Self-Hosted ChatGPT Alternative: Open WebUI vs LibreChat

If you want a ChatGPT-style interface that you host yourself, three open-source projects dominate the shortlist: Open WebUI, LibreChat and AnythingLLM. They are interfaces, not models. All three connect to a model backend of your choice, which can be a local engine such as Ollama or vLLM or a hosted API, and the privacy you get depends entirely on that backend and on how you deploy the interface.

This comparison covers what each project offers, the licensing difference that catches people out, and the work that remains after the interface is running. Details were checked against project documentation in October 2026, but these projects release often, so verify the current state before you commit.

What counts as a self-hosted ChatGPT alternative?

Three layers make up the thing people picture when they say "ChatGPT, but ours."

1. The interface. Chat, history, file upload, prompt sharing, user management.
2. The model backend. The thing that generates text: an open-weight model served locally, or an external API.
3. The surrounding platform. Retrieval over company documents, agents and tools, a gateway, logging and policy.

The open-source projects below are layer 1, with some layer 3 features. Layer 2 is your choice, and it determines where prompts go. Pointing a self-hosted interface at a hosted API still sends every prompt to that provider. That is sometimes exactly what you want and sometimes the opposite of what you thought you were buying, so decide deliberately.

How do Open WebUI, LibreChat and AnythingLLM compare?

Sources: Open WebUI documentation, the LibreChat repository and Open WebUI feature summaries. Feature lists reflect public descriptions and not a hands-on audit, so run a pilot.

What is the Open WebUI licensing catch?

Open WebUI is free to run. But since version 0.6.6 its license adds a branding-protection clause on top of BSD-3: you may not alter, remove or replace the Open WebUI branding unless you have 50 or fewer users in a 30-day period, written permission, or an enterprise license that allows it, per the project's license page. The project's own documentation says the license is not OSI-approved open source.

For an internal deployment that keeps the branding visible, the clause changes little. If you plan to rebrand the interface for a larger group or white-label it for customers, you need to read the text and probably talk to the project. LibreChat and AnythingLLM are MIT licensed, which has no branding requirement. This is not legal advice, so ask counsel to read the license for your use.

Which one should you choose?

• Choose Open WebUI if you want the closest match to a ChatGPT experience with a local model engine, and your identity needs include LDAP or SCIM. Plan for the license terms if you will rebrand.
• Choose LibreChat if you want one front end for many providers at once, MCP tools and agents, and a permissive license. It is a strong fit when your users should switch between local and hosted models in one place.
• Choose AnythingLLM if the center of gravity is document chat with separate workspaces per team, and you want a lighter deployment.
• Choose none of them if your requirement is mostly governance of agents and coding tools on employee machines. That is a different problem, covered below.

What does a self-hosted interface not give you?

This is where most pilots stall. The interface is perhaps a fifth of the work.

• A model that is good enough, on hardware that is fast enough. You need a serving layer and GPUs, or a small model on workstations. See the self-hosted LLM stack guide and running a model locally.
• Permission-aware retrieval. Built-in document chat usually indexes what you give it. Respecting the permissions of your source systems is your integration work.
• A policy for what leaves. If any user can add an external API key, your private deployment has a hole. Control provider keys centrally, ideally behind a gateway.
• Governed agents. Tool calling and MCP are powerful, and an agent with a broad token is a risk. Define scopes and approval steps. See MCP and agent interoperability.
• An audit trail you can show someone. Chat history is not an audit record.
• People. Upgrades, backups, security patches, user support and on-call. The TCO analysis is a realistic way to count them.

Our post on building your own AI on your own data compares the open-source toolchain with a managed path in more depth, and internal AI assistants beyond ChatGPT Enterprise looks at how companies approach this decision.

What is the minimum production setup?

A defensible minimum for a team:

1. The interface behind your single sign-on, on an internal address.
2. One inference endpoint, on your hardware, with the model version pinned.
3. A gateway in front of all model traffic, with centrally managed provider keys.
4. A vector store with ingestion that carries source permissions.
5. Central logs, backups and a tested restore.
6. An acceptable-use policy and a short onboarding for users.

From there, you can add hosted models for tasks the data class allows, and agents with scopes.

How does this compare to a hosted assistant?

A hosted assistant gives you polish, frontier models and no operations. A self-hosted stack gives you control of where inference runs and what is logged, and costs you operations. The honest comparison is in AI workspace versus ChatGPT Enterprise, and the broader buyer's view is in the private AI workspace guide.

Where does Swfte fit?

Swfte covers the layers around the interface. Cortex is a governed AI desktop, not a server-side chat page: it answers from company files and meetings, runs on the laptop by default and puts coding agents such as Claude Code and Codex under one policy and one audit trail. BuildX is the gateway that routes across 50+ models and controls provider access. Studio builds agents and workflows without code, Nexus enforces policy on agent actions, and dedicated cloud provides isolated infrastructure if you would rather not run GPUs yourself. See also Dify versus Swfte for an open-source agent-platform comparison, and the platform overview.

Swfte is designed to provide technical controls, governance mechanisms and evidence for deploying AI within your own regulatory, security and policy requirements. The exact posture depends on your use case, jurisdiction, deployment and configuration. To talk through a self-hosted rollout, contact the team.

Frequently asked questions

What is the best self-hosted ChatGPT alternative?

There is no single best. Open WebUI is closest to the ChatGPT experience with local models, LibreChat is strongest for multi-provider use under an MIT license, and AnythingLLM suits document-centric workspaces. Pilot two against your own requirements.

Is Open WebUI really open source?

Its code is public and free to run, but since v0.6.6 it carries a branding clause, and the project's own documentation says the license is not OSI-approved. For internal use that keeps the branding, the practical effect is small.

Are these tools private by default?

The interface runs on your server, but privacy depends on the model backend. With a local model, prompts stay inside. If you connect a hosted API, prompts go to that provider.

Do I need a GPU?

Not for small models on modern workstations, and not at all if you point the interface at a hosted or dedicated API. A shared server with GPUs makes sense for larger models and many users.

Can I use these with Ollama?

Yes. Open WebUI integrates with Ollama natively, and LibreChat supports Ollama as a provider. Ollama is MIT licensed and serves a local API on port 11434 by default.

Related: Swfte Connect is the model gateway, designed to run in your own cloud or data centre; see self-deploying Connect.