When you use an AI workspace, you rarely think about the physical location of the servers processing your requests. However, for a global productivity platform like Notion, 'where' data lives is a complex engineering challenge. The company recently detailed its journey toward implementing multi-region data residency, a project designed to ensure that user data stays within its origin region—such as the EU—while still powering sophisticated AI features.

At the heart of this challenge is the need to maintain a seamless user experience. If you are a student in Europe using Notion’s AI features, the system must process your queries, index your pages, and generate answers without sending your private workspace content across the Atlantic. Notion achieved this by redesigning its infrastructure to be modular. Instead of one massive, centralized system, they transitioned to isolated private networks, using a workspace's unique ID to route and partition data strictly within specific regional boundaries.

Perhaps the most interesting aspect for those following AI development is how this applies to modern machine learning components. Notion’s AI features rely on vector databases, which store 'embeddings'—mathematical representations of text that allow the AI to understand the meaning behind your documents. To maintain privacy, Notion built regional-specific ingestion pipelines. When you update a document, these pipelines trigger a process that updates the embeddings within a local vector database rather than a centralized one.

This setup required a significant overhaul of how jobs are orchestrated. By using Apache Airflow as a 'control plane'—a central system that tells other systems what to do without actually processing the private data itself—Notion can schedule Spark jobs across different regions. It essentially acts like an air traffic controller, directing tasks to the appropriate regional airport while keeping the sensitive cargo safely on the ground where it belongs.

This approach demonstrates a growing trend in the industry: as AI adoption increases, so does the tension between the desire for powerful, centralized models and the regulatory necessity of data sovereignty. Notion's move proves that companies don't have to choose between advanced AI capabilities and strict user privacy. By architecting for 'region-aware' systems from the ground up, they have created a blueprint for future-proofing global platforms against evolving data protection laws.

When you use an AI workspace, you rarely think about the physical location of the servers processing your requests. However, for a global productivity platform like Notion, 'where' data lives is a complex engineering challenge. The company recently detailed its journey toward implementing multi-region data residency, a project designed to ensure that user data stays within its origin region—such as the EU—while still powering sophisticated AI features.

At the heart of this challenge is the need to maintain a seamless user experience. If you are a student in Europe using Notion’s AI features, the system must process your queries, index your pages, and generate answers without sending your private workspace content across the Atlantic. Notion achieved this by redesigning its infrastructure to be modular. Instead of one massive, centralized system, they transitioned to isolated private networks, using a workspace's unique ID to route and partition data strictly within specific regional boundaries.

Perhaps the most interesting aspect for those following AI development is how this applies to modern machine learning components. Notion’s AI features rely on vector databases, which store 'embeddings'—mathematical representations of text that allow the AI to understand the meaning behind your documents. To maintain privacy, Notion built regional-specific ingestion pipelines. When you update a document, these pipelines trigger a process that updates the embeddings within a local vector database rather than a centralized one.

This setup required a significant overhaul of how jobs are orchestrated. By using Apache Airflow as a 'control plane'—a central system that tells other systems what to do without actually processing the private data itself—Notion can schedule Spark jobs across different regions. It essentially acts like an air traffic controller, directing tasks to the appropriate regional airport while keeping the sensitive cargo safely on the ground where it belongs.

This approach demonstrates a growing trend in the industry: as AI adoption increases, so does the tension between the desire for powerful, centralized models and the regulatory necessity of data sovereignty. Notion's move proves that companies don't have to choose between advanced AI capabilities and strict user privacy. By architecting for 'region-aware' systems from the ground up, they have created a blueprint for future-proofing global platforms against evolving data protection laws.