As someone who primarily self-hosts all essential tools, I was never interested in LLM-powered platforms designed to erode creativity and replace human ingenuity with AI-created slop. But even I must admit that artificial intelligence has its fair share of uses. Take Google’s all-powerful NotebookLM, for example. Not only is it useful for aggregating data from different sources, but it can also answer your queries without sprouting incoherent information like a madman.

But if you’re anything like me, you might not be too keen on storing data on a company’s external servers, especially if it’s research material for work or academic life. Despite a couple of issues, Open Notebook is a fantastic self-hosted alternative to NotebookLM, and here’s a byte-sized piece on why you should check it out if you’re even remotely into running local services on your own hardware.

Supports multiple models

Not just Google’s Gemini

On its own, Google’s Gemini is fairly reliable, but the real game-changing aspect of NotebookLM is the way it works in tandem with the company’s LLM. Rather than using random sources online and displaying information after skimming through them like a typical LLM/chatbot, NotebookLM analyzes sources that you’ve explicitly linked, thereby reducing false inputs and hallucinated content to a considerable extent. That said, Google’s handy assistant can still encounter trouble in certain topics, be it an in-depth analysis of obscure mathematical formulas or sensitive content that might get flagged as inappropriate.

That’s where Open Notebook’s customizable nature comes in handy. Rather than being restricted to Gemini, Open Notebook can be paired with most of the popular AI platforms – be it OpenAI, OpenRouter, Groq, or Anthropic. Each platform has its own collection of models you can choose from, so you can fine-tune your research companion’s reasoning skills depending on your needs. And that’s before you include the sheer number of local LLMs you can add to Open Notebook using Ollama’s API…

Better privacy

Especially with self-hosted AIs

Although Google claims that NotebookLM doesn’t rely on user data to train its AI functionality, your research material and notes still remain on the company’s servers. And if there’s something I’ve realized after several years of using cloud platforms, it’s that anything stored on online servers is no longer private – especially when said servers fall under a tech giant like Google.

Now, I’d say the same thing about OpenRouter LLMs or OpenAI’s models, as any data uploaded to these platforms is no longer private. But aside from cloud AI providers, Open Notebook also works with models loaded via Ollama. Since everything is stored, processed, and displayed on your own hardware, you don’t have to worry about privacy issues when running an Ollama-powered Open Notebook instance.

No daily limits

Perfect for hardcore users with local Ollama models

If you’re a hardcore NotebookLM user who relies on the platform for most of your research tasks, you may have already hit certain limits imposed by Google. For instance, you can’t add over 100 notebooks on the free version of NotebookLM, and each notebook can’t have more than 50 sources. The same holds for your chat queries and audio overviews. Although the average user may not be able to hit the max cap, you’re bound to run out of notebooks or end up requiring more audio summaries when you’re using NotebookLM extensively.

For folks who rely on Open Notebook’s cloud models, you’re bound to encounter token limits instead of running out of notebooks. However, if you’ve deployed LLMs on local hardware, you don’t have to worry about any rate limits whatsoever.

Ships with most NotebookLLM features

You can even generate podcasts!

NotebookLLM’s killer accuracy and custom source facility were already its main draw, and Google has continued to integrate more tools into the platform. Between audio overviews and reports, you can get solid summaries of long and complex documentation. Likewise, you can save specific chat snippets as notes – not only for your perusal, but also to help the AI filter out the unnecessary information and retain the essential aspects of the "conversation.”

Fortunately, Open Notebook borrows many of the key features of NotebookLM. The Podcasts section can turn your research material into audio summaries, and you’re free to customize the speaker profiles and episode format. You can also create custom notes and create a variety of reports with your sources, ranging from simple table of contents to deep summaries, technical analysis, and actionable insights. Although Open Notebook doesn’t support quizzes and flash cards yet, it can turn your sources into a knowledge base, allowing you to build an AI-powered second brain – one that runs on your own hardware.

Still, Open Notebook has some major drawbacks

As much as I want to see Open Notebook succeed, I’d be lying if I said it can replace NotebookLM. Even with a cloud-based AI platform, deploying an instance of Open Notebook can be a pain, as you’ll have to jump through certain API hoops. Should you go for the high-parameter LLMs, you might end up paying more for Open Notebook – and the initial costs only go up once you get into hosting your own language models. Depending on your Ollama rig’s specifications, Open Notebook can feel a tad less responsive than its Google counterpart.

But if you care about privacy over all else and are willing to get your hands dirty with self-hosted LLMs and APIs to bypass Google’s intrusive and censored platform, you’ve got to give Open Notebook a shot.