DigitalOcean
DigitalOcean Gradient GPU Droplets provide on-demand GPU instances powered by NVIDIA and AMD GPUs for AI/ML training, inference, and fine-tuning. Instances come pre-configured with CUDA/ROCm drivers, PyTorch, TensorFlow, and Jupyter, deploying in under 60 seconds. Available in single-GPU and 8-GPU configurations with NVMe storage included.
GPU Hardware
| GPU Models | RTX 4000 Ada RTX 6000 Ada L40S MI300X H100 SXM H200 |
| Max VRAM | 192 GB |
| Max GPUs per Instance | 8 |
| Interconnect | NVLink |
| Multi-Node Training | Yes |
Pricing
| Starting Price | $0.76/hr |
| Billing Granularity | Per-second |
| Spot/Preemptible | No |
| Reserved Discounts | N/A |
| Free Credits | $200 free credit for 60 days |
| Egress Fees | None (included in plan) |
| Storage | 500-720 GiB NVMe boot (included), 5 TiB NVMe scratch on larger configs, Volumes at $0.10/GiB/mo |
On-Demand GPU Pricing
| GPU Model | VRAM | vCPUs | RAM | Price/GPU/hr |
|---|---|---|---|---|
| NVIDIA RTX 4000 Ada | 20 GB | 8 | 32 GiB | $0.76 |
| NVIDIA RTX 6000 Ada | 48 GB | 8 | 64 GiB | $1.57 |
| NVIDIA L40S | 48 GB | 8 | 64 GiB | $1.57 |
| AMD Instinct MI300X | 192 GB | 20 | 240 GiB | $1.99 |
| NVIDIA HGX H100 | 80 GB | 20 | 240 GiB | $3.39 |
| NVIDIA HGX H200 | 141 GB | 24 | 240 GiB | $3.44 |
12-Month Reserved (8-GPU Configurations)
| GPU Model | Price/GPU/hr |
|---|---|
| AMD MI300X x8 | $1.88 |
| AMD MI325X x8 | $2.10 |
| AMD MI350X x8 | $3.18 |
| NVIDIA HGX H100 x8 | $2.50 |
| NVIDIA HGX B300 x8 | $5.65 |
Billing is per-second with a 5-minute minimum. Charges apply even when Droplets are powered off. All GPU Droplets include NVMe boot disk (500-720 GiB) and data transfer (10-15 TB pooled). Larger configs include 5 TiB NVMe scratch disk.
Infrastructure
| Regions | New York (NYC2), Toronto (TOR1), Atlanta (ATL1), Richmond (RIC1), Amsterdam (AMS3) |
| Uptime SLA | 99% |
| Serverless / Autoscaling | No |
| Private Networking / VPC | Yes |
Developer Experience
| Pre-installed Frameworks | PyTorch TensorFlow Jupyter Miniconda CUDA ROCm Hugging Face |
| Docker Support | Yes |
| SSH Access | Yes |
| Jupyter Notebooks | Yes |
| API / CLI | Yes |
| Setup Time | Minutes |
| Kubernetes Support | Yes |
| Custom Images / Templates | Yes |
| Persistent Storage | Yes |
Business Terms
| Min Commitment | None |
| Compliance | SOC 2 Type II SOC 3 HIPAA (with BAA) CSA STAR Level 1 |
| Best For | AI training inference fine-tuning LLM deployment LLM serving computer vision startups generative AI research |
| Support Channels | Email Live Chat (24/7 on Standard+) Video Calls & Slack (Premium) |
| Payment Methods | Credit/Debit Cards PayPal Google Pay Apple Pay ACH Wire Transfer |
How does it compare?
Compare DigitalOcean against other cloud GPU providers.
Frequently Asked Questions
What is DigitalOcean best for?
DigitalOcean is best suited for: AI training, inference, fine-tuning, LLM deployment, LLM serving, computer vision, startups, generative AI, research
Provider type:
With GPU instances starting at $0.76/hr and a hardware lineup that includes RTX 4000 Ada, RTX 6000 Ada, L40S, MI300X, H100 SXM, H200, DigitalOcean is positioned to serve a range of AI/ML use cases from small-scale experimentation to production-grade deployments.
See if DigitalOcean matches your workload requirements at DigitalOcean official website.
What is the current Trustpilot rating and number of reviews for DigitalOcean?
The current Trustpilot rating for DigitalOcean is 4.6 out of 5.0, based on 2,427 total reviews as of June 30, 2026. DigitalOcean was founded in 2012.
You can read all user reviews directly on the Trustpilot page for DigitalOcean. Trustpilot ratings reflect real user experiences with GPU provisioning speed, pricing accuracy, support responsiveness, and overall platform reliability.
See how DigitalOcean compares to alternatives and explore their current offerings at DigitalOcean official website.
What machine learning frameworks does DigitalOcean support?
DigitalOcean provides the following pre-installed frameworks and tools:
PyTorch, TensorFlow, Jupyter, Miniconda, CUDA, ROCm, Hugging Face
Custom images: Yes
Jupyter notebooks: Yes
Persistent storage: Yes
Having popular frameworks pre-installed means you can start training or inference immediately without spending time on environment setup. If you need a specific CUDA version or custom dependencies, custom image support lets you bring your own Docker container.
