Open WebUI and Ollama Frontends: Which Interface to Use

Open WebUI is the most capable Ollama frontend available, but it requires Docker and adds complexity that not every setup needs. For a NAS or always-on mini-PC running Ollama, Open WebUI running in a container gives every device on the network a full-featured chat interface with conversation history, document upload, image generation, and multi-user support. For desktop users who just want to run a model and talk to it, LM Studio or Msty are simpler and install in minutes. The right choice depends on whether you want a server-based setup or a desktop-only experience.

Why You Need a Frontend at All

Ollama is a backend inference engine. It runs LLM models and exposes an API that other applications use to send prompts and receive responses. Out of the box, Ollama has a minimal web interface accessible at port 11434, but it shows models and API status, not a chat window. To have a conversation with a model, you either use the command line with ollama run modelname or you install a frontend that connects to the Ollama API and wraps it in a chat interface.

Frontends add things that the Ollama API does not provide: conversation history that persists between sessions, the ability to switch between models during a conversation, document upload for retrieval-augmented generation (RAG), image generation support, voice input, and in some cases multi-user management for sharing a local AI server with other people on the network. The frontend you choose determines how much of that functionality you get and how much complexity you take on.

Open WebUI: The Full-Featured Option

Open WebUI (previously called Ollama WebUI) is the most feature-complete Ollama frontend available. It runs as a Docker container, connects to an Ollama instance over the network or locally, and exposes a web interface on port 3000 that any browser on the network can access. A single Open WebUI installation on a NAS or mini-PC serves every phone, tablet, and computer in the household without installing anything on those devices.

The feature set is extensive. Open WebUI maintains full conversation history, supports switching between models mid-conversation, has a RAG pipeline for uploading documents and querying them, supports image generation through Stable Diffusion and Automatic1111 integrations, includes voice input and text-to-speech output, has a workspace for saving reusable system prompts and personas, and supports multiple user accounts with role-based access control. It also supports connecting to external API providers like OpenAI and Anthropic alongside local Ollama models, so you can switch between local and cloud models from the same interface.

The Docker installation command for Open WebUI is straightforward:

docker run -d -p 3000:80 --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

On a Synology NAS, this runs through Container Manager. On QNAP hardware, it runs through Container Station. The --add-host flag allows the container to reach Ollama running on the NAS host. If Ollama is running on a separate machine, replace the host gateway with the IP address of that machine.

Msty: The Cleanest Desktop Option

Msty is a desktop application for macOS, Windows, and Linux that connects to Ollama and provides a clean, polished chat interface. It does not require Docker or any server infrastructure. Install it on your computer, point it at a local or remote Ollama instance, and start chatting. For users who want local AI without running containers or managing a server, Msty is the most approachable option with a good-quality interface.

Msty includes conversation history, model switching, folder organisation for conversations, and a split-screen mode for comparing responses from different models side by side. It supports connecting to cloud API providers as well, so you can use the same interface for both local Ollama models and cloud services. The free tier covers most personal use cases. A paid plan adds additional features including syncing across devices.

LM Studio: Models and Interface Together

LM Studio bundles model management and a chat interface in a single desktop application. It handles downloading models from Hugging Face, loading them with configurable hardware settings, and chatting with them, all in one place. Where Ollama is a background service that other applications connect to, LM Studio is self-contained. You do not need Ollama at all to use LM Studio.

LM Studio also includes a local server mode that exposes an OpenAI-compatible API, which means applications built for the OpenAI API can point to LM Studio instead of the cloud. The interface is functional rather than polished, but it has good visibility into what the model is doing, including token counts, generation speed in tokens per second, and context usage. It is well-suited to users who want to experiment with model parameters and hardware settings without using the command line.

AnythingLLM: Built for Document Q&A

AnythingLLM is purpose-built for retrieval-augmented generation (RAG), the process of asking an LLM questions about documents you have uploaded. Where Open WebUI includes RAG as one feature among many, AnythingLLM treats it as the primary purpose of the application. You create workspaces, upload documents to each workspace, and query them in conversation. It handles chunking, embedding, and vector storage automatically.

AnythingLLM runs as a desktop application or as a Docker container for server deployment. It connects to Ollama as a backend inference engine, with your own local embedding model for the RAG pipeline. For users building a personal knowledge base from PDFs, text files, web pages, and other documents, AnythingLLM is more focused and purpose-built than Open WebUI for that specific workflow.

