Private Deployment

Private deployment keeps model weights and inference runtimes entirely within the enterprise's own infrastructure so that data never leaves the domain, whereas in the cloud API model, text data must be transmitted to third-party servers.

Private deployment refers to installing software in an enterprise's own servers or private cloud environment, with all data stored entirely on its own infrastructure and not shared with third parties, meeting data compliance requirements such as GDPR and Classified Protection 2.0; the drawback is that the servers and updates must be maintained by oneself, and the initial investment is relatively high.

The core value of private deployment lies in preventing data leakage, avoiding dependence on external services, flexibly controlling costs, and supporting deep customization.

Common industry solutions for addressing legal data security include private deployment, commitments not to use data for training, SOC 2 Type II security certification, and fine-grained data access audit logs.

For enterprises that deeply integrate AI into their core business, establishing internal model repositories and privately deploying critical components is shifting from an optional choice to a mandatory one.

Medium and large enterprises need to re-evaluate the architectural assumption of relying entirely on closed-source APIs, because the cost threshold for private deployment is falling rapidly.

Private deployment requires enterprises to bear the costs of compute procurement, model operations and tuning themselves, but it can avoid continuous pay-per-call spending and eliminate the risk of data outflow.

Common mitigation measures in enterprise settings include zero data retention agreements, private deployments, and outbound traffic auditing, but none of these can replace the fundamental principle of "do not enter secrets."

The full chain of Dify private deployment includes not only installing Dify itself but also privately deploying and managing language models, embedding models, vector models and multimodal models separately, and connecting them all to the Dify platform.

For small and medium-sized enterprises that need to handle sensitive data, private AI services are a necessity, avoiding data leakage, compliance issues, and per-token payment.

RAG, fine-tuning, and private deployment are often used in combination: RAG manages knowledge, fine-tuning manages capability, and private deployment manages security — this is the mainstream architecture for enterprise-grade AI implementation today.

Online APIs require no operations and maintenance and often have stronger model capabilities, but data must leave the enterprise boundary and billing is by token; private deployment keeps data within the intranet and can run offline, but requires your own GPUs, bears inference latency, and model capabilities are limited by the chosen open-source weights.

Evaluating private deployment capability requires confirming three things: whether the gateway can run fully offline, whether it can connect to enterprise self-hosted open-source models (such as Llama and Qwen deployed via vLLM and TGI), and whether logs and audit data are all retained locally.

Privatized and localized control often means higher operating costs and a more complex usage threshold

Private deployment can keep all conversations, knowledge bases, and model calls within the intranet, allowing access to local large models even without an external network connection.

The value of an open-source model as a foundation lies in its auditability, as regulatory authorities or enterprise security teams can inspect model weights, training data sources, and inference logic.

The selection decision rule is: choose Coze if you are building your own application and want public distribution; choose Dify if you need private deployment.

In high-frequency invocation scenarios, the cost of a hosted API billed per token may far exceed the cost of self-hosted computing power, especially when an enterprise already has GPU resources.

Private AI infrastructure typically refers to deploying all components—such as large model inference services, vector databases, and orchestration frameworks—entirely within an enterprise's own or private cloud environment, with data not passing through any third-party APIs.

Private deployment allows the entire system to be deployed to a local environment, with data 100% kept on-premises and not transmitted to the cloud.