# AWS GPU alternative: EC2 GPU instances vs monthly dedicated servers

> An AWS GPU alternative to EC2 P5 and P6: H100, H200 and B200 servers at a flat monthly price, 20–50 TB of transfer included. Quotas, Capacity Blocks, tenancy.

Source: https://overgpu.com/alternatives/aws  
Updated: 2026-10-23  
Publisher: OverGPU (https://overgpu.com/)

## AWS GPU alternatives at a glance (2026)

| Provider | Billing model | Hardware and tenancy | Crypto payment | Best fit |
| --- | --- | --- | --- | --- |
| [OverGPU (our service)](https://overgpu.com/pricing) | One flat monthly price per server; cancel before any renewal | Dedicated whole GPUs; 8-GPU servers are bare metal | Yes: BTC, ETH, XMR, USDT on TRON, SOL | Workloads that run most of the month |
| [Runpod](https://overgpu.com/alternatives/runpod) | On-demand Pods billed per second or hour; Serverless endpoints; credits | Secure Cloud and Community Cloud tiers; Instant Clusters | Through payment processors, per its docs | Bursty jobs and serverless inference |
| [Vast.ai](https://overgpu.com/alternatives/vast-ai) | Per-second billing: on-demand, interruptible or reserved for 1–6 months | Marketplace of independent hosts; reliability depends on the host | Through BitPay or Crypto.com, per its docs | Flexible jobs where host variance is acceptable |
| [Lambda (Lambda Labs)](https://overgpu.com/alternatives/lambda) | On-demand instances priced per GPU-hour; 1-Click Clusters for 2 weeks to 1 year | Cloud instances; reserved capacity through sales | Not offered, per its public pages | Self-serve instances and short cluster reservations |
| [CoreWeave](https://overgpu.com/alternatives/coreweave) | Public on-demand hourly pricing; large reserved contracts mostly through sales | Kubernetes-native enterprise AI cloud | — | Large reserved deployments run on Kubernetes |
| [Paperspace (DigitalOcean)](https://overgpu.com/alternatives/paperspace) | Per-second billing with a minimum; on-demand and reserved | DigitalOcean GPU Droplets | — | Teams already on DigitalOcean |

Facts from each provider's public pages, checked in October 2026; “—” means not covered. Prices change often, so check each provider's current pricing.

## Choose OverGPU if…

- Your GPUs run most of the month and you want one fixed price per server.
- You serve heavy traffic or move large datasets and want egress included.
- You want whole GPUs on hardware that no other customer shares.
- You want 24/7 engineer support included with every server.

## Choose AWS if…

- Your data, services and security controls already run on AWS.
- You need per-second billing, Spot capacity or a short Capacity Block for ML.
- You rely on managed services such as Amazon SageMaker AI around your GPUs.

## Overview

An AWS GPU alternative makes sense once your GPU usage has become steady. Amazon EC2 offers a broad range of GPU instances, from P5 with NVIDIA H100 to P6 with B200 and B300, billed by the second inside the AWS ecosystem. When those GPUs run most of the month, a dedicated server at a flat monthly price is often easier to plan and to budget. This page compares both options as of October 2026, including the cases where AWS remains the better fit.

## Why teams look for an AWS GPU alternative

The reasons are rarely about the GPUs themselves. They are about how the capacity is obtained and billed:

- **Quotas.** On-Demand GPU capacity is capped by account quotas per Region, measured in vCPUs (for example "Running On-Demand P instances"), and higher limits are requested through Service Quotas. AWS notes that new accounts may start with lower limits.
- **Commitments.** Savings Plans and Reserved Instances run on 1-year or 3-year terms. Capacity Blocks for ML reserve GPUs for a fixed window, are charged upfront and cannot be cancelled.
- **Data transfer.** Inbound data is free, but data transfer out to the internet is billed separately in tiers, which adds up for model APIs and dataset moves.
- **Stopped is not free.** EBS volumes and Elastic IP addresses are still charged while an instance is stopped.

