# Chatbot-UI vs. Open WebUI: A Practical Comparison for Self-Hosted LLM Interfaces ## 1. Quick Verdict (TL;DR) If you are choosing between these two today, **Open WebUI is the clear winner for most users.** It is actively maintained, has a vastly larger community, supports a wider range of backends out of the box, and offers a more polished multi-user experience. **Chatbot-UI** (the `mckind/chatbot-ui` project) filled an important gap in early 2023 as a quick ChatGPT-style UI, but its development has slowed considerably. Unless you have a specific legacy integration that depends on its API shape, there is little reason to start a new deployment on it. That said, Chatbot-UI is not abandoned—it still works—and its lighter footprint can suit minimal setups. The rest of this article breaks down where each tool excels and where it falls short. --- ## 2. Feature Comparison Table | Feature | Chatbot-UI | Open WebUI | |---|---|---| | **Primary purpose** | Web chat front-end for LLMs | Self-hosted all-in-one LLM workspace | | **Default backend** | OpenAI API (or compatible endpoints) | Ollama (local) + OpenAI-compatible APIs | | **Multi-provider support** | OpenAI, Anthropic, Groq, HuggingFace, Replicate, etc. | Ollama, OpenAI, Groq, HuggingFace, LM Studio, SambaNova, and any OpenAI-compatible server | | **Local model support** | Via external API bridge only | Native Ollama integration + custom model paths | | **RAG / file upload** | Yes (LangChain-based) | Yes (built-in document processing, PDF, text, code) | | **Web search (Wolfram, Perplexity, etc.)** | Limited / plugin-based | Built-in web search, URL fetching, Wolfram Alpha integration | | **Multi-user & RBAC** | Basic (single-session focus) | Full user management, roles, API keys per user | | **Function calling / tool use** | Via external plugins | Native tool-calling framework | | **Prompt library / templates** | Basic prompt presets | Community prompt library, import/export, voting | | **Chat export / share** | Markdown, HTML, JSON | Markdown, HTML, PDF, shareable public links | | **Model switching mid-chat** | Yes | Yes | | **Streaming responses** | Yes | Yes | | **Docker / one-liner deploy** | Docker available | Docker, `pip`, and Ollama-bundled installers | | **Mobile / responsive UI** | Basic responsive layout | Polished responsive layout with PWA support | | **Active development cadence (as of mid-2025)** | Infrequent commits, no major releases in over a year | Multiple releases per month, very active issue tracker | | **GitHub stars (approx.)** | ~8 k | ~45 k+ and climbing | | **License** | MIT | BSD-3-Clause (core); some components MIT | --- ## 3. Pros and Cons ### Chatbot-UI **Pros** - Extremely lightweight; the initial codebase is small and easy to audit. - Good "start here" docs if you just want a thin wrapper around an existing OpenAI-compatible endpoint. - Flexible model/provider configuration via environment variables without a GUI. - MIT license makes commercial embedding straightforward. **Cons** - Development has stalled; bugs may linger for months without patches. - No native local-model story—you must run Ollama or vLLM separately and point the UI at it. - Multi-user support is rudimentary; there is no real tenant isolation or per-user API-key management. - Smaller community means fewer plugins, fewer answered issues, and less third-party content. - The RAG pipeline is thinner and less actively improved than Open WebUI's. ### Open WebUI **Pros** - Actively maintained with a large contributor base; new features ship weekly. - First-class Ollama integration: install both, and you have a fully offline LLM stack in under five minutes. - Rich feature set out of the box: RAG, web search, function calling, prompt templates, user management, API-key gating. - Clean, ChatGPT-like UI that also supports a mobile browser and PWA install. - Self-contained deployments via Docker Compose or a single Docker image; optional bundled Ollama. - Strong community: Discord server, forum, and a growing marketplace of user-contributed tools and prompts. **Cons** - Heavier resource footprint than a bare Flask/FastAPI wrapper; expect 1–2 GB RAM minimum for smooth operation. - The feature list can feel overwhelming for someone who only wants "a textbox that talks to one model." - Some advanced features (e.g., certain search backends) require additional services or paid API keys. - Because it moves fast, breaking