How much does Kotaemon cost?

Kotaemon is free and open-source. You only pay for your own self-hosted infrastructure, such as the LLM and vector backend you choose to run it on.

Which LLMs and vector stores are supported?

It supports OpenAI, LlamaCPP, and any OpenAI-compatible endpoint. For vector stores, you can use Chroma, Qdrant, Milvus, LanceDB, or Elasticsearch.

Is Kotaemon suitable for enterprise use?

Skip it if you need a polished SaaS product, enterprise SSO out of the box, or a managed service. Production deployments may require extra work for auth and observability.

What file formats can I upload?

Kotaemon handles ingestion of PDFs, DOCX, Excel, HTML, and plain text files. It embeds these into your chosen vector store for grounded answers with citations.

How hard is it to set up?

Setup takes about 10 minutes via a HuggingFace Spaces template or 20 minutes using installer scripts for Windows, macOS, or Linux. It is designed for self-hosting.

Kotaemon

Open-source RAG UI for chatting with your own documents, locally or self-hosted.

Free· Free, open-source (MIT-style); self-hosted infrastructure costs onlyRAGMulti-model (OpenAI, LlamaCPP, any OpenAI-compatible endpoint)7.0 / 10

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In short

Kotaemon is a free, open-source RAG UI for chatting with local documents. It supports multiple LLMs and vector stores, offering full control without SaaS costs.

Best for

Pick Kotaemon if you want a hackable, self-hosted RAG UI over your own documents with citation support and full control of the LLM and vector store.

Skip if

Skip it if you need a polished SaaS product, enterprise SSO out of the box, or a managed RAG service you don't have to deploy yourself.

Kotaemon is an open-source document question-answering application built by Cinnamon AI that wraps a full RAG pipeline behind a usable web interface. It handles ingestion of PDFs, DOCX, Excel, HTML and plain text, embeds them into a vector store of your choice (Chroma, Qdrant, Milvus, LanceDB, or Elasticsearch), and serves grounded answers with inline citations. The UI ships with admin authentication, multi-user support, and configurable retrieval, re-ranking, and embedding pipelines out of the box.

It's aimed at two audiences: end users who want a private ChatGPT-for-my-files without piping data to a SaaS vendor, and developers who want a hackable RAG starter they can extend rather than build from scratch. Pricing is effectively free — you host it yourself, either via a HuggingFace Spaces template (~10 minutes) or with the Windows/macOS/Linux installer scripts (~20 minutes). Costs are whatever your chosen LLM and vector backend charge.

Kotaemon is model-agnostic: plug in OpenAI, local LlamaCPP models, or any OpenAI-compatible endpoint (Ollama, vLLM, LM Studio, OpenRouter). Optional web-search retrieval via Jina or Tavily extends it beyond local corpora. It's a Gradio-based app, so the UI is functional rather than polished, and production-scale deployments will need work around auth, observability, and concurrency.

Editor's take

Kotaemon is one of the better open-source RAG frontends to land in the last couple of years — it strikes a workable balance between batteries-included and pluggable. Treat it as a strong starting point for an internal knowledge bot rather than a finished product. You will end up customizing the UI and auth before anyone serious uses it.

— The AI Tool Bible editorial team

Pros

✅ Genuinely model- and vector-store-agnostic; swap backends without touching code
✅ Citations with source highlights, not just naked LLM answers
✅ One-click HuggingFace Spaces deploy or local installer scripts
✅ Active GitHub project with clear extension hooks for developers

Cons

⚠️ Gradio UI feels prototype-grade compared to commercial RAG products
⚠️ Default admin/admin credentials and thin auth aren't production-ready
⚠️ Self-hosted only — no managed SaaS option if you don't want to run it

Use cases

document-qaprivate-ragcitation-grounded-chatlocal-llm-frontendknowledge-base-search

Frequently asked

How much does Kotaemon cost?: Kotaemon is free and open-source. You only pay for your own self-hosted infrastructure, such as the LLM and vector backend you choose to run it on.
Which LLMs and vector stores are supported?: It supports OpenAI, LlamaCPP, and any OpenAI-compatible endpoint. For vector stores, you can use Chroma, Qdrant, Milvus, LanceDB, or Elasticsearch.
Is Kotaemon suitable for enterprise use?: Skip it if you need a polished SaaS product, enterprise SSO out of the box, or a managed service. Production deployments may require extra work for auth and observability.
What file formats can I upload?: Kotaemon handles ingestion of PDFs, DOCX, Excel, HTML, and plain text files. It embeds these into your chosen vector store for grounded answers with citations.
How hard is it to set up?: Setup takes about 10 minutes via a HuggingFace Spaces template or 20 minutes using installer scripts for Windows, macOS, or Linux. It is designed for self-hosting.