DigitalOcean

What's good about DigitalOcean

• Simple, transparent pricing with no surprises
• Wide GPU selection including H100, H200, L40s, and AMD MI300X
• No long-term commitments required
• Well-known developer platform with strong community
• Easy-to-use dashboard and API
• Multi-GPU configurations available (up to 8x)

DigitalOcean GPU prices

9 GPU models across 15 configurations. Rank compares DigitalOcean's cheapest on-demand listing for each GPU with every provider's cheapest.

What a run costs on DigitalOcean

24 hours at today's cheapest listing for each GPU, at the size it is listed.

• 1× H100 SXM for 24 hours

  At $4.41/GPU-hr

  $105.84

  $29.04 over the market median rate

• 1× H200 for 24 hours

  At $4.47/GPU-hr

  $107.28

  $1.68 under the market median rate

• 1× L40S for 24 hours

  At $1.57/GPU-hr

  $37.68

  $13.02 over the market median rate

The market median is every provider's cheapest on-demand price for that GPU.

DigitalOcean LLM API prices

Pay-per-token pricing, per 1M tokens.

Prices last updated October 28, 2026

Billing & terms

Regions

GPU Droplets are available in select US data centers with expansion planned.

Support

24/7 support via ticket system, extensive documentation, and active community forums.

Watch out for

• Newer to GPU cloud compared to specialized providers
• Some high-end SKUs (MI325X, B300) require sales contact
• Limited GPU data center regions

Features

GPU Droplets

On-demand NVIDIA and AMD GPU instances with per-hour billing and no long-term contracts.

One-Click ML Frameworks

Pre-configured images with PyTorch, TensorFlow, and CUDA for immediate productivity.

Integrated Platform

GPU Droplets work seamlessly with DigitalOcean's networking, storage, and managed databases.

Simple Pricing

Transparent per-hour pricing with no hidden fees or complex instance families.

Getting started

1. Step 1

   Create an Account

   Sign up at digitalocean.com and verify your account.

2. Step 2

   Create a GPU Droplet

   Select GPU Droplets from the Create menu, choose your GPU type and configuration.

3. Step 3

   Choose an Image

   Select a pre-configured AI/ML image or a base OS image.

4. Step 4

   Launch and Connect

   Launch your GPU Droplet and connect via SSH to start working.