DigitalOcean Model Library

Whether you're building agents with code or in the DigitalOcean console, pick the model that fits your workflow.

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Showing 1-15 models of 81 models available
Price low to high
Model NameTypeCapabilitiesPriceAvailability
Mistral 7B Instruct v0.3ChatN/ADedicatedGet Started
Ministral 3 8BChatN/ADedicatedGet Started
Qwen 2.5 14B InstructChatN/ADedicatedGet Started
Nemotron 3 Nano 30B A3BChatN/ADedicatedGet Started
DeepSeek V3ChatN/ADedicatedGet Started
Fal Fast SDXLImageAsync$0.00000111/secondServerlessGet Started
Fal Flux SchnellImageAsync$0.000003/megapixelServerlessGet Started
Fal Stable Audio 2.5AudioAsync$0.00000058/secondServerlessGet Started
Fal ElevenLabs TTS Multilingual v2AudioAsync$0.0001/1K charsServerlessGet Started
Qwen3 TTSAudioAudio, streaming, multilingual$0.00002/1K charsServerlessGet Started
Stable Diffusion 3.5 LargeImageImage generation$0.08/imageServerlessGet Started
Wan 2.2 T2VChat$0.60/videoServerlessGet Started
Ideogram 3.0 QualityImage$0.09/imageServerlessGet Started
Ideogram 3.0 DefaultImage$0.06/imageServerlessGet Started
Ideogram 3.0 FlashImage$0.03/imageServerlessGet Started

Frequently asked questions

What is a Model Library?

The DigitalOcean Model Library helps you discover, compare, and evaluate AI models from leading providers. Filter by capabilities, pricing, and deployment options, then test models in the Playground and deploy them with Serverless or Dedicated Inference.

What's the difference between foundation models and embedding models?
Foundation models generate text or perform tasks like summarization, Q&A, and chat. Embedding models transform text into vectors and are primarily used for search, recommendations, and Retrieval-Augmented Generation (RAG). You typically combine both when building knowledge-based agents.
Can I test and compare different models before choosing one?

Yes — the Model Playground allows you to run prompts against any available foundation model, compare outputs side-by-side, and fine-tune parameters. It's the fastest way to evaluate accuracy, speed, style, and cost.

How do I incorporate my own data with these models?

Use an embedding model to vectorize your documents and store them in a Knowledge Base. When attached to an agent, the platform will automatically retrieve relevant context and feed it into your foundation model during inference—enabling accurate, domain-specific RAG workflows.

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