AWS GPU alternative: dedicated GPUs at a flat monthly price

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.

FullGPU vs AWS at a glance

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 send a request and we confirm the configuration, price, location and delivery date in writing before you pay. If we miss that date, you can cancel for a full refund. 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 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 a FullGPU 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 shows where the break-even sits for your hours, and our monthly vs hourly comparison covers the trade-offs in depth.

What FullGPU 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 pays service credits if we fall short, and engineers answer 24/7. You pay by card or crypto at the same USD price.

We do not offer per-second billing, Spot capacity, serverless endpoints or managed ML services. Servers are ordered through a request handled by our team rather than launched on demand, and the minimum term is one month. Compare configurations on our pricing page.