DigitalOcean

Create cloud servers with ease, enjoy full control, automated backups, secure networking, and reliable support.

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About DigitalOcean

DigitalOcean is an award-winning cloud hosting provider that makes it easy to deploy, scale, and manage websites and applications, without the complexity and cost of traditional hosting solutions. With DigitalOcean, you can create a cloud server in minutes with just a few clicks, and you have full control over all aspects of your server, including root access, configuration, and scaling. DigitalOcean also provides a wide range of features and services, such as easy-to-use control panel, secure networking, automated backups, and more. Plus, DigitalOcean has a great support team on hand to assist you with any issues you might have. Whether you’re a developer looking for a powerful hosting solution, a startup wanting to quickly launch a website, or an enterprise looking for a reliable hosting partner, DigitalOcean has the solutions you need. With DigitalOcean, you can deploy your applications with confidence and get the performance and features you need to succeed.

DigitalOcean

New York City, United States · Founded 2011

Public

Founded: 2011
Headquarters: New York City, United States
Legal status: Public company

Key features

Create cloud servers in minutes
Full control over server configuration
Automated backups
Secure networking
Reliable support team
Easy-to-use control panel

Use cases

Deploying websites and applications for developers, startups, and enterprises
Scaling cloud servers to meet growing demands
Managing complex hosting solutions with ease

Pros

Integrated AI-native cloud platform spanning silicon to agent layers for end-to-end production workflows
Offers serverless inference, intelligent routing, and access to over 80 models with optimized performance
Provides managed agents, data & learning tools, and core cloud infrastructure with open integrations
Delivers consistent low latency and high throughput for AI workloads across multiple benchmarks
Owns infrastructure from GPUs to networking, reducing complexity and improving unit economics

Cons

Limited global data center presence compared to some larger cloud providers
May require familiarity with cloud infrastructure for advanced AI deployments
Certain AI features are in public preview, indicating potential instability or limited support

Frequently asked questions about DigitalOcean

What is DigitalOcean's AI-Native Cloud?

DigitalOcean's AI-Native Cloud is a single integrated platform designed for AI workloads, covering everything from silicon to agent runtimes. It provides end-to-end infrastructure, including compute, networking, storage, and managed services optimized for AI and machine learning applications.

Who should use DigitalOcean for AI workloads?

DigitalOcean is suitable for developers, startups, and enterprises building and scaling AI applications. It is particularly useful for teams needing cost-effective, high-performance infrastructure for inference, batch processing, and agent-based systems.

What AI models and tools does DigitalOcean support?

DigitalOcean supports over 70 open-weighted and frontier AI models, including DeepSeek, Llama, and Qwen, accessible via a single OpenAI-compatible endpoint. It also integrates with tools like OpenCode, LangGraph, and CrewAI for agent orchestration.

Does DigitalOcean offer serverless inference?

Yes, DigitalOcean provides serverless inference capabilities, allowing users to run AI models without managing underlying infrastructure. It also includes an Inference Router to optimize model selection and routing for each request.

What are the key performance benefits of DigitalOcean for AI?

DigitalOcean delivers sub-second time-to-first-token, consistent low latency, and high throughput for AI workloads. Benchmarks show up to 3.9× higher output speed compared to competitors like AWS Bedrock, with performance improvements scaling with usage.

How does DigitalOcean handle data locality and costs for AI workloads?

DigitalOcean emphasizes data locality to reduce costs associated with vector databases and retrieval-augmented generation (RAG) pipelines. Its end-to-end infrastructure minimizes egress fees and optimizes unit economics as workloads scale.