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Nexpod vs Google Cloud for GPU work.
B200 · €5.00 /GPU·hrcapacity shown liveready in 47 s
Teams running GPU work on Nexpod
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Before you choose
Check the path to capacity first.
For GPU work on a general-purpose cloud, the hard part is often getting the right machines in the right place at the right time. Start your evaluation with this worksheet.
- 01GPU family listed for your zone
- 02Quota request sent and approved
- 03Services a job touches, listed
- 04Network and storage costs modelled
- 05Billing account and currency set
- 06A commitment length you can live with
Region and zone
Confirm which GPU families exist in the zones close to your data and your users.
Lead time
Ask how long a quota increase or a capacity request takes for your project.
Platform layers
List the services a job touches on its way to a GPU, and what each one adds to the bill.
Commitments
Read how discounts tie to term length, and what happens when your needs change.
Why Nexpod
One focus: GPU compute.
Capacity you can see first
EU-AMS-1, EU-FRA-1 and US-NYC-1, with every family's availability in the console before you deploy.
A whole server, by the hour
Eight GPUs on NVLink, the host CPUs, the RAM and the NVMe, reserved for you from boot until you stop it. Shown here: H100.
Endpoints that follow traffic
Workers start when requests queue and stop when the queue drains. Billed per second, down to zero.
Many nodes, one fabric
Join 8-GPU servers into one cluster and launch across all of them with one command. The rate per GPU does not change.
Pricing
One rate per GPU family.
Four families, one on-demand rate each, metered per second. Reserve for 12 months and the rate drops by a euro per GPU-hour, still invoiced in euros.
Rates differ by region, instance size and term, so there is no single number to put here.
→ check Google Cloud's own price list for your region
Placeholder quote. A customer who moved GPU work from Google Cloud describes, in two plain sentences, what changed on the invoice and in the day-to-day.
Comparison
Google Cloud and Nexpod, line by line.
Our answer on the left. On the right, what to look up for your Google Cloud project: machine types, zones and billing decide the real picture.
Workload fit
Match each workload to a cloud.
You do not have to move everything. Put the GPU-heavy jobs where they run best and leave the rest alone.
Nexpod fits when
- You want a dedicated node instead of a share of a host.
- Your jobs start and stop often, so per-second billing matters.
- Your finance team wants one invoice, in euros.
- You would rather skip platform setup and SSH straight in.
Google Cloud may fit better when
- Your pipelines sit next to data warehouses you already run on Google Cloud.
- Your team relies on managed data or ML services from the same provider.
- You hold committed spend there that you plan to use.
Talk to an engineer
Map your setup in 15 minutes.
Tell us which GPU machines and regions you use today. You leave the call with a matching list of Nexpod servers and a quote in euros.
Try it on one workload
Run one job on Nexpod. Then decide.
Rent a single 8-GPU server for a day, run the job you know best, and compare speed and cost on your own terms.
- B300· 288 GB HBM3e€6.00/GPU·hr
- B200· 180 GB HBM3e€5.00/GPU·hr
- H200· 141 GB HBM3e€4.00/GPU·hr
- H100· 80 GB HBM3€3.00/GPU·hr