Lambda alternative: monthly GPU servers, paid by card or crypto

Teams searching for a Lambda alternative usually want different terms rather than different GPUs: a server they keep for the month instead of an instance they launch and terminate, a way to pay other than a credit card, or a delivery date in writing when capacity is tight. Lambda offers self-serve instances billed by the minute, 1-Click Clusters reserved from two weeks to a year, and Superclusters arranged with its team. This page compares that model with dedicated monthly servers, as of October 2026.

Why teams look for a Lambda alternative

• Payment. Lambda's billing documentation says it only accepts major credit cards, and lists prepaid and debit cards among common reasons for declined payments. Teams that need to pay in crypto have to look elsewhere.
• Instance lifecycle. On-demand instances can be launched, restarted or terminated, but not stopped, so billing runs until you terminate. Persistent filesystems are billed for as long as they exist.
• Capacity. Self-serve access is first come, first served, not every instance type is available in every region, and new accounts have a limit on the number of instances they can launch. With GPU capacity tight across the market in 2026, a confirmed delivery date has real value.
• Cluster terms. Lambda's documentation notes that a 1-Click Cluster reservation cannot currently be extended or renewed, so a workload that outlives its term needs a new reservation.

FullGPU vs Lambda at a glance

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

Instances, clusters and Superclusters

Lambda's offer has three tiers. Instances are self-serve virtual machines with one to eight whole GPUs, billed by the minute while they exist. 1-Click Clusters group H100 or B200 nodes into a cluster reserved for a fixed period. Superclusters are large deployments of systems such as GB300 NVL72 and Vera Rubin NVL72, arranged with Lambda's team.

A FullGPU server sits between the first two. It is not launched by the minute, and it is not a reservation with a fixed end date: it is a dedicated machine, or a dedicated VM with a whole GPU, that renews every month until you cancel. That suits workloads with no natural end date, such as inference endpoints, continuous fine-tuning and internal model platforms. For multi-server setups, private networking between your servers and term pricing for several servers are available on request.

When Lambda is the better choice

• You want to launch and terminate GPU instances yourself, from the web or the Lambda Cloud API, and pay by the minute.
• You need a multi-node B200 or H100 cluster for a defined number of weeks.
• You are planning a long-term deployment of GB300 NVL72 or Vera Rubin NVL72 systems at large scale.
• You pay by credit card and prefer usage billing to a monthly term.

What FullGPU offers, and what it does not

We rent whole GPUs on monthly terms: single-GPU plans from RTX 5090 to B300, and 8-GPU servers with HGX H100, H200, B200 or B300, AMD Instinct MI355X or RTX PRO 6000. Prices are flat and published on our pricing page, billed monthly in advance with no setup fee, and you can cancel any month with no fee. You pay by card (Visa, Mastercard, American Express) or crypto (Bitcoin on-chain and Lightning, Ethereum, USDT, USDC, Litecoin) at the same USD price, with the exchange rate locked at checkout and the same invoice either way; see paying with crypto. Bandwidth is included with no egress fees up to 20 TB per month on single-GPU servers or 50 TB on 8-GPU servers, we commit to 99.9% monthly availability with service credits, and engineers answer 24/7.

We do not offer per-minute or per-second billing, serverless endpoints or spot capacity. Servers are ordered through a request handled by our team: we confirm the configuration, price, location and delivery date in writing, send a payment link, and email your credentials. If we miss the confirmed date, you can cancel for a full refund.

Moving a workload from Lambda

Moving is mostly a matter of rebuilding the environment. Our Ubuntu 22.04 and 24.04 images ship with NVIDIA drivers, CUDA, Docker and the NVIDIA Container Toolkit, so containerized jobs run as they are, and our PyTorch and vLLM guide covers framework setup. Lambda does not charge for egress and inbound traffic is not counted on our side, so moving models and datasets costs transfer time rather than fees. Check memory needs with the VRAM calculator, and compare billing models in our monthly vs hourly guide.