PrivateGPT vs Quivr

PrivateGPT

RAG

Quivr

RAG

Tagline

PrivateGPT

Production-ready, air-gapped RAG framework for querying your documents with local LLMs.

Quivr

Open-source RAG framework for building custom AI assistants over your own documents in a few lines of Python.

Pricing

PrivateGPT

Freemium· OSS free; Zylon enterprise contract (contact sales)

Quivr

Free· Open source (pip install quivr-core); pay only for LLM/vector-store usage

Free trial

PrivateGPT

Yes

Quivr

Yes

API

PrivateGPT

Yes

Quivr

Yes

Platforms

PrivateGPT

api

Quivr

api

Open source

PrivateGPT

Yes · Apache-2.0

Quivr

Yes · Apache-2.0

GitHub stars

PrivateGPT

57,552

checked 2026-09-29

Quivr

7,414

checked 2026-09-29

Last GitHub push

PrivateGPT

2026-09-29

Quivr

2025-02-21

First commit

PrivateGPT

2023-05

Quivr

2024-05

Model used

PrivateGPT

Multi-model (BYO local LLM)

Quivr

Multi-model (OpenAI, Anthropic, Mistral, Gemma)

Best for

PrivateGPT

Pick PrivateGPT if you need a private, on-prem RAG stack for regulated data and don't want to ship documents to a hosted LLM provider.

Quivr

Pick Quivr if you are a Python developer who wants a lightweight, model-agnostic RAG library you can extend rather than a hosted chat-your-docs SaaS.

Not for

PrivateGPT

Skip it if you just want a hosted chat-with-PDF SaaS and have no interest in self-hosting models or managing infrastructure.

Quivr

Skip it if you want a turnkey no-code product with a polished UI, hosted vector store, and a sales team to call.

Editorial score

PrivateGPT

7.0 / 10

Quivr

8.4 / 10

Use cases

PrivateGPT

private-ragchat-with-documentson-premises-llmair-gapped-aienterprise-knowledge-base

Quivr

document-qacustom-knowledge-baserag-pipelineinternal-assistantschat-with-pdf

Pros

PrivateGPT

• Fully local and air-gapped; data never leaves your infrastructure
• OpenAI-compatible API makes integration straightforward
• Massive OSS community (57k+ stars) with proven deployments
• Model-agnostic across llama.cpp, Ollama, vLLM, and Qdrant

Quivr

• Genuinely open source and pip-installable, no vendor lock-in
• Model-agnostic: OpenAI, Anthropic, Mistral, and Gemma supported
• Minimal boilerplate to get a working RAG assistant running
• Pairs with Megaparse for tougher PDF and document ingestion
• Customizable pipeline with tools and web search when you need more

Cons

PrivateGPT

• No public pricing for the enterprise Zylon platform
• OSS repo cadence has slowed since the commercial pivot
• Operating at scale still requires meaningful DevOps effort

Quivr

• Python library, not a hosted product or UI
• You manage infra, vector store, and evals yourself
• Documentation site is sparse compared to larger RAG frameworks
• LLM and embedding costs are on you

Website

PrivateGPT

www.zylon.ai

Quivr

core.quivr.com

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.