Google’s NotebookLM has become the reference point for a category of AI tools that let people ask questions of their own documents rather than the open web. Upload a set of files and the assistant can summarize them, answer questions grounded in their content and even turn them into an audio discussion. For students, researchers and writers who already work inside Google’s ecosystem, it is hard to beat for convenience.
Some users, however, want the same idea with more control: over where their data lives, which AI model does the work and which other systems the tool can read. SurfSense, an open-source project, is one of the better-known attempts to meet that demand. It is not a drop-in copy of NotebookLM but a different approach to the same problem.
What NotebookLM does well
NotebookLM’s strength is simplicity. Users add documents, web pages, videos or notes and immediately get grounded answers, summaries and study aids with citations back to the source material. Everything runs on Google’s own Gemini models, so there is nothing to configure, and the experience is consistent across web and mobile.
The trade-off is that it is a closed system. Your material is processed on Google’s infrastructure, the model is whatever Google provides, and the tool mainly works with content you bring into it rather than with the systems where that content normally lives.
How SurfSense differs
SurfSense is an open-source research assistant that users can run on their own machine or server. According to its GitHub repository and website, it is designed to work air-gapped, meaning it can operate without sending documents to a third-party cloud service, and it does not require an account with the project.
Its main distinguishing feature is the range of connectors. Beyond uploaded files, SurfSense lists integrations with Slack, Notion, GitHub, Discord, Gmail, Google Drive, YouTube, Jira, ClickUp and Confluence, among others. In practice that turns it into a searchable knowledge hub across the places where a team’s information is scattered, rather than a notebook for a single set of documents.
SurfSense also lets users choose which AI models to use. It can connect to commercial APIs or to models running locally, so a technically inclined user can pick a different model for different tasks or keep everything offline. For retrieval it uses hybrid search, combining semantic matching with full-text search, and it shows the sources behind each answer.
Which one to choose
The two tools serve the same basic need with different priorities. NotebookLM suits people who value a polished, zero-setup experience and are comfortable keeping their material within Google’s services. SurfSense suits teams and individuals who want open-source flexibility, broader integrations and more direct control over their data, and who are willing to handle installation and maintenance themselves.
- Choose NotebookLM for ease of use, tight Google integration and features such as audio overviews.
- Choose SurfSense for self-hosting, connecting many work tools and selecting your own models.
As with most open-source alternatives, the decision comes down to whether convenience or control matters more in your working environment.
