PrivateGPT
PrivateGPT is an open-source project that provides a local, offline API for interacting with large language models (LLMs) and performing retrieval-augmented generation (RAG) on private documents.
Profile
PrivateGPT provides an open-source, self-hosted API for running large language models and performing retrieval-augmented generation on private documents, ensuring data never leaves the user's local environment.
PrivateGPT is an open-source project that provides a local, offline API for interacting with large language models (LLMs) and performing retrieval-augmented generation (RAG) on private documents. Founded by Iván Martínez, the project has grown into a community-driven platform with over 57,000 GitHub stars and 7,600 forks. The core product is a self-hosted Python application that allows users to run LLMs like Llama 3, Mistral, and GPT-4o-mini entirely on their own hardware, ensuring data never leaves the user's environment.
The project is maintained by Zylon AI, a company that also offers a managed cloud version called Zylon PrivateGPT. As of June 2026, the project has released version 1.0.0, which includes a redesigned UI, support for multiple LLM providers, and enhanced RAG capabilities. PrivateGPT is used by enterprises and individuals who require strict data privacy, such as legal firms, healthcare organizations, and financial institutions.
The project has not disclosed any formal funding rounds or revenue figures, but its GitHub activity and community engagement suggest a growing user base. The project's main competitor is Ollama, another local LLM runner, but PrivateGPT differentiates itself by offering a full RAG pipeline out of the box. The project's recent 1.0.0 release marks a significant milestone, indicating a shift from experimental to production-ready software. However, the project's reliance on community contributions and lack of a clear business model raise questions about long-term sustainability.
Products by PrivateGPT
Who buys this
- Enterprises requiring strict data privacy for internal document analysis and Q&A
- Legal firms handling confidential client documents and case law research
- Healthcare organizations processing patient records and medical literature
- Financial institutions analyzing sensitive financial reports and compliance documents
- Developers and researchers building privacy-preserving AI applications
Strengths and what to watch
Strengths
- Strong community adoption with over 57,000 GitHub stars and 7,600 forks, indicating widespread developer interest and use
- Full RAG pipeline out of the box, supporting multiple document formats and embedding models, making it easy to deploy for private document Q&A
- Active development with regular releases, including the recent 1.0.0 milestone, suggesting a maturing codebase and commitment to improvement
Watch for
- Lack of disclosed funding or revenue model raises questions about long-term sustainability and support for the open-source project
- Heavy reliance on community contributions for maintenance and feature development, which may lead to inconsistent updates or security vulnerabilities
- Competition from similar open-source projects like Ollama and LM Studio, which may offer simpler or more performant alternatives for local LLM deployment
Recent moves
Key Information
- Industry
- MLOps & AI Infra
- Founded
- 1986
Frequently Asked Questions
What is PrivateGPT and what does it do?
PrivateGPT is an open-source, self-hosted API for running large language models and performing retrieval-augmented generation on private documents. It ensures data never leaves your local environment, making it ideal for strict privacy needs.
How does PrivateGPT keep my data private?
PrivateGPT runs entirely on your own hardware, so your documents and queries never leave your local environment. This self-hosted approach means no data is sent to external servers, ensuring complete privacy for sensitive information.
Who typically uses PrivateGPT?
PrivateGPT is used by enterprises, legal firms, healthcare organizations, and financial institutions that need strict data privacy for internal document analysis. Developers and researchers also use it to build privacy-preserving AI applications.
What LLMs does PrivateGPT support?
PrivateGPT supports running large language models like Llama 3, Mistral, and GPT-4o-mini locally. Its recent 1.0.0 release added support for multiple LLM providers, giving users flexibility in choosing models for their private RAG pipeline.
How does PrivateGPT compare to Ollama?
PrivateGPT differentiates itself from Ollama by offering a full retrieval-augmented generation pipeline out of the box. While both are local LLM runners, PrivateGPT includes built-in support for multiple document formats and embedding models for private Q&A.
What is new in PrivateGPT version 1.0.0?
PrivateGPT version 1.0.0, released in June 2026, includes a redesigned UI, support for multiple LLM providers, and enhanced RAG capabilities. This milestone marks a shift from experimental to production-ready software for private document analysis.
Sources
- github.com — GitHub repository showing 57.2k stars, 7.6k forks, and recent 1.0.0 release
- ir.c3.ai — C3.ai financial results (not directly related to PrivateGPT but included in dossier)
- www.wsj.com — OpenAI revenue and user growth data (not directly related to PrivateGPT but included in dossier)
- futuresearch.ai — OpenAI revenue forecast (not directly related to PrivateGPT but included in dossier)