How to Compare GPU Cloud Prices by Provider on Deploybase

Updated

Deploybase lets you compare GPU cloud prices by opening its GPUs section, where instance pricing is organized by GPU model, configuration, and provider, with comparable provider prices shown in USD per GPU-hour. Use it when you want a side-by-side cost view before choosing a cloud; the site presents market data and charts rather than a single quote for your exact workload.

What the GPU section shows

The GPU page is built around one core comparison: GPU cloud prices by provider. For each GPU model, you can see listings from multiple providers and a price expressed in USD per GPU-hour. The page also includes a chart with a price axis (roughly $0 to $12 per GPU-hour in the displayed scale) and an "Explore" control for paging through additional listings.

This structure means you compare like with like: the same GPU model across providers, rather than mixing different hardware into one number.

Step-by-step comparison

  1. Open the GPUs section. From the Deploybase homepage, select GPUs ("View GPU instance pricing by model, configuration, and provider").
  2. Pick a GPU model. Choose the model you're evaluating so every price you see refers to the same hardware.
  3. Filter by configuration. Narrow listings by configuration so you're comparing equivalent setups, not a single card against an eight-GPU node.
  4. Read the per-GPU-hour price. Compare the USD per GPU-hour figures across providers in the listing and the chart.
  5. Page through results. Use Explore to move beyond the first set of listings and see more providers.
  6. Record your shortlist. Note provider, model, configuration, and price per GPU-hour for each candidate before moving to other checks.

Expected result: a shortlist of providers offering the same GPU model at different per-GPU-hour prices, which you can convert into an estimated cost for your planned usage.

Turning per-hour prices into a budget estimate

The listed unit is USD per GPU-hour, so estimate cost as:

Estimated cost = price per GPU-hour × number of GPUs × hours of use

For example, if you need four GPUs of one model for 100 hours, multiply the per-GPU-hour price by 4 and then by 100. Doing this for each shortlisted provider gives a comparable total rather than a per-hour figure that's hard to judge.

Reading the chart and the listings together

The chart plots provider prices against the per-GPU-hour axis, which makes outliers easy to spot: a provider sitting well above the cluster of comparable offers is visibly more expensive for the same model. The listings underneath give the specific provider and configuration behind each point. Use the chart to spot the range and the listings to confirm exactly what each price covers.

What Deploybase does not settle for you

Deploybase is described as market intelligence for AI compute — live pricing, benchmarks, and market data across providers. It is a comparison and research resource, not a quoting or purchasing tool. The page evidence does not state that prices are guaranteed, that listings are exhaustive, or that any provider is recommended. Treat the figures as a starting point and confirm current pricing, availability, and terms with the provider before committing.

Related comparisons on the site

The research section extends the same per-hour logic to specific providers and hardware, including:

  • Azure OpenAI vs Google Vertex — API pricing, speed, and latency (as of March 2026)
  • Azure vs AWS GPU cloud — ND vs P5 instances for LLM hosting
  • Azure vs Google Cloud — A100/H100 costs and reserved instance pricing
  • B200 pricing — AWS 8xB200 at $80–100/hr, CoreWeave 8xB200 at $68.80/hr ($8.60 per GPU), and Lambda B200 SXM at $6.08/hr

These articles are useful once you've narrowed the field on the GPU page and want provider-specific detail.

Common sticking points

  • Comparing different configurations. A per-GPU-hour price for one card isn't comparable to a multi-GPU node price unless you normalize to the same unit.
  • Ignoring the model. Prices only compare meaningfully within the same GPU model.
  • Treating the chart as a quote. The displayed axis and points are market data; your actual cost depends on configuration, duration, and provider terms.
  • Skipping the research articles. They add context — such as availability timelines and reserved-capacity pricing — that a single price point doesn't show.