AI infrastructure that accelerates innovation
Interested in developing and deploying AI applications? We’ve got you covered when it comes to scalable, secure, and customizable AI infrastructure.
Secure the AI infrastructure you need to build the future
Artificial intelligence adoption is increasing productivity, automating repetitive workflows, and providing more personalized customer services. However, AI deployment requires the right infrastructure to support and scale your application.
AI infrastructure is designed to support machine learning, deep learning, and extensive data processing. This includes high-speed processors like GPUs, storage and networking that can handle large amounts of data and quickly transfer it, as well as software platforms to develop, train, and manage AI models.
Main AI infrastructure use cases
AI model development
Training agentic AI actions, identifying model anomalies, predicting numerical values, performing data clustering tasks, and equipping models for data categorization. These tasks require GPUs, distributed clusters, dedicated storage and memory, along with version control and pipeline orchestration tools.
AI-as-a-Service platforms
Supporting low-code or no-code AI software, providing pretrained AI API access, hosting private AI instances, running MLOps platforms, and offering multi-modal AI services. This use case demands high-availability APIs, container orchestration software, and large amounts of secure, available storage.
AI edge computing deployments
Reducing overall bandwidth for AI data cloud uploads, enabling latency-critical AI tasks, running AI models without an internet connection, and securing sensitive data on local devices. Effective deployments need enough onboard processing power to compress and optimize AI model data on the device itself.
Inference
Offering image and text recognition, personalizing AI models, providing real-time recommendations, as well as giving connectivity to AI model frameworks such as TensorFlow, TorchServe, and Triton. These tasks need low-latency hardware, scalability, multi-model API support, and model quantization tools.
LLM workloads
Extracting knowledge from data and unstructured text, supporting chatbots and virtual assistants, generating code, and summarizing text. Large language model deployment and development need high-memory bandwidth, integrate caching and streaming functions, and preprocessing infrastructure for tokenization.
AI infrastructure hardware and framework integrations
AI infrastructure vs. IT infrastructure
Traditional IT infrastructure can run applications, store company data, and help keep your business operations online. However, current CPUs, SSDs, and low-bandwidth networks don’t offer the processing power required to run AI.
This is where AI-specific infrastructure comes in, with GPUs and model integrations, as well as enough storage and memory to properly support AI applications. DigitalOcean offers both NVIDIA and AMD-based GPU Droplets to handle the extensive data produced by AI applications.
NVIDIA’s CUDA ecosystem and GPU selection provide enough cores to run deep learning, model training, and AI workloads. DigitalOcean offers:
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NVIDIA H100
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NVIDIA RTX 400 Ada Generation
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NVIDIA RTX 600 Ada Generation
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NVIDIA L40s
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Coming soon: NVIDIA H200
AMD’s Instinct GPUs are optimized for general AI applications and high-performance computing. DigitalOcean offers:
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AMD Instinct™ MI300X
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AMD Instinct™ MI325X
Framework integrations for AI infrastructure
Aside from the significant processing power AI requires, you also need to integrate with AI frameworks. These act as the foundation for AI applications and allow you to start building out systems that can learn, adapt, and evolve. Popular frameworks include:
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TensorFlow: Large-scale machine learning creation and numerical computation
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PyTorch: Deep learning, natural language processing, and computer vision development
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OpenCV: Computer vision algorithm library
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Keras: API for deep learning model training and development
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Rasa: Conversational AI and chatbot development
These integrations, along with the right AI infrastructure, can help you create the features that you want most for your users and build out your AI applications.
DigitalOcean AI: simple, secure, and scalable
Ease of use
DigitalOcean offers multiple ways to deploy your AI models, such as Llama, Mistral, DeepSeek R1, and more, with open source technology. You can spin up deployments with a few clicks via GPU Droplets and the Gradient Platform or reserve space for a customized Bare Metal GPU deployment. You’re covered whether or not you want a fully managed service or decide to take the time to configure your own Bare Metal servers. In contrast, Google’s Vertex AI and Amazon Bedrock can require tool-specific knowledge, have a steeper learning curve, and offer less control over specific AI models.
Cost
With DigitalOcean, you won’t encounter hidden fees or surprise charges. Our GPU Droplets start at $0.76/GPU/hr, the GradientAI Platform starts at $0.198/million tokens, and we offer custom pricing for Bare Metal. These pricing plans come with our App Platform, Container Registry, and Functions at no additional cost.
Scalability
Our AI portfolio also supports different workflow types, including more predictable or elastic, so you can select the right offering for how often you need to scale.
Performance
Backed by our SLAs, extensive network of data centers, and GPU integrations, our AI infrastructure offerings provide consistent performance and smooth operation across your applications.
Learn more about DigitalOcean AI infrastructure
Find the right AI infrastructure to run your business and cutting-edge applications with any of our offerings:
Bare Metal
Get dedicated computing power that’s purposely built for AI and machine learning projects.
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Ideal for large-scale model training, complex orchestration, and real-time inference.
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Dedicated, single-tenant infrastructure.
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Available in New York, USA, and Amsterdam, Netherlands, data centers.
GPU Droplets
On-demand computing power for AI, including our GradientAI platform and DigitalOcean Kubernetes Service. Spin up your infrastructure in just two clicks.
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Useful for 3D modeling, training large language models, generative AI, and high-performance computing.
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Python and deep learning software packages come installed.
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Configurations available from single GPUs to 8-GPU setups.
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Available in New York, USA, Atlanta, USA, and Toronto, Canada, data centers.
GPUs for DOKS
This managed service lets you scale your Kubernetes deployment without manually configuring the underlying container infrastructure.
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Create worker nodes on dedicated GPU Droplets.
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Modify and create node pools at any time.
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Have DigitalOcean manage system updates, OS configuration, and installed packages.
Gradient Platform
Using DigitalOcean’s library of large language models to build custom AI agents in a matter of hours instead of weeks.
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Access to serverless models from Anthropic, DeepSeek, Meta, Mistral, and OpenAI.
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Direct integration with DigitalOcean Spaces for streamlined RAG creation.
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Create agents that can create content and complete tasks with Functions.
AI infrastructure FAQs
What is AI infrastructure?
AI infrastructure is hardware and software designed to support machine learning, deep learning, relevant data, and AI workloads. It requires more specialized hardware and software than traditional IT infrastructure, including GPUs and TPUs, high-capacity storage, and high-speed networking.
Why is AI infrastructure important for model training?
AI infrastructure is important for model training because it has the capacity to support the amount of data, scale, and software that AI models require. These models are complex and can grow very quickly over time, and need processing and storage infrastructure that can regularly accommodate large datasets and quickly complete model training workloads.
What components are included in an AI infrastructure stack?
AI infrastructure technology stacks require dedicated components for data storage and processing, networking, high-capacity computing resources, and machine learning frameworks. You can also include machine learning operations platforms to help with model training, storage, and implementation.
How does DigitalOcean's solution scale with enterprise AI workloads?
DigitalOcean’s solution scales to meet enterprise workload demands with a variety of offerings that can be customized for your organization’s requirements. Our AI infrastructure portfolio includes bare metal, GPU Droplets, and our GradientAI Platform, which can all be deployed in a matter of clicks to run AI workloads.
Does DigitalOcean’s infrastructure support open source AI tools and frameworks?
Yes, DigitalOcean’s infrastructure supports open source AI tools and frameworks. It relies on open source projects such as Linux and Kubernetes and maintains open source projects on the DigitalOcean Community GitHub.