GPT4All: The Beginner Option

GPT4All is the most beginner-friendly local AI application available. It bundles model management, a simple chat interface, and basic document Q&A in a single installer. There is no Docker, no command line, and no Ollama required. Download the installer, install it, click to download a model, and start chatting. It handles everything else automatically.

GPT4All is not as capable or flexible as Open WebUI or LM Studio. The interface is basic, model selection is more limited, and it does not expose an API for other applications to use. But for someone installing local AI for the first time who wants to understand what it is like before investing in more infrastructure, GPT4All provides the quickest path from zero to running a local model with no technical overhead.

Frontend Comparison: Which to Choose

Setting Up Open WebUI on a NAS

Open WebUI on a NAS running Ollama is the most capable home AI server configuration. The NAS handles always-on availability, and Open WebUI is accessible from any device on the network without installing anything beyond a browser. The setup process requires Docker support on your NAS (Synology Container Manager, QNAP Container Station, or equivalent).

The process involves running Ollama first (either directly on the NAS or on a separate mini-PC), then deploying Open WebUI as a Docker container pointing at the Ollama API address. First-run setup creates an admin account and connects to the Ollama instance. From that point, additional user accounts can be created for other household members, each with their own conversation history.

Storage for Open WebUI is handled via the Docker volume mount in the run command. Conversation history, uploaded documents, user accounts, and settings all persist in that volume. Backing up the NAS volume containing the Open WebUI data backs up the entire configuration. Updates are handled by pulling a new version of the container image and restarting the container.

Related reading: our NAS buyer's guide, our NAS vs cloud storage comparison, and our NAS explainer.

Use our free AI Hardware Requirements Calculator to size the hardware you need to run AI locally.

Do I need Ollama installed to use Open WebUI?

Open WebUI connects to Ollama to run models, so Ollama must be running and accessible. On a NAS, Ollama can run directly on the NAS host (if supported) or on a separate mini-PC on the same network. Open WebUI can also connect to OpenAI and Anthropic cloud APIs if you configure API keys, so it can function without any local Ollama instance if you only use cloud providers. For full local AI use, Ollama and Open WebUI run together on the same machine or on separate machines on the same network.

Can I access Open WebUI from outside my home network?

Yes, but this requires either port forwarding (if your ISP provides a public IP) or a tunnel service. Most Australian ISPs use CGNAT, which prevents direct port forwarding to a home server. Tailscale is the recommended solution for most home users. Install Tailscale on the NAS or mini-PC running Open WebUI and on your phone or laptop. The Open WebUI interface becomes accessible as a Tailscale IP address from anywhere. The free Tailscale tier supports this without any ongoing cost. See the guide on CGNAT and remote access in Australia for more context.

Does Open WebUI store my conversations on an external server?

No. Open WebUI is self-hosted. All conversation history, user data, uploaded documents, and configuration are stored in the Docker volume on your own hardware. Nothing is sent to external servers except model API calls. If you are using local Ollama models, no data leaves your network at all. If you configure an OpenAI or Anthropic API key in Open WebUI, conversations using those providers go to their respective cloud services, but that is your choice to configure, not a default behaviour.

What is the difference between Open WebUI and LM Studio?

Open WebUI is a server-based web interface that runs in Docker and serves any browser on the network. LM Studio is a standalone desktop application that runs only on the machine it is installed on. Open WebUI requires Docker and some technical setup but supports multi-user households, runs on a NAS, and is accessible from phones and tablets. LM Studio requires no Docker and is simpler to set up, but is only accessible on the desktop where it is installed. Open WebUI also includes significantly more features, including image generation, voice input, and full user management. LM Studio includes built-in model management and hardware configuration visibility that Open WebUI lacks.

Can Open WebUI connect to both local Ollama and cloud AI at the same time?

Yes. Open WebUI supports multiple connection sources simultaneously. You can configure a local Ollama instance for local models and an OpenAI API key for cloud models. In the chat interface, you select which model to use from a dropdown that includes both local Ollama models and cloud models in the same list. This makes Open WebUI a unified interface for local and cloud AI use, letting you switch between them based on the complexity of the task. Local models handle routine tasks at zero API cost; cloud models handle tasks requiring more capability.