For pre-built templates and framework compatibility details, see DigitalOcean official website.
How fast can I deploy a GPU instance on DigitalOcean?
Deployment and developer tools at DigitalOcean:
Setup time: Minutes
Docker support: Yes
SSH access: Yes
Jupyter notebooks: Yes
API / CLI: Yes
Custom images: Yes
A fast setup time combined with Docker and SSH support means you can go from sign-up to running your first training job in minutes. DigitalOcean provides the tools needed for both interactive development (via Jupyter) and automated pipelines (via API/CLI).
For step-by-step deployment tutorials and quickstart guides, visit DigitalOcean official website.
Does DigitalOcean offer serverless GPU inference?
Serverless GPU at DigitalOcean: No
Serverless GPU inference allows you to deploy models that automatically scale up when requests arrive and scale down to zero when idle, eliminating the cost of keeping GPUs running during quiet periods. This is particularly cost-effective for applications with variable or unpredictable traffic patterns.
DigitalOcean standard GPU pricing starts at $0.76/hr with Per-second billing.
For serverless GPU endpoint setup guides and pricing, see DigitalOcean official website.
Where are DigitalOcean data centers located?
DigitalOcean is headquartered in United States and operates GPU infrastructure in the following regions:
New York (NYC2), Toronto (TOR1), Atlanta (ATL1), Richmond (RIC1), Amsterdam (AMS3)
Uptime SLA: 99%
Private networking: Yes
Data center location matters for latency-sensitive inference workloads and for compliance with data residency requirements. Choosing a region close to your users or data sources can significantly reduce round-trip time for API-served models.
See all available data center locations and latency benchmarks at DigitalOcean official website.
Does DigitalOcean support multi-GPU instances with NVLink or InfiniBand?
DigitalOcean supports multi-GPU configurations with the following specifications:
Interconnect technology: NVLink
Maximum GPUs per instance: 8
Multi-node training: Yes
The choice of interconnect is critical for distributed training performance. NVLink provides up to 900 GB/s bidirectional bandwidth between GPUs, while InfiniBand enables high-speed communication across nodes. PCIe-only setups are suitable for inference but may bottleneck multi-GPU training.
Available GPU models: RTX 4000 Ada, RTX 6000 Ada, L40S, MI300X, H100 SXM, H200
For detailed interconnect specs and multi-GPU topology diagrams, see DigitalOcean official website.
Does DigitalOcean offer spot or preemptible GPU instances?
Spot/preemptible instances at DigitalOcean: No
Spot instances offer significantly reduced pricing (typically 50-90% cheaper) in exchange for the possibility that your instance may be interrupted when demand is high. This makes them ideal for fault-tolerant workloads like distributed training with checkpointing, batch inference, and hyperparameter sweeps.
DigitalOcean standard pricing starts at $0.76/hr with Per-second billing.
Check current spot instance availability and discount rates at DigitalOcean official website.
Does DigitalOcean charge egress or data transfer fees?
Egress fees at DigitalOcean: None (included in plan)
Egress fees are the charges applied when you transfer data out of the cloud provider (e.g., downloading trained model weights, serving inference results, or moving datasets to another provider). This is an important cost consideration for ML workflows that involve frequent model exports or large dataset movements.
Storage options: 500-720 GiB NVMe boot (included), 5 TiB NVMe scratch on larger configs, Volumes at $0.10/GiB/mo
For the complete data transfer fee schedule and free egress tiers, see DigitalOcean official website.
Does DigitalOcean offer free credits or a free trial?
DigitalOcean offers the following free credits or trial options for new users:
$200 free credit for 60 days
With GPU instances starting at $0.76/hr, even a modest free credit can provide meaningful hands-on time to evaluate the platform, test your workloads, and benchmark performance before committing to paid usage.
Check current free credit offers and sign-up bonuses at DigitalOcean official website.
What GPU models does DigitalOcean offer?
DigitalOcean offers a range of GPU models for AI, machine learning, and high-performance computing workloads. The full list of available GPUs includes:
RTX 4000 Ada, RTX 6000 Ada, L40S, MI300X, H100 SXM, H200
The maximum VRAM available on a single GPU at DigitalOcean is 192 GB, and instances can be configured with up to 8 GPUs. The interconnect technology used for multi-GPU setups is NVLink, which determines the bandwidth between GPUs during distributed training.
Browse the full catalog of available GPUs and their specifications at DigitalOcean official website.
What is DigitalOcean pricing and how does billing work?
DigitalOcean offers cloud GPU instances starting at $0.76/hr. Billing is calculated on a Per-second basis, which means you only pay for the exact compute time you use rather than rounding up to full hourly increments.
Spot/preemptible instances: No
Reserved instance discounts:
DigitalOcean supports the following payment methods: Credit/Debit Cards, PayPal, Google Pay, Apple Pay, ACH, Wire Transfer.
For live pricing across all GPU models and current availability, see DigitalOcean official website.
User Feedback
There are no public trader reviews for this firm yet. If you have traded with them, be the first to leave a short, honest review and help other traders.
Share Your Experience
Short, honest feedback helps other prop traders understand what it is really like to work with this firm.