AnythingLLM vs Open WebUI

AnythingLLM and Open WebUI look interchangeable from a distance — both are self-hosted chat front-ends you put in front of whatever LLM you can reach, both ship as a single Docker container, both start from the same 2 GB RAM floor, and both rate the same friendly 2 out of 5 to deploy. But they are built around different questions. Open WebUI asks "how do I chat with my models?"; AnythingLLM asks "how do I chat with my documents?". Answer honestly which one you are really asking and the choice mostly makes itself.

Document workspaces vs. a better ChatGPT

AnythingLLM is RAG-first. Its organizing unit is the workspace: you create one per project or topic, ingest documents into it, and every chat in that workspace is grounded in those documents. Ingestion is built in — no sidecar retrieval service to stand up — and the app is deliberately agnostic about the rest of the stack: it works with any local or hosted LLM and lets you plug in the vector database of your choice rather than imposing one. It also ships agents that can act on your workspace — searching, summarizing, and pulling in live data — which pushes it past "chat with PDF" toward a small self-hosted research tool. The stack is JavaScript/Node.js with a React front-end, all in one container.

Open WebUI is chat-first. It is the polished, most popular self-hosted ChatGPT-style interface, Ollama-native from its origins: point it at an Ollama backend and every pulled model appears in the picker, with any OpenAI-compatible API available beyond that. It is not without retrieval — you can upload documents into chats and build knowledge bases — but retrieval is a feature here, not the architecture. Where it clearly leads is everything around the chat: model management, multi-user administration with role-based access control, and a large community producing functions, pipelines, and tooling. The stack is Python behind a Svelte/TypeScript front-end.

Models and backends

Neither app runs models itself — both are front-ends you pair with an inference backend, and for local models that backend is usually Ollama in both cases. The difference is emphasis. Open WebUI's happy path is Ollama; the integration is close to zero-configuration and the product is designed around it. AnythingLLM treats the model as one more pluggable slot: local runtimes and hosted APIs are configured the same way, per workspace if you like, which suits setups that mix a local model for private documents with a hosted one for heavier reasoning.

Setup and footprint

There is little to separate them operationally. Each is a single official Docker container — AnythingLLM on port 3001 with a storage volume for its database, documents, and vector cache; Open WebUI on port 3000 with a data volume — and both carry the same 2 GB floor and 2 / 5 difficulty. One quirk: AnythingLLM's official quick start runs the container with an added SYS_ADMIN capability, which is more privilege than most self-hosted apps ask for and worth knowing about before you run it next to other workloads.

Licensing

This is the sharpest formal difference. AnythingLLM is MIT-licensed — use it, modify it, embed it, white-label it. Open WebUI is source-available, not OSI open source: it ships under the "Open WebUI License," a modified BSD-3-Clause with a branding clause that restricts removing or altering the Open WebUI branding unless you meet its conditions. Self-hosting for your own team, this changes essentially nothing. Building a product on top of the UI, or rebranding it for clients, it changes a lot — read the clause first, or pick the MIT option and skip the reading.

Who each is for

Pick AnythingLLM if…

• Your documents are the point — you want workspaces with built-in ingestion and grounded answers, not a chat window with an upload button.
• You want agents and a pick-your-own vector database in one self-contained container.
• A permissive MIT license matters — you may embed, extend, or white-label.

Pick Open WebUI if…

• You want the best general-purpose self-hosted ChatGPT, especially over Ollama and local models — the most polished option with the smoothest defaults.
• You value the larger community and extension ecosystem, plus built-in RBAC and multi-user administration for a shared deployment.
• You are self-hosting for your own use and the source-available license does not constrain you.

Running either on a VPS

Either fits comfortably on a small server at the 2 GB floor — but that floor is for the web tier only. If you plan to run models locally through Ollama, the inference backend is where the real memory and compute go, so size that up substantially (and ideally add a GPU). AnythingLLM's RAG workloads add their own weight as your document corpus grows, since embeddings and the vector store live on the same box by default. Whichever you pick, back up its data volume — that is where your workspaces, conversations, and users live. The step-by-step setups are linked below, and any of the VPS options here has room for either app plus a modest model backend.