changes occasionally appear between minor releases; pin your Docker image tags in production. --- ## 4. Pricing Both projects are **free and open-source**. There are no licensing fees, no per-seat charges, and no usage caps. Your actual cost depends on the infrastructure you run them on: - **Fully local (Ollama + Open WebUI on your own GPU machine):** $0 marginal cost after hardware. - **Cloud VPS (e.g., a 4-core / 16 GB instance on Hetzner, DigitalOcean, or AWS):** roughly **$12–$60 / month** for the hosting. Add any paid API tokens (OpenAI, Anthropic, Groq) on top. - **GPU cloud (e.g., RunPod, Lambda Labs) for serving open-weight models:** **$0.30–$1.50 / hour** depending on the GPU tier. - **Chatbot-UI:** identical hosting costs; the software itself is free. There is no hosted SaaS tier from either project's maintainers. If you need managed hosting, you will deploy it yourself or use a third-party managed container service. --- ## 5. When to Choose Each **Choose Open WebUI when:** - You want a turnkey, feature-rich self-hosted LLM portal for a team or family. - You plan to run local models via Ollama and want zero API-key management. - You need multi-user accounts, role-based access, and per-user API keys. - You value an actively maintained codebase and a large community for troubleshooting. - You need built-in RAG, web search, and tool-calling without assembling a separate stack. **Choose Chatbot-UI when:** - You need a deliberately minimal front-end and want to keep the dependency tree small. - You already have a stable, custom OpenAI-compatible API and just need a lightweight chat shell in front of it. - You are embedding a chat widget inside a larger application and prefer the smaller JS bundle. - You specifically rely on a legacy Chatbot-UI API endpoint that has not yet been migrated. - You prefer MIT licensing over BSD-3-Clause for a particular compliance reason. In the vast majority of greenfield projects, Open WebUI is the safer default. --- ## 6. Frequently Asked Questions **Q1: Can I run both side by side on the same server?** Yes. They use different default ports (Chatbot-UI typically 3000, Open WebUI 8080). Just give each its own Docker container and point them at the same Ollama or API backend. Many teams keep a minimal Chatbot-UI instance for a single-agent use case and Open WebUI for the team portal. **Q2: Which handles local models better, Chatbot-UI or Open WebUI?** Open WebUI, by a wide margin. Its Ollama integration is a first-class citizen: model discovery, streaming, vision models, and structured-output parsing all work without extra configuration. Chatbot-UI can call Ollama's OpenAI-compatible endpoint, but you have to manage the Ollama service, model pulls, and health checks yourself. **Q3: Is either one suitable for production, internet-facing deployments?** Open WebUI is commonly used in small-to-medium production environments and ships with user authentication, rate-limiting hooks, and CSRF protection. That said, neither project is a hardened SaaS platform. Put both behind a reverse proxy (Nginx, Caddy) with TLS, enable user authentication, and restrict the management interface to internal IPs. Chatbot-UI, being less actively patched, is a riskier choice for anything exposed to the public internet. **Q4: What is the migration path from Chatbot-UI to Open WebUI?** There is no one-click importer. You will re-create chat histories manually (export JSON from Chatbot-UI, import via Open WebUI's data layer if the schema matches, or simply start fresh). Provider API keys, system prompts, and RAG knowledge bases must be re-entered or re-uploaded. Most users find the switch takes under an hour for a typical setup. **Q5: Do either of these work on a Raspberry Pi or low-spec hardware?** Open WebUI can run on a Raspberry Pi 4/5 with 4 GB RAM, but performance will be modest and you should pair it with a small quantized model (e.g., Llama 3.2 3B via Ollama). Chatbot-UI, being lighter, will load faster on the same hardware, but without a strong GPU or CPU, inference latency will dominate the user experience regardless of which UI you pick. For true edge deployments, consider trimming features in Open WebUI (disable web search, reduce RAG chunks) to keep memory under 2 GB. --- *Last updated: June 2025. Both projects evolve quickly; check their respective GitHub repositories for the latest release notes before committing to either.*