# What are the best DigitalOcean GPU Droplet alternatives? > Nodus, RunPod and Lambda compared with DigitalOcean GPU Droplets: H100 rates, one 8-GPU job priced on each, and when DigitalOcean is the better pick. Source: https://www.nodus-compute.ai/blog/digitalocean-gpu-droplets-alternatives/ Build revision: dabea545e1965ff72b84a75483985dcd92947356 The best DigitalOcean GPU Droplet alternatives are Nodus for training and fine-tuning that must survive a lost machine, RunPod for cheap hourly pods, and Lambda for big NVIDIA machines and reserved clusters. Nodus lists the NVIDIA H100 at $2.60/hr against DigitalOcean’s $4.41 on-demand rate, restores your latest checkpoint on a replacement machine, and can run jobs in your own DigitalOcean account first (Beta). Stay on DigitalOcean if you want AMD Instinct GPUs, a 12-month reserved price, or a plain virtual machine next to the rest of your DigitalOcean setup. We build Nodus. Every DigitalOcean, RunPod and Lambda fact below links to that company’s own pricing page or docs, the arithmetic is shown once, and one section covers where DigitalOcean is the better choice. ## How do the DigitalOcean GPU Droplet alternatives compare? They sell two different things. DigitalOcean, RunPod and Lambda give you a GPU machine that you start, use and destroy. Nodus runs a command or training job to completion and handles placement, checkpoints and replacement machines for you. ||DigitalOcean GPU Droplets|Nodus|RunPod Pods|Lambda On-Demand| |-|-|-|-|-| |What you get|A virtual machine with 1 or 8 GPUs|A Job that runs to completion|A GPU pod|A virtual machine with GPUs| |H100 per GPU-hour (listed)|$4.41 on demand, $3.26 reserved 12 months|$2.60 (H100 SXM)|$2.69 Community, $3.99 Secure (H100 SXM)|$3.99 (8 x H100 SXM)| |How billing works|Per second, 5-minute minimum|Prepaid credits|Per second, prepaid balance|Per minute, weekly invoices| |When stopped|Billed until destroyed, even powered off|Job ends when your command exits|Volume disk billed at $0.20/GB/mo while idle|Billed until terminated| |When a machine is lost|Spot: at least 2 hours’ notice aimed for, not guaranteed|Urgent checkpoint, then a replacement machine restores it|Not stated on the pricing page|Not stated on the pricing page| |AMD GPUs|MI300X, MI325X, MI350X, MI355X|Not listed|Not listed|Not listed| Sources: [DigitalOcean GPU Droplet pricing](https://www.digitalocean.com/pricing/gpu-droplets), [DigitalOcean GPU Droplet docs](https://docs.digitalocean.com/products/droplets/how-to/gpu/), [RunPod pricing](https://www.runpod.io/pricing), [Lambda pricing](https://lambda.ai/pricing), [Lambda billing](https://docs.lambda.ai/public-cloud/billing/), the [Nodus pricing page](https://www.nodus-compute.ai/pricing/) and the [Nodus recovery docs](https://www.nodus-compute.ai/docs/concepts/attempts-and-recovery/). Prices checked October 30, 2026. ## Is there anything cheaper than DigitalOcean for GPUs? Yes, for most NVIDIA cards, on listed rates. DigitalOcean’s [GPU Droplet pricing page](https://www.digitalocean.com/pricing/gpu-droplets) lists the H100 at $4.41 and the H200 at $4.47 per GPU-hour on demand. The L40S and RTX 6000 Ada are both $1.57. The [Nodus pricing page](https://www.nodus-compute.ai/pricing/) lists the same four cards at $2.60, $3.593, $0.793 and $0.743. [RunPod’s Secure Cloud](https://www.runpod.io/pricing) lists them at $3.99, $5.29, $1.09 and $0.99. ![Listed per GPU-hour, DigitalOcean vs Nodus vs RunPod Secure: H100 $4.41, $2.60, $3.99; H200 $4.47, $3.59, $5.29; L40S $1.57, $0.79, $1.09; RTX 6000 Ada $1.57, $0.74, $0.99.](https://www.nodus-compute.ai/blog-charts/digitalocean-gpu-droplets-alternatives/gpu-rates.svg) *Nodus lists the lowest rate on all four cards; RunPod Secure is cheaper than DigitalOcean except on the H200.