Open WebUI vs LibreChat vs AnythingLLM: which self-hosted chat UI should you run?

What do these three projects do?

All three give you a private, self-hosted chat interface for large language models, so your team can use local or API-based models without sending conversations through a consumer chat app.

Open WebUI began as a front end for Ollama and grew into a full platform: multi-user accounts, model management, document retrieval, tools and pipelines. It works with local models and OpenAI-compatible APIs.

LibreChat is a ChatGPT-style interface that connects to many providers in one place, including OpenAI, Anthropic, Google, Azure and custom endpoints. It adds agents, conversation branching, presets and MCP support.

AnythingLLM, from Mintplex Labs, is organised around workspaces that hold documents. It focuses on chatting with your own files, ships a desktop app as well as a Docker server, and includes agent skills.

They overlap heavily, so the useful question is what each is centred on: Open WebUI on running models for a team, LibreChat on unifying many providers, and AnythingLLM on your documents.

How do they compare side by side?

Why teams choose Open WebUI

Open WebUI feels closest to a finished product. The interface is polished, onboarding is quick, and pairing it with Ollama gives a private assistant on a single machine with very little configuration.

Admins get user roles, groups, model access control and usage settings, which matter as soon as more than a handful of people use it. Pipelines and functions let developers extend behaviour in Python.

The licence is the thing to read carefully. The project moved to its own licence that adds conditions about keeping its branding, which matters if you want to white-label it for customers. Internal use is typically fine, but confirm against the current licence text.

Open WebUI also has a large community sharing functions, tools and model presets, so many small extensions already exist. Quality varies, so review community code before installing it on a shared instance, because it runs with server permissions.

It also supports features such as web search, image generation connections and voice input, so a single deployment can cover most of what people use a consumer chat app for.

Why teams choose LibreChat

LibreChat is the natural choice when people want to switch between models from several vendors in one familiar interface. A single configuration file defines endpoints, models and features, which keeps deployments reproducible.

It covers features power users expect: editing and branching conversations, presets, file uploads, agents with tools, and MCP servers. Authentication supports common providers, so it fits into company login setups.

Its permissive MIT licence makes it attractive for companies that want to customise or embed it. The trade-off is operational: a full deployment runs several services, including MongoDB and a search component, so plan resources accordingly.

Because it treats providers as interchangeable endpoints, LibreChat is also useful for comparing models side by side on real work, which helps teams decide which provider to standardise on.

Why teams choose AnythingLLM

AnythingLLM is built for “chat with our documents”. You create a workspace, drop in files or connect sources, and it handles chunking, embedding and retrieval with sensible defaults and a choice of vector databases.

The desktop app is a genuine differentiator: a non-technical user can install it, point it at a local model and get private document chat without Docker. The server version adds multi-user support for teams.

Because the workspace concept is central, it is less natural as a general-purpose chat front end with many models and personas. It shines when documents are the point.

AnythingLLM also exposes an API and an embeddable chat widget, so a workspace can power a simple support bot on a website. That is handy for small teams, though a dedicated support product may be better for serious customer-facing use.

Its defaults are friendly, including a built-in vector store, but for large document sets you will want to choose the embedding model and vector database deliberately.

Which should you choose?

• Local models via Ollama for a team, with good admin controls: Open WebUI.
• One interface for OpenAI, Anthropic, Google and others, with a permissive licence: LibreChat.
• Private document Q&A with minimal setup, including for non-technical users: AnythingLLM.
• White-labelling a chat product for clients: LibreChat or AnythingLLM, after checking licences; Open WebUI’s branding conditions need review.
• Single laptop, single user: AnythingLLM desktop or Open WebUI with Ollama.
• Heavy use of MCP tools: all three have some support; test the specific servers you need.

How much does it cost to run?

The software is free to self-host. Costs come from the models: API usage billed by your providers, or hardware for local models. A chat UI on its own needs modest resources; a local model is what demands a GPU or a large-memory machine.

For RAG, add embedding costs and storage for the vector database. LibreChat’s full stack and multi-user Open WebUI deployments need more memory than a single-user desktop app, so size servers by the number of concurrent users.

Remember the people cost: someone must apply updates, which arrive frequently for all three projects, and back up conversation data.

If you plan to serve local models to a whole team, the model server is the part to size carefully. A chat UI in front of a single GPU box will queue requests at busy times, so set expectations or add a second model server.

Mixing is common: a local model for sensitive documents and a commercial API for everything else, both behind the same chat interface, with clear labels so users know which one they are talking to.

How to choose and roll out

A pilot surfaces the real questions quickly: which documents fail to parse, which model people prefer, and which features nobody uses. Decide the platform after the pilot, not before, and keep the first deployment small enough to rebuild.

Treat the chat UI as replaceable. Keep documents in a source of truth outside it, so switching front ends later means re-indexing, not losing knowledge.

• Decide where models come from: local only, APIs only, or both.
• List the documents people will ask about and test retrieval quality on them.
• Set up single sign-on and roles before inviting users, not after.
• Pilot with five to ten users for two weeks and collect specific failures.
• Write a short policy on what data may be uploaded, especially with external API models.

Common mistakes

RepoLoot’s catalog tags self-hosted chat UIs by licence and deployment difficulty, which helps when you want to compare more options than these three.

• Exposing the instance to the internet without TLS, strong auth and rate limits.
• Assuming conversations stay private when the backend model is a third-party API.
• Judging RAG on one demo document instead of your messy real files.
• Skipping backups of the database that stores users, chats and settings.
• Ignoring licence terms before rebranding or reselling.

Frequently asked questions

Can I use Open WebUI with Claude or GPT models?

Yes, through OpenAI-compatible connections or pipelines and functions that bridge to other providers. Open WebUI is closely associated with Ollama, but it is not limited to local models. LibreChat is often simpler if multi-provider access is the main goal.

Which has the best RAG?

AnythingLLM is the most document-centred out of the box, with workspaces built around your files. Open WebUI offers knowledge collections with configurable retrieval, and LibreChat uses a separate RAG service. Quality depends heavily on chunking, embedding model and your documents, so test with real files.

Are they safe for company data?

They can be, if you deploy them properly: private network or TLS, single sign-on, role-based access, backups and updates. The bigger question is the model. With local models data stays on your hardware; with external APIs it goes to that provider under their terms.

Can I white-label these chat UIs?

LibreChat and AnythingLLM use the permissive MIT licence, which generally allows modification and rebranding with the licence notice kept. Open WebUI’s licence includes branding conditions for larger deployments. Always read the current licence file and consider legal advice for commercial resale.