Gradient™ AI Agentic Cloud
Whether you're training large models, running inference, or creating AI-driven applications, DigitalOcean has the infrastructure and platform to enable you to build, deploy, and scale with ease.
Power your AI workloads from development to production
Run model training, fine-tuning, and high-performance compute workloads.
Your needs:
- Virtualized environment for scalability and cost-efficiency.
Start with: GPU Droplets - Containerized environment requiring flexibility and resilience of Kubernetes to run and scale.
Start with: GPUs for DOKS - Single tenant and direct hardware access with high-touch engineering support to manage complex multi-node deployments.
Start with: Bare Metal
DigitalOcean AI Ecosystem
Power Your AI Projects with Leading Technologies
Hear from AI native enterprises who use Gradient AI Agentic Cloud to power their tasks
Getting started with DigitalOcean's Gradient AI Platform was incredibly easy. We really appreciated the intuitive design... setting up and deploying our first agent took just like a few minutes. This allowed us to quickly implement AI capabilities without requiring extensive setup processes or even specialized expertise from our side. And that was truly a game changer for us.
Benedikt Klinglmayr
Full Stack Developer, Autonoma
Training our next-generation text-to-video model with millions of video inputs on NVIDIA H100 GPUS on DigitalOcean took us just 3 days, enabling us to get a newer version of our model much faster than before.
Naeem Ahmed
Founder, Moonvalley AI
AI Resources
Choosing the Right DigitalOcean Offering for Your AI/ML Workload
Choosing the Right GPU Droplet for Your AI/ML Workload
Run BAGEL VLM on a DigtialOcean GPU Droplet
How to run Deepseek R1 LLMs on GPU Droplets
Devstral: An Open-Source Agentic LLM for Software Engineering
uv: The Fastest Python Package Manager
See how easy it is to spin up a GPU Droplet
Scale with simplicity leveraging DigitalOcean’s Gradient AI Agentic Cloud tools
When building robust machine learning infrastructure for AI app development, choosing the right GPU solution is crucial. DigitalOcean's range of products, from GPU virtual machines (VMs) to bare metal servers to specialized generative AI platforms, each offering unique advantages, built with DigitalOcean's signature simplicity in mind. GPU Droplets provide flexibility and scalability, ideal for AI developers who need on-demand GPU compute, while Bare Metal cloud servers offer flexible configuration, making them a top choice for intensive workloads such as large-scale ML training.
Looking to get started with AI app development? DigitalOcean provides AI developers with a large library of tutorials on a range of topics—from articles on Jupyter Notebook setup, to getting started with Llama, and using the LLM CLI to deploy the GPT-4o model.
Rent GPU Droplets for LLMs starting at $0.76/GPU/hour
Experience affordable, high-performance AI infrastructure for companies of all sizes with DigitalOcean GPU Droplets.
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Frequently asked questions for Gradient AI
What is DigitalOcean Gradient AI Agentic Cloud?
DigitalOcean Gradient AI Agentic Cloud is a unified platform for building and scaling AI applications, providing both infrastructure and platform solutions. The infrastructure components, such as GPU Droplets and Bare Metal GPUs, offer the necessary computing power. On the platform side, it provides tools for building intelligent agents, including function calling, Retrieval Augmented Generation (RAG) with knowledge bases, and built-in evaluation tools, all designed to streamline the AI development lifecycle.
What are GPU Droplets and Bare Metal servers?
GPU Droplets are virtual machines that provide on-demand GPU compute for AI tasks. Bare Metal servers offer direct hardware access for more intensive, multi-node workloads, like large-scale model training.
Can I use pre-trained models on DigitalOcean?
Yes, the platform features one-click models, which allow you to get started with popular models quickly.
How does DigitalOcean make AI infrastructure affordable?
The platform is designed to be cost-effective, with transparent pricing. For example, GPU Droplets start at a low hourly rate for multi-month commitments, making AI development more accessible.
How can I store my large datasets for AI training?
How do I choose between a CPU and GPU for my AI workload?
Choosing between a CPU and a GPU depends on your specific workload. CPUs are excellent for tasks like data preprocessing, feature engineering, and inference for smaller models. GPUs are purpose-built for parallel processing, making them ideal for computationally intensive tasks like training deep learning models. Many AI Native Businesses use both, with GPUs for training and CPUs for serving predictions.