* DigitalOcean’s number is fairer than it looks. Its H100 plan lists 20 vCPUs, 240 GiB of memory, a 720 GiB boot disk, a 5 TiB NVMe scratch disk and 15,000 GiB of transfer. The Nodus pricing page lists GPU rates and CPU-plus-memory rates as separate lines, so a heavy data loader adds to the Nodus figure. Run `nodus run --dry-run` before a real job and compare that estimate, not a table. DigitalOcean is also not the most expensive option for every card. Its AMD Instinct MI300X costs $2.59 per GPU-hour on demand with 192 GB of GPU memory, and none of the other three list AMD hardware on the pages we checked. ## What does one 8 x H100 training run cost on each? We priced the same job everywhere: a fine-tune on one 8 x H100 machine for 10 hours, or 80 GPU-hours. Each rate comes from the company’s public pricing page on October 30, 2026. |Option|Rate used|8 x H100 for 10 hours| |-|-|-| |Nodus|$2.60/GPU-hr|$208.00| |RunPod, Community Cloud|$2.69/GPU-hr|$215.20| |DigitalOcean, 12-month reserved|$3.26/GPU-hr|$260.80| |RunPod, Secure Cloud|$3.99/GPU-hr|$319.20| |Lambda, 8 x H100 SXM|$3.99/GPU-hr|$319.20| |DigitalOcean, on demand|$4.41/GPU-hr|$352.80| The math is rate x 80. On listed rates, Nodus comes in $144.80 below DigitalOcean on demand, which is 41% less. DigitalOcean’s reserved price closes most of that gap, but it needs a 12-month contract through sales, so it only fits if you will keep the machines busy all year. ![8 x H100 for 10 hours: Nodus $208.00, RunPod Community $215.20, DigitalOcean reserved $260.80, RunPod Secure $319.20, Lambda $319.20, DigitalOcean on demand $352.80.](https://www.nodus-compute.ai/blog-charts/digitalocean-gpu-droplets-alternatives/job-cost.svg) *At list prices, the same 80 GPU-hours cost 41% less on Nodus than on an on-demand DigitalOcean Droplet.* The bill also depends on what happens after the job. DigitalOcean says a powered-off GPU Droplet is [still billed until you destroy it](https://www.digitalocean.com/pricing/gpu-droplets), because its disk, CPU, memory and IP stay reserved. Say your run finishes at 2 a.m. and nobody destroys the machine until 9 a.m. Those 7 extra hours of an 8 x H100 Droplet cost $246.96 at $4.41 per GPU-hour. A Nodus Job ends when your command exits, and `--max-cost` caps what a single run can spend. ## What happens when a GPU machine is lost mid-run? On DigitalOcean the recovery is yours to build. Spot GPU Droplets are interruptible, and DigitalOcean [aims to give at least 2 hours’ notice](https://www.digitalocean.com/pricing/gpu-droplets) but says it may reclaim with less or none in some cases. Two hours is a generous window, and a script that saves often will use it well. Restarting on a new machine, restoring files and relaunching is still your job. On Nodus that loop is built in. When a provider reclaims interruptible capacity, Nodus [asks your attempt for an urgent checkpoint](https://www.nodus-compute.ai/docs/concepts/attempts-and-recovery/) and prepares a replacement machine at the same time. The new attempt restores your latest committed checkpoint, so a checkpointed run loses at most the work since its last save. Your script writes model, optimizer and step files under `/nodus/state` and loads them on start, as the [checkpoints guide](https://www.nodus-compute.ai/docs/guides/checkpoints/) shows. Here is our decision rule. If losing the machine at hour 8 of a 10-hour run would cost you a night, pick a platform that restores checkpoints for you. If the job is short or you are watching it, any of these options works. ## Can you keep your DigitalOcean account and still use Nodus? Yes. DigitalOcean is one of the clouds listed on the Nodus [cloud accounts page](https://www.nodus-compute.ai/docs/guides/pools/cloud-accounts/). There are two levels. View existing resources is the default. You paste an API token and Nodus lists the CPU and GPU Droplets in the account, including powered-off ones, without creating, changing or deleting anything. Read-only observation is free. Run work in my account is a separate Beta upgrade that Nodus support turns on per organization. You give a token with Droplet read, create and delete scopes and set a credit limit. Nodus then places your Jobs, Workspaces and other single-node work in that account first, up to the limit, and uses Nodus capacity when the