Turn Your Data Into Smarter Multimodal AI Systems with DigitalOcean.
Train and deploy multimodal AI workflows that process text, image, and audio data, securely hosted and easily scalable on DigitalOcean.
Build Multimodal AI Applications that See, Hear, and Understand
With DigitalOcean’s GradientAI Platform, you can build multimodal AI solutions that process images, audio, and text together, which helps you solve complex challenges like visual inspections, content moderation, or voice-driven interfaces. Use pre-trained models like OpenAI’s GPT-4o, Anthropic’s Claude 3.7 Sonnet, or Meta’s LLaMA 3.3 Instruct-70B or fine-tune your own for richer context and smarter results, all running on GPU-backed infrastructure that scales with your needs.
Bring Human-Like Understanding to Your AI Stack
Whether you're building content moderation tools, visual search engines, or voice assistants, you can integrate AI models that combine text, images, audio, video, and more.
Understand Multiple Inputs
Create AI agents that can process and interpret images, text, audio, or video for context-aware responses. This allows you to build use cases like visual product search, audio transcription, or field extraction from PDFs. Your agent becomes more versatile and closer to how humans perceive and understand information.
Combine Models with Modular Tools
Use pre-trained vision, speech, and language models or bring your own. Chain them together with functions, workflows, and knowledge bases specific to your use case. You can integrate tasks like image tagging, sentiment analysis, and document summarization in a single flow. Your agents can run via serverless inference or be hosted on DigitalOcean infrastructure using the API, depending on your architecture, with support for tools like function calling and RAG.
Scale with GPUs and Storage
Run high-performance multimodal workloads with GradientAI GPU Droplets and attach Spaces or Volumes for large media files. Easily scale up or down to meet your workload, whether you’re processing image batches, streaming audio, or analyzing documents, without performance bottlenecks with predictable pricing.
Keep Your Data Private
Deploy multimodal agents on secure, private infrastructure using DigitalOcean’s GradientAI Platform API so that sensitive files and inputs stay within your control. With full control over where and how your models are hosted, you can avoid vendor lock-in and third-party data exposure.
Deploy multimodal AI models with the DigitalOcean GradientAI platform
DigitalOcean’s GradientAI Platform offers everything you need to build, deploy, and scale AI agents that process text, images, and audio, perfect for multimodal AI applications. From serverless inference to multi-agent workflows, you can customize how your agents understand and act on real-world data with minimal setup and maximum flexibility.
Serverless inference with top multimodal models
Run multimodal AI models without managing infrastructure. With serverless inference, you can integrate image, audio, and text understanding into your application via simple API calls. Serverless Inference is well-suited for quick prototyping, low-latency endpoints, or adding multimodal AI to your app without infrastructure management. Gradient AI GPU Droplets are useful for training custom models, running fine-tuned versions, or scaling production workloads that need full control over compute and frameworks.
Access models from OpenAI and more
No provisioning required; just call the endpoint and go
Transparent token-based billing ensures you only pay for what you use.
Agents + RAG: context-rich multimodal responses
Bring your own data into the conversation with retrieval-augmented generation (RAG). Your agents can pull from internal knowledge bases, like product manuals, support docs, or image tags, while handling complex multimodal inputs.
Ingest data from PDFs- Spaces folders - URLs or local files Use performant embedding models to index your knowledge base
Only pay for indexing when your data changes
Agents + Functions: actionable multimodal agents
Make your multimodal agents do more than just respond; let them act. With DigitalOcean Functions, you can link your agents to external data sources and services.
Convert speech to text; then route results to APIs or databases
Add image analysis results to automated decision flows
Perform custom tasks like form submissions/alerts or workflow triggers
Multi-agent crews for specialized multimodal tasks
Design intelligent, multi-agent systems that work together. Your primary agent can handle user interaction while specialized agents handle different modalities, like one for vision, one for audio, and another for domain-specific tasks.
