[{"data":1,"prerenderedAt":194},["ShallowReactive",2],{"seo-verification":3,"blog-open-webui-librechat-dify-which-one-to-choose-on-a-vps-en":6},{"google":4,"bing":5},"EycwPY2XMyTkVzas3n1ygeNJFGAH513qrMjfDljzsMQ","",{"id":7,"slug":8,"slugs":9,"title":13,"excerpt":14,"readTime":15,"views":16,"isPinned":17,"publishedAt":18,"category":19,"categories":25,"featuredImage":27,"bgImage":28,"posterImage":29,"relatedSolution":30,"intro":33,"sections":34,"ctaTitle":140,"ctaBody":141,"ctaButton":142,"ctaUrl":143,"relatedPosts":144},329,"open-webui-librechat-dify-which-one-to-choose-on-a-vps",{"fr":10,"en":8,"ar":11,"es":12},"open-webui-vs-librechat-vs-dify-choisir-interface-ia-vps","open-webui-أو-librechat-أو-dify-أيها-تختار-على-vps","open-webui-librechat-dify-cual-elegir-en-un-vps","Open WebUI, LibreChat, Dify: which one to choose on a VPS?","Open WebUI, LibreChat or Dify: three open-source AI interfaces, three usage profiles. Three questions to choose in under five minutes.",10,0,false,"2026-09-04T00:00:00+00:00",{"id":20,"name":21,"slug":22,"color":23,"icon":24},1,"Artificial Intelligence","intelligence-artificielle","bg-purple-500\u002F10 text-purple-400","ia",[26],{"id":20,"name":21,"slug":22,"color":23,"icon":24},null,"\u002Fblog\u002Fcovers\u002Fbg.svg","\u002Fblog\u002Fcovers\u002Fopen-webui-vs-librechat-vs-dify-choisir-interface-ia-vps-poster.svg",{"categorySlug":31,"appSlug":32},"artificial-intelligence","open-webui","Open WebUI, LibreChat and Dify are all available in the ServOrbit Marketplace. Three VPS templates, three distinct architectures, three answers to needs that do not overlap as much as one might think at first glance. On the surface, all three offer a chat interface for major language models — but their architectures, resource requirements and use cases diverge significantly. This guide asks three binary questions to point you directly to the tool that fits your actual situation.",[35,39,50,53,66,69,72,127,130,134,137],{"type":36,"title":37,"body":38},"h2","Why three distinct tools for the same apparent need?","The confusion comes from the fact that all three tools offer a chat interface and support major language models. But their architectures address fundamentally different intentions — mixing them up leads to under-provisioning your VPS, deploying a full builder for a solo use case, or missing the MCP support an entire automation chain depends on.\n\n**Open WebUI** is a chat interface built around Ollama: it serves local models from your server, keeps your data on your infrastructure, and adds a native RAG layer to query your documents. Its priority is privacy and straightforward access to local models. Open WebUI License (BSD 3-clause), 135,000 GitHub stars in September 2026, version v0.8.x actively maintained.\n\n**LibreChat** is a multi-provider platform: OpenAI, Anthropic, Ollama, Azure, Mistral, Groq — it unifies these connections in a single interface and adds agents compatible with the Model Context Protocol (MCP). Its strength is flexibility: connecting multiple AI providers from a single workspace with a shared history. MIT license.\n\n**Dify** is a visual AI application builder: you assemble workflows without writing code, automatically expose an API, and manage multiple applications from a single workspace. Its value is productivity for teams that want to put AI use cases into production without depending on a developer for every new need. Dify Open Source license (Apache 2.0 with additional conditions). Its Cloud Team plan is a paid subscription — deploying on a VPS eliminates this recurring cost.",{"type":40,"title":41,"items":42},"ul","What all three tools have in common",[43,44,45,46,47,48,49],"Available as templates in the ServOrbit Marketplace — deployable on a Cloud VPS in minutes","Self-hosted: conversations, documents and history remain on your infrastructure","Support for major language models via OpenAI-compatible API","Responsive web interface accessible from a browser, no client-side installation required","Multi-user authentication with role and permission management","Active in 2026, maintained by communities with tens of thousands of GitHub stars","Deployable on a single VM or on separate VPS instances depending on your architecture",{"type":36,"title":51,"body":52},"Question 1: are you working exclusively with local models?","If your top priority is running language models locally — Llama 3, Mistral, Gemma, Qwen — without sending a single request to an external provider, **Open WebUI is the direct answer**.\n\nDeployed alongside Ollama, it presents a chat interface comparable to ChatGPT but entirely on your VPS. You manage multiple models from the same panel, enable RAG to query your PDFs and internal documents, and share access with your team using defined roles. The interface supports multimodal conversations (text, images, code), shared sessions between users, and SSO authentication via OpenID Connect.\n\nOpen WebUI suits the developer testing models locally before integration, the team that wants an internal document assistant without cloud dependency, and the organization that needs to keep its exchanges off any third-party network.