What Research and Provider Comparisons Does Deploybase Publish?

Deploybase publishes dated market-intelligence research on AI compute: side-by-side comparisons of GPU cloud pricing and LLM API cost/performance across providers, plus hardware-specific pricing and setup articles. It is a research and comparison resource, not a cloud provider itself — you use it to shortlist infrastructure, then verify current prices with the provider before committing, since the published figures carry specific dates.

What the site covers

Deploybase organizes its data around three areas, per its own navigation:

• GPUs — GPU instance pricing by model, configuration, and provider, presented as comparable per-GPU-hour prices across providers.
• LLMs — LLM inference pricing and benchmarks by provider, including a performance-vs-cost view plotting output price (USD per 1M output tokens) against throughput.
• MLOps — tools for training, inference, observability, and deployment.

The site describes itself as tracking "live GPU cloud and LLM API pricing, performance benchmarks, and market data across leading AI infrastructure providers," with a stated update date of October 21, 2026.

The kinds of comparisons it publishes

The research articles follow a consistent pattern: two or more named providers, a specific dimension (price, speed, latency, availability), and a dated snapshot. Examples from the published list:

Two things stand out about this coverage. First, it spans both cloud-platform comparisons (hyperscaler vs hyperscaler) and single-hardware deep dives (the same B200 GPU priced across AWS, CoreWeave, and Lambda). Second, the B200 articles show how much the same hardware can vary by provider and configuration — from roughly $6 per GPU-hour at Lambda to $8.60 per GPU-hour on CoreWeave's reserved clusters, with AWS's 8xB200 listing quoted at $80–100/hour for the whole instance. Those are the article's figures as published, not a live quote.

How to use it

1. Pick your dimension first. If cost is the constraint, start with the GPU price-by-provider view or the LLM output-price-vs-throughput chart. If latency matters, go to the LLM speed comparisons.
2. Read the dated article for your shortlist. Each one names the providers, the hardware or model, and the specific metrics compared.
3. Check the date before relying on a number. Every article carries a publication date (the examples above are March 2026; the site's overall data is stamped October 21, 2026). GPU and API pricing moves quickly, so treat any figure as a snapshot and confirm it on the provider's own pricing page before you budget or deploy.
4. Use the setup sections for planning, not as a runbook. The B200 articles include availability timelines and deployment notes, which help you judge readiness — but the actual provisioning steps belong to the provider's documentation.

Limits worth knowing

The published material is comparison and market data. It does not appear to offer its own compute, accounts, or billing — the site's footer lists only GPUs, LLMs, MLOps, Research, Privacy, and Terms, and the page promotes a subscription for updates. Pricing signals on the site reference "pricing" and "premium" without listing payment platforms, so whether any content sits behind a subscription is not something the available material confirms; check the site directly if access matters to you. And because every comparison is dated, the most reliable habit is to use Deploybase to narrow the field, then verify the final numbers at the source.