## OverGPU vs AWS at a glance

| Criterion | OverGPU | AWS (Amazon EC2) |
| --- | --- | --- |
| Billing model | Flat monthly price per server, billed in advance; per-GPU-hour equivalent published | Per-second billing with a 60-second minimum across purchase options; Capacity Blocks charged upfront |
| Minimum commitment | One month; cancel any month with no fee | None for On-Demand; 1 or 3 years for Savings Plans and Reserved Instances; Capacity Blocks from 1 to 182 days |
| Hardware tenancy | Whole physical GPUs for one customer: bare-metal 8-GPU servers or a dedicated VM with the full GPU passed through; no vGPU, MIG or time-sharing | Virtual machines on the AWS Nitro System; shared tenancy by default, Dedicated Instances available; fractional L4 GPUs on G6f |
| GPU lineup focus | B300, B200, H200, H100, MI355X, RTX PRO 6000, L40S and RTX 5090; GB300 NVL72 on request | P5 (H100), P5e and P5en (H200), P6 (B200, B300), P6e UltraServers (GB200 and GB300 NVL72), G7e (RTX PRO 6000), G6e (L40S), G6 (L4) and older families |
| Data transfer and egress | 20 TB/month (single-GPU) or 50 TB/month (8-GPU) included, no egress fees, fair use; inbound not counted | Inbound free; data transfer out to the internet billed in tiers after a free monthly allowance |
| Payment methods | Crypto (BTC, ETH, XMR, USDT TRC-20, SOL) via a prepaid USD balance, with a top-up bonus of up to 50%; card payments coming soon | Cards, plus direct debit (ACH, SEPA or Bacs) for eligible accounts |
| Support | 24/7 engineers; 99.9% monthly availability SLA with service credits | Basic plan covers account, billing and quota requests; 24/7 technical support comes with paid plans such as Business Support+ |
| Best for | Steady workloads that run most of the month: inference, long training and fine-tuning runs | Teams built on AWS that need elastic, short-lived or very large GPU capacity next to AWS services |

AWS details come from its public website and documentation in October 2026 and can change, so check their current terms before you decide.

## Quotas, Capacity Blocks and egress in practice

On AWS, getting a GPU is a capacity question as much as a price question. On-Demand use is capped by quotas, and new instance types do not always start as self-serve On-Demand capacity: P6-B300 launched through Capacity Blocks for ML and Savings Plans, with On-Demand reservations handled by account managers. Capacity Blocks reserve instances for 1 to 14 days, or multiples of 7 days up to 182 days, starting up to eight weeks ahead; running instances begin terminating 30 minutes before the block ends. That suits a planned training run better than a service that must stay up.

With us, you choose a configuration and pay the first month from your balance, and the server is delivered in under 10 minutes after payment. There is no quota process, though verification may be requested for large orders or when fraud, sanctions or legal checks require it.

Egress is the other line that changes the math. A model API that streams responses, or a pipeline that ships checkpoints and datasets out, pays for what it sends to the internet. On our servers, bandwidth is included with no egress fees up to 20 TB per month on single-GPU servers and 50 TB on 8-GPU servers, under fair use; see [bandwidth and egress](https://overgpu.com/help/bandwidth-and-egress) for the details.

## When AWS is the better choice

Stay on AWS, or keep part of your GPU work there, when:

- your data, identity, networking and security controls already live in AWS, and moving them would cost more than it saves;
- you need GPUs for minutes, hours or a planned few days, where per-second billing or a Capacity Block fits;
- your jobs tolerate interruption and Spot Instances, which come with a two-minute interruption notice, can run them;
- you rely on managed services such as Amazon SageMaker AI, or on rack-scale UltraServers inside AWS.

## Running dedicated GPUs alongside AWS

A common pattern is to keep storage, databases and applications on AWS and move the always-on GPU work to dedicated servers. Data you pull into an OverGPU server is inbound traffic on our side and is not counted against your allowance, while AWS bills its data transfer out under its own pricing, so copy large datasets once and keep them on the server's local NVMe. Serve or fine-tune on a monthly server that stays busy, and keep bursty experiments on per-second instances. Our [GPU cost calculator](https://overgpu.com/tools/gpu-cost-calculator) shows where the break-even sits for your hours, and our [monthly vs hourly comparison](https://overgpu.com/compare/monthly-vs-hourly-gpu-pricing) covers the trade-offs in depth.

