Unverified50% confidenceFactExact time
A typical approach to Dify private deployment is to use Docker Compose to orchestrate Dify service components, load open-source models locally on the model side through inference frameworks such as Ollama, vLLM and Xinference, and then connect them to Dify's model provider settings via OpenAI-compatible interfaces.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/18/2026
First Seen
Valid until: 1/16/2027
Sources
Related Entities
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UnverifiedDify构建了一个大语言模型应用平台，支持私有化部署或云端部署，并拥有开放的Marketplace供贡献者上架插件78% similarVerifiedDify supports integration with multiple LLM backends including OpenAI, Anthropic, and locally deployed open-source models.77% similarUnverifiedPrivate deployment technical paths fall into two categories: deploying open-source models (such as LLaMA, Qwen, and DeepSeek) on self-owned servers using inference frameworks such as vLLM, Ollama, and TGI, or purchasing privately licensed versions of commercial models.77% similarUnverifiedOpen-source models can be run locally or on private servers via inference frameworks such as Ollama, vLLM, or llama.cpp.73% similar
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