account is full or out of credit. Nodus creates, labels and deletes only its own machines there. The docs ask you to review the routing terms shown before you enable it. The CLI command from the docs reads the token from an environment variable, never from the command line: Terminal window ```sh nodus cloud connect digitalocean my-cloud --credit-limit 100 --key-env DIGITALOCEAN_TOKEN ``` This is the practical move if you hold DigitalOcean credits or an existing balance. You spend them first, and the work gets Nodus checkpoints and recovery either way. ## Where is DigitalOcean the better choice? DigitalOcean wins in several specific cases, and we would tell you to stay. * You want AMD GPUs. DigitalOcean lists the [MI300X, MI325X, MI350X and MI355X](https://www.digitalocean.com/pricing/gpu-droplets), and the MI300X at $2.59 per GPU-hour with 192 GB of memory is a lot of memory per dollar. Nodus does not list AMD cards. * You will run 8 x H100 all year. The $3.26 reserved H100 rate on a 12-month contract is a known, fixed price. * You need a big host around the GPU. The H100 plan’s 240 GiB of memory and 5 TiB scratch disk suit heavy preprocessing on the same machine. * You want spot B300s with a notice window. DigitalOcean lists a spot B300 at $6.17 per GPU-hour and aims for 2 hours’ notice before reclaiming. * You want a plain VM with SSH that you manage yourself, or ready model images. DigitalOcean documents [multi-node GPU setups](https://docs.digitalocean.com/products/droplets/how-to/gpu/configure-multi-node/) and [1-Click Models](https://docs.digitalocean.com/products/marketplace/1-click-models/). RunPod is the better pick for cheap consumer GPUs and interactive pods; our [RunPod alternatives guide](https://www.nodus-compute.ai/blog/runpod-alternatives-2026/) covers when. Lambda fits reserved clusters of 16 GPUs or more, listed from 2 weeks to 1 year on its [pricing page](https://lambda.ai/pricing). ## How do you move a DigitalOcean GPU job to Nodus? The smallest migration is one command from the directory that holds your training script. On a Droplet you would SSH in, copy code, run `torchrun` and destroy the machine afterwards. On Nodus you install the CLI, sign in and submit the same command: Terminal window ```sh pip install nodus-compute nodus login nodus run --gpu H100:8 --max-cost 250 -d -- torchrun train.py ``` The `--gpu H100:8` form, the `--max-cost` spend cap and `-d` to return once the Job exists all come from the [`nodus run` reference](https://www.nodus-compute.ai/docs/reference/cli/nodus_run/). Add `--dry-run` first to see the estimate without creating the Job. Then follow it with `nodus logs -f` and copy outputs back with `nodus cp`, as the [quickstart](https://www.nodus-compute.ai/docs/getting-started/quickstart/) shows. For the cheapest H100 and B200 hours in general, see [how to get the lowest-cost H100 and B200 hours](https://www.nodus-compute.ai/blog/lowest-cost-h100-and-b200-hours/). ## FAQ ### Is Nodus cheaper than DigitalOcean for an H100? On listed rates, yes. The Nodus pricing page lists the H100 SXM at $2.60 per GPU-hour, and DigitalOcean lists $4.41 on demand and $3.26 on a 12-month reservation. DigitalOcean’s plan bundles more CPU, memory and disk, so compare a dry-run estimate for your real job. ### Does DigitalOcean charge for a GPU Droplet that is turned off? Yes. DigitalOcean bills a powered-off GPU Droplet until you destroy it, because its resources stay reserved. Billing is per second with a 5-minute minimum. ### Can Nodus spend my DigitalOcean credits? Only with the Beta run-work option enabled for your organization. You connect a DigitalOcean token with Droplet create and delete scopes and set a credit limit, and Nodus places eligible single-node work there first. Sandboxes and multi-node runs use Nodus capacity. ### Does Nodus offer AMD Instinct GPUs like DigitalOcean? Not on the current pricing page, which lists NVIDIA cards from the RTX 3070 to the B300. If you need an MI300X or newer AMD card, DigitalOcean is the better place to run it.