## What OverGPU offers, and what it does not

Every server comes with whole GPUs assigned to you alone: 8-GPU servers are bare-metal machines, and single-GPU plans are a dedicated VM with the full GPU passed through, with no vGPU, MIG or time-sharing. You get root access over SSH, Ubuntu 22.04 or 24.04 images with NVIDIA drivers, CUDA, Docker and the NVIDIA Container Toolkit preinstalled, local NVMe storage, a public IPv4 address with an IPv6 /64, and DDoS mitigation. The price is flat with no setup fee, our [99.9% SLA](https://overgpu.com/legal/sla) pays service credits if we fall short, and engineers answer 24/7. You pay from a prepaid balance topped up in crypto (BTC, ETH, XMR, USDT on TRON or SOL), with a top-up bonus of up to 50%; card payments are coming soon.

We do not offer per-second billing, Spot capacity, serverless endpoints or managed ML services. Servers are ordered on our [deployment page](https://overgpu.com/deploy), where you can also create a free account, and delivered in under 10 minutes after payment; the minimum term is one month. Compare configurations on our [pricing page](https://overgpu.com/pricing).

## Frequently asked questions

### Is OverGPU a good AWS GPU alternative for inference?

It fits inference servers that run most of the month: you pay one flat monthly price per server, with bandwidth included up to 20 TB (single-GPU) or 50 TB (8-GPU) and no egress fees. For short or spiky workloads, per-second instances may cost less.

### Do I need a quota increase to rent GPUs from OverGPU?

No. You choose a configuration on our deployment page and pay the first month from your balance, and the server is delivered in under 10 minutes after payment. Verification may be requested for large orders or when fraud, sanctions or legal checks require it.

### Does OverGPU charge for data transfer out?

Not within the included bandwidth: 20 TB per month on single-GPU servers and 50 TB on 8-GPU servers, under fair use. Inbound traffic is not counted.

### Can I keep my data on AWS and run GPU work on OverGPU?

Yes. Traffic into your OverGPU server is not counted against your allowance, but AWS bills its own data transfer out, so copy large datasets once and keep them on the server's local NVMe.

### Does OverGPU bill per second like Amazon EC2?

No. We bill a flat monthly price per server, in advance, with a one-month minimum and no cancellation fee. If you need GPUs for a few hours at a time, per-second billing will usually suit you better.

## Related pages

- [Monthly vs hourly GPU pricing: how to find your break-even](https://overgpu.com/compare/monthly-vs-hourly-gpu-pricing): The break-even formula, a utilization table with our prices, the extras hourly bills add and when hourly is the better deal.
- [GPU server bandwidth and egress fees](https://overgpu.com/help/bandwidth-and-egress): 20 TB or 50 TB of outbound traffic per month, inbound not counted, no egress fees. See what counts.
- [Rent NVIDIA H200 and HGX H200 servers from $3,090 per month](https://overgpu.com/gpus/nvidia-h200): Rent a dedicated NVIDIA H200 (141 GB) for $3,090/month, about $4.23 per GPU-hour at 24/7, or an 8-GPU HGX H200 for $23,900. Flat price, paid in crypto.
- [Rent NVIDIA B200 and HGX B200 servers from $4,590 per month](https://overgpu.com/gpus/nvidia-b200): $4,590/month for a dedicated 180 GB B200 (about $6.29 per GPU-hour at 24/7) or $34,900 for an HGX B200 server. Delivered in under 10 minutes.
- [CoreWeave alternatives in 2026: 6 GPU clouds compared](https://overgpu.com/alternatives/coreweave): Self-serve and monthly options vs a sales-led, Kubernetes-native AI cloud. Runpod, Lambda, Vast.ai, AWS and OverGPU.