PrivateGPT

PrivateGPT is a free, open-source API layer for building AI applications around an inference server you choose. It suits developers who want document, data, and tool workflows on infrastructure they control. Its flexible API is the draw; operating a separate model server is part of the job.

Overview

PrivateGPT runs as a local server and can be installed as a Python package, from source, or with Docker. It connects to OpenAI-compatible servers such as Ollama, LM Studio, llama.cpp, and vLLM, but does not run models itself. That arrangement leaves model selection open while adding a separate service to set up and maintain.

Application data, including ingested documents, vector-store data, models, and caches, is stored under a local home directory by default. PrivateGPT can also connect to remote compatible servers, so developers need to account for where inference happens when designing a private deployment. Optional observability can trace LLM calls, embedding requests, and retrieval steps through the console, Arize Phoenix, or Opik.

Key features

The Messages API supports streaming, asynchronous processing, and token counting. Those capabilities give developers useful controls when building applications that handle longer tasks or need to report token use. File and artifact ingestion feed retrieval-augmented generation, with citations that help users follow answers back to source material.

PrivateGPT also supports database querying and analysis of tabular data such as CSVs. Documented tools include web search and extraction, code execution, custom tools, and MCP connectors, with MCP usage through the Messages API. The breadth makes it a capable application layer, though the compatibility table marks skills as basic or in progress; prompt caching and OAuth or organizations are unsupported.

A browser workbench at /ui supports testing, demos, and inspection of API requests. It stores chats and settings in browser localStorage and has no access control. That makes it suitable as a local demonstrator, not as a shared production interface where access needs to be restricted.

Pricing

PrivateGPT costs nothing. Its open-source plan is self-hosted and includes the API layer; users bring a separate OpenAI-compatible inference server. This fits developers who can operate their own stack and want to avoid depending on cloud AI APIs. The trade-off is responsibility for deployment and inference, rather than a turnkey hosted model service.

Platforms

PrivateGPT is available for API, Linux, macOS, self-hosted, web, and Windows use. The package installation guide requires Python 3.11 and does not support Python 3.10 or Python 3.12 or later, a narrow version requirement that may complicate installation in environments standardized on other Python releases. Docker is also documented as an installation route. Users can get community support through Discord.

Who it's for

PrivateGPT is a strong fit for developers building private AI products who want one API layer for retrieval, structured data, and tool use, and who can choose and run a compatible inference server. It is less suitable for teams seeking a managed model service, or anyone planning to expose its workbench to multiple users with access controls.

Pros and cons

• Pros: Streaming, asynchronous processing, and token counting give the Messages API more application control than a basic prompt endpoint.
• Pros: File and artifact retrieval with citations, database access, tabular analysis, MCP, and custom tools cover varied application workflows.
• Pros: Open-source, self-hosted deployment and local default storage support infrastructure control.
• Cons: A separate compatible inference server is required, adding setup and maintenance beyond PrivateGPT itself.
• Cons: The workbench has no access control and stores chats and settings in browser localStorage, limiting it to local demonstrations.
• Cons: Python package installation requires Python 3.11; Python 3.10 and 3.12 or later are unsupported.
• Cons: Skills are marked basic or in progress, while prompt caching and OAuth or organizations are unsupported.

Alternatives

For a broader set of retrieval-augmented generation tools, browse Retrieval-Augmented Generation Tools.

• AnythingLLM is an option for readers who want Android support or a freemium offering alongside self-hosted deployment.
• Flowise suits readers who want a free tier with 2 Flows & Assistants, 100 Predictions per month, and 5MB storage.
• txtai is another free, open-source option, installable with pip or Docker.
• I3K RAG Enterprise offers a free AGPL-3.0 self-hosted Community plan with community support.
• RAGFlow may suit readers who prefer a free hosted tier with 5 apps, one team member, and 0.1 GB of dataset storage.
• RAGStack is a free open-source RAG package that uses Astra DB Serverless or Apache Cassandra as a vector store.
• Dify offers a free Sandbox plan with 200 message credits, one workspace member, and five apps.
• Haystack Enterprise Platform is an alternative with a free Studio plan and a free trial.

Verdict

Choose PrivateGPT if you are a developer who wants an open-source API for retrieval, data, and tool workflows on infrastructure you control. Its central advantage is that it connects these capabilities to the compatible model server you select. Look elsewhere if you need inference included or an access-controlled workbench for shared use.