Route tasks across multiple agents with clear roles using orchestration frameworks such as LangChain / LlamaIndex deployed on DigitalOcean’s Gradient AI GPU Droplets or Kubernetes
For lightweight workflows or triggers you can also use DigitalOcean Functions to manage API calls between agents
Attach guardrails - knowledge bases and functions per agent
Use models like DeepSeek for task coordination and reasoning
Resources to help you build
Learn about SmolDocling, which makes fast, accurate document conversion possible with a lightweight multimodal AI model.
Discover how AI models like GPT-4, Gemini, and ImageBind are now combining text, images, and audio to understand and create more like humans.
Build a multimodal bot that understands text, voice, and images using Django, GPT-4, Whisper, and DALL·E.
Deploy a multimodal AI chatbot that sees, listens, and responds in real time using OpenAI, Deepgram, and LiveKit on GPU Droplets.
Explore Multimodal Large Diffusion Language Models (MMaDA), a diffusion-based model that blends text and image understanding, now runnable on DigitalOcean GPU Droplets for faster, cost-effective AI generation.
FAQs
What is multimodal AI?
Multimodal AI is an artificial intelligence system that processes and understands information from more than one type of input, such as text, images, audio, or video. Unlike unimodal AI, which focuses on a single data type, multimodal models can learn patterns by combining inputs. This allows them to make more context-aware decisions and provide richer outputs. In the context of document processing, it means understanding both visual structure and textual content.
How is multimodal AI different from single-modality AI?
Single-modality AI handles only one type of data, for example, a text-based chatbot or an image classifier. Multimodal AI combines multiple inputs to make more informed decisions. This leads to better performance on tasks that require cross-referencing data types, like identifying an object in an image based on a spoken description. The result is a more natural and human-like user experience.
What are some use cases for multimodal AI?
Multimodal AI is used in applications like content moderation, medical imaging analysis, and smart assistants. For example, a support bot might combine voice input with text documentation to provide relevant answers. In e-commerce, multimodal agents might help analyze product images and customer reviews to recommend items.
Does DigitalOcean support inference for multimodal models?
Yes. DigitalOcean supports multimodal inference in two ways, which gives you flexibility to choose between a managed or self-managed inference setup.
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GradientAI Platform for serverless, API-based inference using third-party models that can be part of a multimodal pipeline (e.g., combining image input with a text-based agent).
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GPU Droplets for full control and custom inference with open-source multimodal models like LLaVA, BLIP, or CLIP.
What kind of GPU clusters are available for multimodal workloads?
DigitalOcean offers GPU Droplets optimized for demanding AI workloads. These are ideal for training, fine-tuning, or deploying multimodal models at scale. You can choose from top-tier GPUs like NVIDIA H100 (80 GB, up to 8 GPUs per Droplet), AMD MI300X (192 GB per GPU, 1.5 TB in 8-GPU setups), or RTX 4000/6000 Ada GPUs for graphics and inference. These configurations are optimized for large language model training, vision, audio, and generative tasks. Storage is easily extendable with Volumes and Spaces for managing large datasets.
What’s the difference between multimodal AI and multi-agent systems?
Multimodal AI combines multiple data types (e.g., text, image, audio) into a single model or workflow to improve understanding. Multi-agent systems involve multiple agents, each focused on a different task, that work together to solve a problem. You can combine both approaches: for instance, one agent could process text, another could handle images, and a coordinator agent could manage the final response. The two concepts are complementary but solve different challenges.
Can I deploy models like GPT-4V or LLaVA?
Yes, you can deploy models like GPT-4V or LLaVA on GPU Droplets by installing the required frameworks and model weights. DigitalOcean provides full control over the GPU environment to run open-source or API-accessed multimodal models for tasks such as image captioning, VQA (Visual Question Answering), or text-image grounding. Note that GPT-4V (if accessed via OpenAI API) is better suited for integration with GradientAI Platform serverless endpoints, while LLaVA can be self-hosted on GPU Droplets.
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