-cost-index until assigned},
  url          = {https://scrutica.com/datasets/compute-cost-index}
}
```
See also

The interactive surface that consumes this dataset lives at /economics/cost-index; for the platform-wide data-provenance overview, see /methodology.