About ComputingPower.org
Last reviewed on 2026-04-30
What this site is: a public reference index for GPU and cloud compute pricing, paired with editorial guides that explain GPU hardware, cloud pricing models, and the cost trade-offs that come with running AI workloads.
Who the site serves
ComputingPower.org is built for engineers, researchers, and small teams who need to make a concrete decision: which GPU, which provider, which pricing model, and at what total cost. The audience ranges from a single graduate student fine-tuning a model on a rented A100 to a startup planning a multi-week training run on H100 clusters. The same questions come up regardless of scale — VRAM headroom, hourly rate versus total cost, availability, and whether spot capacity is worth the operational overhead.
What the site covers
- Pricing index. The homepage and the cheapest-A100 page list hourly rates by provider and GPU model.
- Hardware comparisons. Pages such as H100 vs A100 and TPU explained walk through architectural and cost differences.
- Provider guides. AWS vs Lambda Labs and the providers directory map the major options.
- Cost optimization. The spot-instance guide and the cost calculator help reduce the bill.
- Reference material. The glossary defines the terminology, and the carbon calculator estimates emissions.
Editorial approach
Hourly rates and availability are sourced from each provider's published pricing pages and product documentation. Where multiple SKUs exist for the same GPU (different VRAM, different region, different commitment level), the index uses the lowest publicly listed on-demand single-GPU rate unless a footnote says otherwise. Pricing changes constantly; readers should always confirm current rates with the provider before committing to a workload.
Editorial guides are written to answer one specific question per page. Comparison tables list the figures the decision actually depends on — VRAM, memory bandwidth, FP8/FP16 throughput, TDP, and cloud rate — rather than full spec sheets. Worked examples use round, illustrative numbers so the reader can re-run the math with their own inputs.
How content is produced
Pages are static HTML, written and reviewed manually before publication. There is no algorithmic feed and no auto-generated text on the editorial pages. The pricing dataset is a small JSON file that is updated when a provider publishes a meaningful change; pages that load it client-side will always show the most recent committed values.
The site has no contributors with undisclosed financial ties to the providers listed. There are no affiliate links in the editorial pages. If a provider link ever earns a referral fee in the future, that disclosure will be added directly to the relevant page.
Principles
Transparent sourcing. Every figure on the index either comes from a provider's own pricing page or from the dataset linked above. Where a number is illustrative rather than measured, the page says so.
Decision-first writing. Comparison pages are organized around the choice the reader is trying to make, not around exhaustive feature lists.
No paywalls on the index. Core pricing data and the calculator stay free to access.
Limitations
The pricing dataset is a snapshot, not a real-time feed. Providers change prices, retire SKUs, and shift availability without notice. Treat the figures here as a research starting point, then verify with the provider before committing money or time. The cost calculations in worked examples assume continuous utilization at the listed hourly rate — real workloads have idle time, restart overhead, and networking costs that are not modeled.
Contact
Pricing corrections, broken links, and editorial questions: see the contact page.