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HUGGINGCHAT / CHOOSE THE MODEL BEHIND THE ANSWER
HuggingChat
Hugging Face’s chat interface for available AI models, backed by the open-source Chat UI project. You can explore the hosted interface or self-host the free software with a local compatible model server.
HuggingChat public interface captured October 3, 2026; ML Intern and hosted inference have separate usage conditionsExplore the image ↓
What you can do with it
What this tool is for
HuggingChat is the public chat experience from Hugging Face, while Chat UI is the open-source application that powers it. This distinction is useful for readers who want to try available models and for developers who want a configurable interface around their own model service.
The verified free route here is the software: Chat UI can be self-hosted and connected to a local compatible model server. That does not make every hosted model, cloud job or external tool free. Before choosing a setup, identify which part runs on your machine, which provider receives the request and where conversation data is stored.
01
A small model comparison
02
A local writing interface
03
A controlled tool-assisted question
04
A team interface prototype
A closer look at the product
Official product image. Click to inspect the details.
Product interface and examples
Key features, practical tasks
Capabilities in practice
- Chat with available models: The hosted interface offers a changing model catalog and a model-selection route. Compare models on the same small, representative task instead of assuming that the longest answer or newest name is the best fit.
- Self-hosted interface: Run the Chat UI application under your own configuration. This provides control over the interface and deployment, but it still requires model inference, a data store and an appropriate maintenance routine.
- Compatible API connection: The current main branch uses a compatible chat API and model discovery endpoint. It documents local server options as well as remote providers; interface compatibility does not imply that the model or its outputs have identical behavior.
- Omni routing: Optional routing selects among configured paths for ordinary, multimodal or tool-oriented requests. Keep the selected model visible when comparing results so a routing change is not mistaken for an improvement in the same model.
- MCP tool integration: Configured tools can provide capabilities beyond a model’s text response. Inspect the server and allowed actions before enabling it, because a tool result can involve a separate service, data transfer or operational side effect.
- Persistent conversations: The application stores chat-related data in MongoDB and documents an embedded option for local development. Plan how to retain, back up and remove this data instead of treating a local demo folder as disposable by accident.
- Experimental ML Intern: The broader project includes an experimental mode for machine-learning work using Hub resources and jobs. It has setup, billing and budget requirements and should not be confused with ordinary free local chat.
Get started, step by step
- 01
Choose hosted use or self-hosting
- 02
Select model and data routes
- 03
Run a small representative task
- 04
Add capabilities deliberately
A practical first workflow
A practical first workflow
- Choose hosted use or self-hosting. Use the public interface for exploration, or read the current repository setup for a local deployment. Do not mix hosted-account limits with the rights granted by the software license.
- Select model and data routes. Configure the intended endpoint and protected credentials. Confirm where inference occurs and where conversations are saved before entering material that should remain private.
- Run a small representative task. Start with text you can inspect easily, without attaching unrelated tools. Check model identity, answer quality and any provider usage records before expanding the workflow.
- Add capabilities deliberately. Introduce tools, multimodal input or routing only when needed. Keep a record of configuration changes and a small set of examples so later failures can be traced to the relevant component.
Example prompt or task: Summarize the supplied passage in five points. Preserve the main claim and its limitations, quote no more than a short phrase, and list questions the passage does not answer.
This is a model-comparison task. The same interface can produce different results when the model, routing or provider changes.
Find the right plan for you
Free self-hosted software
Explore official plans ↗Free software Model inference and hosting are separate
The hosted interface requires an account for use. This listing does not promise unlimited hosted inference or free compute jobs.
What is free and what can cost extra
Chat UI is available under Apache 2.0 and can be used as free self-hosted software. A local model uses your hardware and electricity; a remote endpoint can charge for inference. Hosting, database services and operational maintenance can also create costs.
Hugging Face’s Inference Providers documentation lists a small monthly free-user credit allowance, currently $0.10, with purchased credits for additional usage. That API allowance is not a promise of unlimited HuggingChat usage. ML Intern jobs and other compute-backed activities require their own budget review.
A few things worth knowing
Frequently asked questions
Is HuggingChat the same as Chat UI?
HuggingChat is the hosted experience; Chat UI is the underlying open-source application.
What is free in this listing?
The Apache-licensed self-hosted interface software.
Are all remote models unlimited and free?
No. Provider usage and billing must be checked separately.
Can it connect to a local model?
Yes, the current guide documents compatible local model servers.
Do old setup tutorials still apply?
Not always. Several legacy integrations were removed from the main branch.
Does ML Intern mean free GPU work?
No. It has compute, billing and budget requirements.
Does local hosting guarantee private data handling?
Only if the model, tools, storage and configuration also follow the intended data boundary.
The details behind your decision
Explore platforms, inputs and outputs, licensing, and access requirements.
Four useful starting projects
Four useful starting projects
- A small model comparison: Ask two available models to summarize the same permitted text using fixed criteria. Compare factual preservation and usefulness, recording which model and provider produced each result.
- A local writing interface: Connect a suitable local model to a private development instance and revise a few nonsensitive paragraphs. Check the actual endpoint before assuming the text stays on the same machine.
- A controlled tool-assisted question: Enable one trusted tool for a narrow information task. Inspect its result and the final answer separately so a provider failure is not hidden behind fluent model prose.
- A team interface prototype: Build a demonstration with clear data and access boundaries. Decide who maintains the model, database and updates before offering the prototype as a dependable shared service.
Platforms, inputs, outputs and setup
Platforms, inputs, outputs and setup
The current Chat UI main branch has removed older provider-specific integrations, GGUF discovery and legacy web-search helpers. Older tutorials may therefore describe a different version. Use the current compatible-API instructions, or deliberately choose a documented legacy branch if that is the version you intend to maintain.
For a repeatable deployment, record the application revision, model endpoint and database location. A local browser interface is not by itself proof of local inference. Likewise, an open-source interface does not determine the license of downloaded model weights or the privacy terms of a remote provider.
Data, permissions and licensing
Data, permissions and licensing
Trace the complete request path: browser, application server, model provider and optional tools. Keep credentials protected and avoid granting an external tool broader access than the task requires.
Review the hosted service’s privacy policy and each connected provider’s conditions. For self-hosting, decide who can access saved conversations and backups; deleting a message in one layer does not necessarily remove copies in external logs or tools.
Sources, documentation and help
Official sources and review date
Official sources and review date
Reviewed October 3, 2026. Product facts come from the official sources below. Suggested projects, prompts and review methods are FindGoodAI editorial guidance, not measured performance results.
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