Compare the frontends for chat

#What exactly is a frontend?

A frontend is the web or desktop interface that communicates with an inference server: Ollama, LM Studio, llama-server, vLLM, or a cloud API. It handles conversation history, Markdown rendering, accounts, file uploads, and sometimes searching your documents and agents. It doesn't run the model: if the server behind it is stopped or overloaded, the interface can't do anything about it.

The connection point is almost always an OpenAI-compatible API. Ollama exposes its own at http://localhost:11434/v1/ and accepts any dummy key, letting you connect nearly any frontend without specific configuration. Keep this in mind: choosing a frontend does not commit you to a particular engine, and you can change one without rebuilding the other.

#The comparison in one table

The criteria below are what actually determine a deployment: license (it determines commercial use), authentication (it determines sharing), documents (they determine enterprise use), and installation effort. GitHub star counts, recorded on September 30, 2026, measure popularity only, not quality.

#The Open WebUI license: the detail many people get wrong

Many comparisons, including the earlier version of this guide, present Open WebUI as an MIT project. That is no longer accurate. The repository's LICENSE file is a three-clause BSD-style license with an additional fourth clause: it prohibits modifying, removing, or replacing the “Open WebUI” trademark (name, logo, visual identifiers), unless the deployment does not exceed fifty users over thirty rolling days, you have written permission from the rights holder, or you have an enterprise license.

What this changes in practice. Personal use, family, small team: nothing. Deployment for more than fifty users with your own branding (company logo, product name): you need authorization or an enterprise license, or you must keep the branding. Using the interface as-is, with the branding visible, remains possible. If you are considering a large-scale deployment, read the LICENSE file and its history in the repository, and have the person responsible for licensing review it.

#Open WebUI: the default choice with Ollama

Open WebUI describes itself as a self-hosted, extensible AI platform that can run entirely offline, with support for Ollama and OpenAI-compatible APIs. Its main benefit for beginners is the setup: a single Docker command, then a browser. It offers local RAG backed by multiple vector databases, fine-grained role and group management, and enterprise authentication (LDAP, Active Directory, SSO, SCIM).

docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main

Then open http://localhost:3000 and create the first account, which becomes the administrator. If Ollama runs on another server, the README says to set the OLLAMA_BASE_URL variable. The open-webui volume preserves your data: without it, every container recreation erases history and accounts.

Limits to keep in mind: the interface is feature-rich and sometimes complex, each additional function adds configuration and update overhead, and the branding clause described above applies to large deployments.

#LibreChat: multiple providers in one interface

LibreChat targets teams that want a single interface for multiple providers: its repository cites Anthropic, OpenAI, Azure, Groq, Mistral, OpenRouter, Vertex AI, and Gemini, with presets, an administration panel, and multi-user authentication via OAuth2, LDAP, or email. The project is licensed under the MIT license, with no branding requirement.

Typical case: a team that wants Ollama internally but keeps access to a cloud provider for certain requests, with a single interface and monitored quotas. Tradeoff: deployment uses Docker Compose with multiple services, and the “local-first” approach is less pronounced than with Open WebUI.

#AnythingLLM: when the goal is to talk to your documents

AnythingLLM presents itself as an all-in-one application for chatting with your documents and using agents in a multi-user environment, without extensive configuration. Its strength is its organization into workspaces (one per project, client, or topic) and the availability of a desktop app for Mac, Windows, and Linux: install it like regular software, without Docker. The license is MIT.

Important note: the README states that the application includes a telemetry feature that collects anonymous usage information. If you require strict offline operation or confidentiality in a sensitive environment, disable this collection in the settings before connecting any data, and verify what actually leaves the network.

• AnythingLLM: production-ready RAG locally

#SillyTavern: role-playing and fiction

SillyTavern is designed for role-playing and interactive fiction. The repository describes it as a unified interface for numerous LLM APIs (KoboldAI/CPP, Horde, NovelAI, Ooba, Tabby, OpenAI, OpenRouter, Claude, Mistral and others), with visual novel mode, lorebooks (WorldInfo), image generation, speech synthesis, and third-party extensions. It is licensed under AGPL-3.0, which is more restrictive for use as a service.

It isn’t intended to replace a professional chat tool: the interface is dense and character-oriented, not designed for business accounts. For professional use, rule it out. For creative writing with fine-grained sampling controls, it’s the most complete option.

• SillyTavern: the character interface for local LLMs

#Big-AGI: multi-model productivity

Big-AGI is a self-hosted AI suite under the MIT license. Its repository highlights personas, multi-model Beam conversations, image generation, voice, PDF import, and on-premises deployment. With around 7,000 GitHub stars, its community is significantly smaller than Open WebUI's or AnythingLLM's: expect fewer tutorials, fewer ready-made answers when something goes wrong, and greater reliance on a small number of maintainers.

#What no frontend can fix: concurrent users

Opening the interface to five colleagues does not make the machine five times more capable. Each frontend sends its requests to the same engine, which processes them. According to the Ollama FAQ, a model processes one request at a time by default (OLLAMA_NUM_PARALLEL is 1); subsequent requests are queued, up to 512 by default, and then rejected.

Enabling parallelism has a direct memory cost. The documentation states that the required memory grows as the product of the number of parallel requests and the context length: a context of 2,000 tokens with 4 parallel requests allocates the equivalent of 8,000 tokens. For a model with 27 billion parameters that is already close to the card’s limit, parallelism can overflow memory and offload part of the model to the CPU, slowing everyone down.

The practical rule: size the engine before the interface. For more than a few active users at the same time, a server designed for throughput, such as vLLM or SGLang, can usefully replace Ollama behind the same frontend, since they all speak the same OpenAI-compatible API.

• llama.cpp vs vLLM vs Exllama

#Which frontend for which profile

#Before opening it to other people

A frontend that works on your workstation isn't ready to serve a team. Three checks are essential before sharing the address. First: enable authentication and disable open registration; otherwise, anyone on the network can create an account. Second: put the service behind an HTTPS reverse proxy, especially if traffic leaves the local network. Third: never expose the engine's port directly (11434 for Ollama), which has no built-in authentication.

These points are covered in detail in the security and network-sharing guides. If you are the only user on a machine, the frontend does not need all this, but getting into the habit of checking what is listening on the network is worthwhile from the start.

#Test a frontend in thirty minutes

1. 01
   Connect the Engine

   Launch the frontend and point it to your engine using the OpenAI-compatible URL. Verify that a simple conversation works before changing any other settings.
2. 02
   Send a real document

   Upload a PDF about your work—not a demo example—and ask three questions whose answers you know.
3. 03
   Create a second account

   Test registration, roles, and access to another user’s conversations: this is where frontends differ most.
4. 04
   Monitor the network

   Monitor the application’s outbound connections during a conversation. Nothing should be sent to a service you did not choose.
5. 05
   Decide

   Keep the one whose output is reliable, whose license suits your use case, and that you’ll be able to update six months from now.

• Secure your Ollama server
• Open WebUI with Ollama: complete guide
• LibreChat: the multi-user web interface
• Source: Open WebUI repository
• Source: LibreChat repository
• Source: AnythingLLM repository
• Source: Ollama documentation, OpenAI compatibility