Compare
Nexpod vs Azure for GPU work.
H100 · €3.00 /GPU·hrscoped keys, € limitsprivate network per region
Teams running GPU work on Nexpod
Placeholder- Customer
- CUSTOMER
- customer
- Customer Co
- customer.ai
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Before you choose
Map the setup before the spend.
Moving a GPU workload is rarely only about the GPU. Work through this worksheet with your team before anyone signs.
- 01Identity and roles for GPU jobs scoped
- 02Virtual networks and endpoints planned
- 03GPU family offered in your region
- 04Quota or capacity confirmed in writing
- 05Storage, transfer and support priced
- 06Invoice currency and entity checked
Identity and access
Decide how jobs authenticate, and who may start, stop and pay for GPU servers.
Networking
Plan the virtual networks, private endpoints and storage a training job will need.
Capacity
Confirm the GPU family and the volume you need are offered where you deploy.
Total cost
Fold compute, storage, transfer and support into one number per month.
Why Nexpod
GPU servers, nothing in between.
Endpoints that follow traffic
Workers start when requests queue and stop when the queue drains. Billed per second, down to zero.
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: H200.
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
Euro rates, metered per second.
Each family has one on-demand rate per GPU-hour and a lower reserved rate. You see the price before you deploy, and the invoice matches it.
Rates differ by region, instance size and term, so there is no single number to put here.
→ check Azure's own price list for your region
Placeholder quote. A customer who moved GPU work from Azure describes, in two plain sentences, what changed on the invoice and in the day-to-day.
Comparison
Azure and Nexpod, compared plainly.
Our answer on the left. On the right, the question for your Azure subscription: VM size, region and agreement decide what you actually get.
Workload fit
Use each cloud for what it does best.
Keeping identity and apps on one cloud while GPU jobs run on another is a common, sensible split.
Nexpod fits when
- You need raw GPU time for training, inference or batch jobs.
- You want the same rate every hour of the day.
- You want invoices in euros from an EU-billed provider.
- You want root on the machine and nothing between you and the GPU.
Azure may fit better when
- Your identity, security and compliance tooling runs through Microsoft.
- Your apps already depend on other Azure services in the same region.
- An enterprise agreement already covers the spend.
Talk to an engineer
Talk it through in 15 minutes.
Share the GPU workloads you run on Azure today. A Nexpod engineer sizes them on our servers and sends a quote in euros within one working day.
Try it on one workload
Keep your stack. Move the GPU work.
Leave identity, apps and data where they are, and send the GPU jobs to Nexpod through the API.
- 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