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compare · ai cloud

The CoreWeave alternative priced by the whole market

CoreWeave is an AI-native cloud running large NVIDIA fleets on a Kubernetes-based platform, focused on training-scale customers. 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

CoreWeave vs GPU.ai

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

CoreWeave

Strengths

  • +Very large modern GPU fleets, early access to new NVIDIA generations
  • +Purpose-built scheduling and networking for cluster-scale training
  • +Publicly listed, capitalized for massive build-outs

Trade-offs

  • Commercial focus has moved toward large reserved contracts
  • Kubernetes-native platform assumes real platform engineering
  • On-demand access for small teams is not the core product

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

What GPUs cost on GPU.ai right now

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

GPUVRAMGPU.ai / GPU / hrCoreWeave listStock
RTX A400016 GB$0.080/hrNot sold24 available
V10016 GB$0.090/hrNot sold2 available
RTX 308010 GB$0.100/hrNot sold14 available
RTX 309024 GB$0.120/hrNot sold36 available
RTX 508016 GB$0.159/hrNot sold20 available
RTX 408016 GB$0.180/hrNot sold5 available
RTX A500024 GB$0.232/hrNot sold7 available
RTX 2000 Ada16 GB$0.240/hrNot sold8 available
L424 GB$0.270/hrNot sold22 available
RTX 409024 GB$0.270/hrNot sold125 available
RTX 509032 GB$0.347/hrNot sold158 available
RTX A600048 GB$0.405/hrNot sold107 available
A4048 GB$0.490/hrNot sold8 available
A100 40GB40 GB$0.565/hrNot sold25 available
RTX 6000 Ada48 GB$0.650/hrNot sold167 available
RTX PRO 450032 GB$0.720/hrNot sold52 available
L4048 GB$0.780/hrNot sold116 available
L40S48 GB$0.790/hr$2.25/hr356 available
A100 80GB80 GB$0.880/hr$2.70/hr128 available
RTX PRO 600096 GB$1.69/hr$2.50/hr218 available
H100 PCIe80 GB$1.98/hrNot sold36 available
H100 SXM80 GB$2.27/hr$6.16/hr59 available
H100 NVL94 GB$2.37/hrNot sold10 available
H200 SXM141 GB$3.59/hr$6.30/hr87 available
H200 NVL141 GB$3.79/hrNot sold17 available
B200192 GB$5.32/hr$8.60/hr15 available
B300288 GB$7.89/hrNot sold12 available

market data via Compute Prices

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. CoreWeave 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 2026-09-07; this page refreshes hourly.

honest verdict

Which should you pick?

Stay with CoreWeave if…

  • You are reserving thousands of GPUs on multi-year terms
  • Your team already operates Kubernetes at scale

Switch to GPU.ai if…

  • You need a handful of GPUs by the hour, not a contract
  • You want SSH into a box in seconds, not a cluster onboarding

faq

Switching from CoreWeave

Is GPU.ai a good CoreWeave 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 CoreWeave's strengths (very large modern gpu fleets, early access to new nvidia generations) are not the thing you're paying for, an aggregated market usually wins on price and availability.

How does GPU.ai pricing compare to CoreWeave?

GPU.ai publishes its live per-GPU floor price for every model it carries, currently from $0.080/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 CoreWeave 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 CoreWeave?

Honestly: you are reserving thousands of gpus on multi-year terms, or if your team already operates kubernetes at scale. This page's job is a fair comparison, not a hard sell: the live price table above is the argument that matters.