Data Sovereignty

Local deployment means the model runs on servers or personal computers controlled by the user, with data not passing through third-party servers.

Local deployment means model weights run on one's own hardware with data never leaving the local environment, which is critical for cybersecurity scenarios.

Dify's source code is fully open, supports private deployment, allowing enterprises to run the entire system on their own infrastructure with data never leaving the corporate intranet.

Self-hosted solutions can meet the needs of data sovereignty, compliance auditing, long-term cost control, and deep customization by keeping data within the enterprise's own infrastructure.

Open-source solutions allow enterprises to run all components in private environments, meeting compliance requirements such as SOC 2, GDPR, and HIPAA while achieving audit transparency

Data sovereignty refers to the principle that data is subject to the laws of the country or region in which it is generated.

The concept of data sovereignty is affirmed in regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act)

Privacy-first on-device processing architectures are shifting from a technical option to a core marketing differentiator for AI products, driven by growing user privacy awareness and tightening regulations such as GDPR.

Cross-organizational data sharing requires fine-grained permission control, audit trails, and compliance frameworks, with the sharing party retaining the right to withdraw and restrict use, which is consistent with the spirit of data protection regulations such as the EU's GDPR.

For industries sensitive to data flows, such as finance, healthcare and government, keeping data within the enterprise boundary is often more important than pure model capability.

The core trade-off of locally deploying a large model is: upfront investment in GPU hardware and operations costs, and model capability is usually slightly inferior to top cloud models, but in exchange you get data sovereignty, offline availability, and unlimited calls.

A multi-node cluster keeps model weights, inference logs and experimental data all local, making it friendlier for privacy-sensitive or highly compliance-regulated scenarios.

Binding business logic to a cloud vendor's proprietary API, storing user data on a platform that may change its terms at any time, and making the build process depend on a single service pose dependency risks structurally identical to those faced by content creators who rely on a single social platform.

The open models launched by NVIDIA are intended to satisfy enterprises' deployment preference for running models in their own data centers, achieving 'models that never leave home'.

Data sovereignty and platform openness are becoming increasingly important dimensions in the purchase of smart hardware.

Behind the transformation of agents from 'a service you call' into 'a process you own' is the superposition of trends such as the continuous improvement of on-device model capabilities, excess compute power in personal devices, and rising user sensitivity to data sovereignty.

The fundamental contradiction facing the direction of data sovereignty is that the core commercial value of platforms often comes from the aggregation and exclusive control of user data.

Data sovereignty in enterprise scenarios refers to an enterprise's complete control over the storage, processing, and flow of its own data.

本地化数据中心能帮助企业满足数据主权和数据存储、隐私保护相关法律规定

The core difference between self-hosted mode and SaaS subscriptions lies in data sovereignty — self-hosting returns data control to the user's own infrastructure.