Where to Find GPU, LLM, and MLOps Information on Deploybase

Deploybase organizes its AI compute market data into four main sections: GPUs, LLMs, MLOps, and Research. Use the GPUs section for GPU cloud instance pricing by model, configuration, and provider; the LLMs section for inference pricing and performance benchmarks; the MLOps section for tools covering training, inference, observability, and deployment; and the Research section for provider comparison articles. The site's data is dated as updated October 21, 2026, so check that date before relying on any figure.

GPUs: GPU Cloud Prices by Provider

The GPUs section is where you compare GPU instance pricing across providers. Its main view, "GPU cloud prices by provider," plots comparable provider prices in USD per GPU-hour by GPU model, with a price axis running from $0 to $12 in the displayed chart. The page also notes counts of providers, GPU listings, and LLM endpoints, and offers an "Explore" control for paging through results.

Use this section when you need to:

• Compare the same GPU model across multiple providers on a per-GPU-hour basis.
• Filter pricing by model and configuration rather than by provider marketing pages.
• Sanity-check a quoted rate against the market range shown in the chart.

The chart's $0–$12 axis reflects the plotted comparison view, not a cap on all market prices. Deploybase's own research articles cite figures well above that range, such as 8×B200 instances at $80–100/hour on AWS and $68.80/hour on CoreWeave. Treat the chart as a comparison tool and the articles as the source for specific configurations.

LLMs: Inference Pricing and Benchmarks

The LLMs section covers LLM inference pricing and benchmarks by provider. Its main view, "LLM performance vs cost," plots output price against throughput, with output price measured in USD per 1M output tokens on a logarithmic axis (roughly $0.10 to $100 in the displayed chart) and a "Fastest measured" marker for top-throughput models.

Use this section when you need to:

• See which models deliver the most output throughput per dollar of output tokens.
• Compare hosted LLM pricing across providers on a consistent unit.
• Identify the fastest measured models before shortlisting candidates.

The chart compares output price only. If your workload is input-heavy (long prompts, retrieval contexts), confirm input pricing separately before deciding.

MLOps: Tools for Training, Inference, Observability, and Deployment

The MLOps section catalogs tools across four categories: training, inference, observability, and deployment. It is a discovery index rather than a pricing table, so use it to build a shortlist of tools for each stage of the model lifecycle, then evaluate each tool on its own documentation.

Research: Provider Comparisons and Market Articles

The Research section publishes dated comparison articles. Examples listed on the site include:

These articles are useful for configuration-specific numbers that the comparison charts don't break out, such as multi-GPU node pricing and launch timelines.

A Practical Navigation Path

1. Start with GPUs if you're choosing infrastructure: compare per-GPU-hour prices for your target model across providers.
2. Move to LLMs if you're choosing a hosted API: compare output price against throughput and note the fastest measured models.
3. Check Research for the specific provider or GPU you're considering, since articles carry exact configurations and dates.
4. Use MLOps to fill gaps in training, serving, observability, or deployment tooling.
5. Confirm the update date (October 21, 2026) and each article's date before committing to a price.

One caveat: the site presents market intelligence and benchmarks, not quotes. Prices and availability change, and the B200 articles in particular describe launch timelines (Q2–Q3 2026 for AWS), so verify current terms with the provider before budgeting.