What Is Deploybase and What Does It Offer?

Updated

Deploybase is a market intelligence site for AI compute. It aggregates live GPU cloud and LLM API pricing, performance benchmarks, and market data across leading AI infrastructure providers, and organizes that data into four areas: GPUs, LLMs, MLOps tools, and research articles. It is useful when you need to compare providers on price and speed, or track how the AI compute market is moving, before committing to a cloud or API vendor.

What Deploybase covers

The site's own description frames it as "Live GPU cloud and LLM API pricing, performance benchmarks, and market data across leading AI infrastructure providers." In practice, that breaks down into these sections:

Section What you can look at
GPUs GPU instance pricing by model, configuration, and provider; a "GPU cloud prices by provider" view charts comparable provider price in USD per GPU-hour
LLMs LLM inference pricing and benchmarks by provider; a "LLM performance vs cost" view plots output price (USD per 1M output tokens) against throughput, with the fastest measured models called out
MLOps Tools for training, inference, observability, and deployment
Research Articles comparing providers and pricing, such as Azure OpenAI vs Google Vertex, Azure vs AWS GPU cloud, Azure vs Google Cloud GPU pricing, and per-provider B200 pricing posts

The homepage also shows counts for providers, GPU listings, and LLM endpoints, and the data is dated ("Updated October 21, 2026" on the captured page).

How you would actually use it

The site is built around comparison views rather than a single vendor's catalog. A typical workflow:

  1. Pick your unit of comparison. For raw compute, compare USD per GPU-hour across providers for the same GPU model and configuration. For hosted inference, compare USD per 1M output tokens against measured throughput.
  2. Read the chart, then the article. The GPU and LLM views give you the market spread; the research articles add context such as availability timelines and platform integration.
  3. Check the date. Pricing and availability change quickly. The research pieces are dated (for example, March 2026 comparisons), and the B200 coverage notes availability windows like Q2–Q3 2026, so treat older figures as historical rather than current.

Expected result: a shortlist of providers whose price and performance fit your workload, backed by a stated date and source article you can revisit.

What the research articles add

The article library is where Deploybase goes beyond raw numbers. Examples from the page:

  • Azure OpenAI vs Google Vertex — API pricing, speed, and latency for hosted LLM services as of March 2026.
  • Azure vs AWS GPU Cloud — ND and P5 instances compared for LLM hosting, covering pricing, performance, and integration.
  • Azure vs Google Cloud — A100/H100 costs, Vertex AI vs Azure ML, TPU advantages, and reserved instance pricing.
  • B200 pricing across providers — AWS B200 at $80–100/hr for 8xB200 (launching Q2–Q3 2026), CoreWeave 8xB200 at $68.80/hour ($8.60 per GPU), and Lambda B200 SXM at $6.08/hour with 192GB HBM3e memory and managed support.

These are useful as worked examples of how the same hardware or API can land at very different price points depending on provider, commitment type (on-demand vs reserved), and included support.

Limits to keep in mind

  • It is an aggregator, not a provider. Deploybase does not sell compute; you still sign up and pay with the individual cloud or API vendor. The site's pricing signals mention "pricing" and "premium," but the captured page does not state subscription terms or whether any content sits behind a paywall — the page shows a "Subscribe" option, so check access terms directly if you plan to rely on it regularly.
  • Figures are point-in-time. Every comparison carries a date. Re-verify before budgeting, especially for GPUs with limited availability.
  • Coverage is a selection, not the whole market. The charts show a set of leading providers and models; niche or regional providers may not appear.

Who it is for

Deploybase fits teams and individuals doing infrastructure due diligence: choosing between GPU clouds for training or inference, picking an LLM API on cost-per-throughput, or tracking where B200-class capacity and pricing are heading. If you already know your vendor and just need to deploy, the provider's own docs will be more direct; Deploybase is most valuable at the comparison and planning stage.