Open WebUI vs LibreChat

Open WebUI and LibreChat are the two names that top every shortlist when you decide to self-host a ChatGPT-style interface instead of paying per seat. Both are polished web front-ends that put a chat window in front of whatever models you can reach, both run in Docker, and both start from the same 2 GB RAM floor. The choice between them is not really about whether they work — they both do — it is about what you are optimizing for: the smoothest possible local-model experience, or a multi-provider power tool with agents and tooling baked in.

Polished Ollama-native UI vs. multi-provider power tool

Open WebUI is the more popular and the more polished of the two, and it is unapologetically Ollama-native. It began life as the default web UI for Ollama, and running a local model behind it is close to zero-configuration: point it at your Ollama backend and every pulled model appears in the picker. It also speaks any OpenAI-compatible API, so remote models are available too, but the product is designed around the local-first workflow. The stack is Python on the backend with a Svelte/TypeScript front-end, the web tier is genuinely lightweight, and it rates a friendly 2 out of 5 to deploy. If your goal is "the easiest self-hosted ChatGPT, talking to models on my own box," this is the one built for it.

LibreChat is the multi-provider power tool. Where Open WebUI leans local-first, LibreChat is built to unify many providers behind one UI — OpenAI, Anthropic, Google (Gemini/Vertex), Azure, AWS Bedrock, Mistral, Groq, DeepSeek, OpenRouter, and local Ollama — and to let users switch between them mid-conversation. Its stack is heavier: TypeScript/React/Node.js with MongoDB as the datastore, and it rates a 3 out of 5 to deploy, the extra point reflecting the database plus the provider and feature configuration you wire up in .env before it sings.

Providers and models

This is LibreChat's clearest advantage. Out of the box it treats a broad set of commercial and local providers as first-class endpoints, and it supports per-user API keys — you can set a provider to user_provided so each person brings their own key from the UI rather than sharing one server-side secret. That makes it a natural fit for a team that wants one interface over Claude, Gemini, and GPT at once, with usage attributed per person.

Open WebUI reaches the same remote models through OpenAI-compatible endpoints, but the experience is centered on Ollama and local inference. If most of your chatting happens against a model on your own hardware, Open WebUI's defaults are already pointed the right way; if you are fanning out across several hosted providers, LibreChat's endpoint model does less fighting.

Features: RAG, agents, MCP

Both ship retrieval-augmented generation, but with different philosophies. Open WebUI builds RAG in directly — hybrid document search over files you upload into chat workspaces, web-search injection from several providers, and knowledge bases — so document Q&A works without standing up extra services. LibreChat provides RAG through an optional companion RAG API (LangChain + pgvector), which is more moving parts but keeps retrieval as its own scalable tier.

On agents and tooling, LibreChat is the deeper toolbox. It offers a genuine agent builder, strong Model Context Protocol (MCP) support for wiring in external tools, custom OpenAPI actions, and a secure sandboxed code interpreter spanning several languages. This is what makes it "more than a chat window" for developer-facing workflows. Open WebUI is far from bare — it has its own extensibility via functions and pipelines, web search, and image generation — but LibreChat's agent-and-MCP story is the more built-out of the two today.

Where Open WebUI pulls ahead is administration and team knowledge work: it ships role-based access control, user whitelisting, and a super-admin account out of the box, which admins consistently praise for multi-user deployments.

Setup and footprint

The specs put them on the same starting line — both want 2 GB of RAM as a realistic floor — but the shape of the deployment differs. Open WebUI's happy path is a single container pointed at Ollama, which is why it earns the lower 2 / 5 difficulty. LibreChat is an app-plus-MongoDB deployment, and the more of its provider and feature surface you turn on (the RAG API, the code interpreter sandbox), the more services you are running — hence the 3 / 5. Neither is fragile; both publish official Docker setups. But if "stand it up and forget it" is the priority, Open WebUI has less to keep healthy over time.

Licensing

This is the one place to read the fine print. LibreChat is MIT-licensed — about as permissive and unencumbered as self-hosted software gets. Open WebUI is source-available, not OSI open source: it ships under the "Open WebUI License," a modified BSD-3-Clause with an added branding-protection clause that restricts removing or altering the Open WebUI branding unless you meet its conditions. For most people self-hosting for their own team it changes nothing, but if you intend to white-label or redistribute the UI, LibreChat's MIT terms are the cleaner ones, and Open WebUI's clause deserves a read before you build on it.

Who each is for

Pick Open WebUI if…

• You want the easiest self-hosted ChatGPT, especially with Ollama and local models — the polished, most popular option with the smoothest defaults.
• You value built-in RAG, image generation, and out-of-the-box RBAC and multi-user administration for a team knowledge base.
• You are fine with a source-available license and are not white-labeling the UI.

Pick LibreChat if…

• You need many providers at once — Anthropic, Google, OpenAI, Bedrock and more — with the ability to switch mid-chat and per-user API keys.
• You want the deeper agents, MCP, and code-interpreter tooling for developer-facing workflows.
• A permissive MIT license matters to you, and you do not mind running MongoDB and a bit more configuration.

Running either on a VPS

Both run comfortably on a single small server from the same 2 GB floor, but remember that floor is for the web tier — if you plan to run models locally with Ollama, size up substantially (and ideally add a GPU) for the inference backend, which is where the real memory and compute go. Whichever you pick, back up its datastore — Open WebUI's data volume or LibreChat's MongoDB — because that is where your conversations, users, and settings live. The step-by-step setups are linked below, and any of the VPS options here has room for the app plus its database.