A desktop app and a self-hosted chat UI solve different problems
LM Studio and Open WebUI both run models on hardware you control, and they get compared because of that. The resemblance ends at the install. LM Studio is a desktop application for macOS, Windows and Linux: it downloads and runs local models, offers a chat interface, serves them on OpenAI-compatible endpoints, and connects MCP servers (LM Studio's docs). Open WebUI is a self-hosted platform: one deployment on a server with per-user accounts, retrieval over your files and plugins, reached through a shared URL (Open WebUI's docs). LM Studio serves the person at that machine, and Open WebUI serves a group through one deployment.
What each one is good at
LM Studio's docs list what you can do with it: download and run local LLMs, use a flexible chat interface, connect MCP servers, search and download models from Hugging Face, serve local models on OpenAI-like endpoints, and manage local models, prompts and configurations. Its runtime uses llama.cpp for GGUF models and MLX on Apple Silicon. The current release also ships an agent, Bionic, which LM Studio markets for documents, coding, automations and computer control, with local voice transcription and optional frontier open models in LM Studio Cloud (LM Studio's site, checked October 2026).
Open WebUI's docs describe a self-hosted platform built to run entirely offline, with support for Ollama and OpenAI-compatible APIs. It adds accounts and groups, retrieval over your own files, and a wide plugin and tool ecosystem. Open Terminal gives an agent a terminal and filesystem inside the chat, and Terminals gives each user an isolated environment on a team deployment.
Where the team decision turns
A desktop app and a server deployment are two answers to where the software runs, and a team usually hits the wall in the same place: the work has to be shared, versioned and approved. A desktop app keeps its history, prompts and models on one machine. A chat deployment keeps its settings and users in the application's own database. Neither turns an answer into a change a reviewer can read.
Kortix answers that question by putting the configuration in git. Agents and skills are markdown files, kortix.yaml declares the connectors and triggers, and each session runs on its own cloud computer and branch. The work reaches main through a change request a person reads first, and merge is default-deny for agents (Kortix docs).
Choosing between them
Pick LM Studio when one person wants to run and chat with local models on their own machine, with a local server and MCP tools. Pick Open WebUI when a team wants a shared, self-hosted chat and document window over local and API models. Pick Kortix when the output has to be finished work: agents that use your tools, return deliverables you keep, and land each change for review on infrastructure you choose.
Kortix works with any model provider with your own keys, so a team can keep running local models. What changes is what the team owns: agents, memory and connectors in one repo, and a reviewed change for every update.