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Compare · Hyperscaler

The AWS alternative priced by the whole market

Amazon Web Services rents GPUs through its P- and G-series EC2 instances, from single T4s to 8× B200-class training nodes. GPU.ai takes the other approach: one account that prices 12+ clouds on every launch and passes the raw rate through at zero markup.

At a glance

AWS vs GPU.ai

A fair read of both sides — their genuine strengths, the trade-offs, and what an aggregated market does differently.

AWS

Strengths

  • +Deepest ecosystem of managed services around the GPU (S3, SageMaker, EKS)
  • +Enterprise compliance, support plans, and private networking
  • +Reserved and spot options for committed or interruptible workloads

Trade-offs

  • On-demand list rates for high-end GPUs are among the highest in the market
  • New accounts face GPU quota requests that can take days to approve
  • Egress fees add a meaningful, hard-to-predict line item

GPU.ai

The aggregator model

  • Every launch is routed to the cheapest of 12+ clouds with live stock
  • Zero markup — the provider's raw rate is the price you pay
  • Per-second billing from SSH-ready to terminate
  • 30+ GPU models, from budget consumer cards to flagship training silicon
  • One account, one API, one CLI across the whole market
Live pricing

What GPUs cost on GPU.ai right now

Live per-GPU floor prices across our aggregated fleet, next to AWS's public on-demand list rates where they sell the same model.

GPUVRAMGPU.ai / GPU / hrAWS listStock
RTX A400016 GB$0.075Not sold45 available
RTX 508016 GB$0.083Not sold32 available
RTX 308010 GB$0.083Not sold19 available
V10016 GB$0.105Not sold7 available
RTX 309024 GB$0.120Not sold65 available
RTX 408016 GB$0.130Not sold8 available
RTX A450020 GB$0.190Not sold8 available
RTX 4000 Ada20 GB$0.200Not sold8 available
RTX A500024 GB$0.233Not sold20 available
RTX 2000 Ada16 GB$0.240Not sold8 available
RTX 409024 GB$0.270Not sold80 available
A3024 GB$0.290Not sold3 available
RTX 509032 GB$0.295Not sold88 available
L424 GB$0.310Not sold35 available
RTX PRO 450032 GB$0.340Not sold49 available
A4048 GB$0.440Not sold52 available
RTX A600048 GB$0.450Not sold34 available
L4048 GB$0.575Not sold132 available
A100 40GB40 GB$0.610Not sold26 available
RTX 6000 Ada48 GB$0.650Not sold61 available
L40S48 GB$0.740$1.52232 available
A100 80GB80 GB$0.880$5.1294 available
RTX PRO 600096 GB$1.69$4.14183 available
H100 SXM80 GB$1.74$3.9345 available
H100 PCIe80 GB$1.87Not sold8 available
H100 NVL94 GB$2.59Not sold26 available
H200 SXM141 GB$3.59$4.9741 available
H200 NVL141 GB$3.61Not sold10 available
B200192 GB$5.88Not sold12 available

How this comparison works

GPU.ai prices are live per-GPU floor rates from our aggregated fleet, normalized per GPU per hour, billed per second, with zero markup on the provider's raw rate. AWS figures are their public on-demand list rates for the closest equivalent SKU — never spot, reserved, or committed-use discounts. We only show models with launchable stock. Last updated 2026-08-17; this page refreshes hourly.

Honest verdict

Which should you pick?

Stay with AWS if…

  • Your stack is deeply built on AWS managed services
  • You need specific compliance regimes only a hyperscaler offers

Switch to GPU.ai if…

  • You are paying list rate for raw GPU hours a smaller cloud sells for a fraction of the price
  • You want capacity now, without a quota-increase ticket
FAQ

Switching from AWS

Is GPU.ai a good AWS alternative?

GPU.ai takes a different approach: instead of running one fleet, it prices the whole GPU market on every launch and routes you to the cheapest provider with live stock, at the provider's raw rate with zero markup. If AWS's strengths — deepest ecosystem of managed services around the gpu (s3, sagemaker, eks) — are not the thing you're paying for, an aggregated market usually wins on price and availability.

How does GPU.ai pricing compare to AWS?

GPU.ai publishes its live per-GPU floor price for every model it carries — currently from $0.075/GPU/hr at the low end. Prices are normalized per GPU per hour and billed per second, and the comparison table on this page is refreshed hourly from live fleet data.

How hard is it to switch from AWS to GPU.ai?

Launches are standard SSH instances with your keys, so most workflows move without changes: pick a GPU, launch, rsync your data, run. There are no commitments to unwind and no minimum spend — you can trial a single instance for a few cents before moving anything.

When should I stay with AWS?

Honestly: your stack is deeply built on aws managed services, or if you need specific compliance regimes only a hyperscaler offers. This page's job is a fair comparison, not a hard sell — the live price table above is the argument that matters.