\n\nIf you also need to connect OpenAI or Anthropic alongside your local models, move on to the next question — LibreChat solves this without abandoning Ollama.",{"type":54,"title":55,"steps":56},"steps","Open WebUI in three typical use cases",[57,60,63],{"title":58,"body":59},"Internal AI assistant for a team","You deploy Open WebUI on a VPS, connect Ollama with two or three models, and share access with your team. Each member has their own persistent conversation history. No data leaves your server — conversations, documents loaded into RAG, and histories all remain on your infrastructure.",{"title":61,"body":62},"Queryable knowledge base in natural language","You load your procedures, technical documentation and PDFs into the built-in RAG. Team members ask questions in natural language without prior training. Open WebUI retrieves the relevant passages and formulates a contextualized answer, with cited sources for verification.",{"title":64,"body":65},"Test bench for new models before integration","Before integrating a model into an application, you test it under real conditions on Open WebUI. You compare multiple models side by side from the same interface, measure their responses against your real data, and choose the one matching your latency and quality constraints — without touching your production code.",{"type":36,"title":67,"body":68},"Question 2: do you need multiple AI providers and MCP agents?","If your team uses OpenAI for some tasks, Anthropic for others, and local models via Ollama — all from a single interface with a shared history — **LibreChat is the right tool**.\n\nIts distinctive strength is native support for the Model Context Protocol (MCP): you connect MCP servers (databases, APIs, business tools) to your agents, which can then read data, trigger actions, and compose responses from heterogeneous sources. This capability makes LibreChat relevant for teams building AI agents connected to their existing information systems — CRM, codebase, internal documentation — without having to develop a custom integration for each source.\n\nLibreChat also handles conversation presets (configurations saved per model and per use case), history search, code artifacts with execution, and a team management system with differentiated access.\n\nRecommended requirements: 2 vCPU and 2 GB RAM minimum. This is the most resource-efficient configuration of the three tools compared here — LibreChat delegates inference to external providers or to Ollama and does not include its own inference engine.",{"type":36,"title":70,"body":71},"Question 3: do you want to build AI applications without coding?","If your goal is to produce AI applications or automated workflows — a customer chatbot with RAG on your catalogue, a ticket classification pipeline, a document summarization agent, a code review assistant — without writing code, **Dify is the answer**.\n\nDify offers a visual builder: you assemble nodes (LLM, RAG, conditional logic, API calls, data transformations) on a canvas, configure the parameters of each node, and Dify automatically exposes your workflow via a REST API and a web interface. Multiple applications coexist in the same workspace and share the same knowledge bases and model connections.\n\nDify suits product teams, agencies and integrators who want to build AI use cases with fast iteration, without depending on a backend developer for each new workflow. The automatically exposed API then allows Dify applications to be integrated into third-party systems without rewriting.\n\nThis power comes at a resource cost: plan for 4 vCPU and 8 GB RAM minimum for the full Docker stack, which includes the Python backend, a vector database, Redis and PostgreSQL. In return, Dify's Cloud Team plan is a paid service. Deploying Dify on your own VPS eliminates this recurring cost entirely.",{"type":73,"title":74,"headers":75,"rows":80},"comparison","Open WebUI, LibreChat, Dify comparison",[76,77,78,79],"Criterion","Open WebUI","LibreChat","Dify",[81,86,91,96,100,105,110,115,120,123],[82,83,84,85],"Main use","Chat with local models and RAG","Multi-provider chat and MCP agents","AI app and workflow builder without code",[87,88,89,90],"License","Open WebUI License (BSD 3-clause)","MIT","Dify Open Source (Apache 2.0 +)",[92,93,94,95],"Recommended RAM (no GPU)","4 GB minimum","2 GB minimum","8 GB minimum",[97,98,98,99],"Recommended vCPU","2 vCPU","4 vCPU",[101,102,103,104],"Ollama support (local models)","Native — built around Ollama","Yes, among multiple providers","Yes, as LLM source in workflows",[106,107,108,109],"OpenAI \u002F Anthropic support","Yes (compatible API)","Yes — unified multi-provider","Yes — configurable per workflow",[111,112,113,114],"Built-in RAG","Yes — native","No (configurable via MCP)","Yes — native, via knowledge base",[116,117,118,119],"Native MCP agents","No","Yes — official support","Via plugins and integrations",[121,117,117,122],"Visual no-code builder","Yes — workflow canvas",[124,125,125,126],"Equivalent Cloud cost","N\u002FA","paid Cloud plan (Team)",{"type":36,"title":128,"body":129},"Choose in three questions: the decision framework","The decision takes less than five minutes with this framework.\n\n**Q1 — Do you need exclusively local models (Ollama) and total data privacy?**\nYes → **Open WebUI**. Deploy it alongside Ollama on a VPS with at least 4 GB RAM. For a 7B model in 4-bit quantization (Llama 3.1 7B, Mistral 7B), plan for 8 GB RAM — the model loads entirely into memory.\n\n**Q2 — Do you need to connect multiple AI providers (OpenAI, Anthropic, Ollama…) from a single interface, or to build MCP agents?**\nYes → **LibreChat**. It is the most flexible and least resource-intensive configuration. Two vCPU and 2 GB RAM are enough to get started.\n\n**Q3 — Do you want to build AI applications or workflows without writing code, and automatically expose an API?**\nYes → **Dify**. Plan for 4 vCPU and 8 GB RAM for the full Docker stack. The gain is the elimination of the monthly Cloud subscription cost.\n\nIf you answer \"yes\" to multiple questions, start with the profile closest to your day-to-day use. Deployment guides for each tool are available on the blog, and all three templates are accessible directly from the Marketplace.",{"type":131,"title":132,"body":133},"tip","The recommended starting point if you are still unsure","Open WebUI is the fastest entry point: the ServOrbit template includes Ollama, network configuration, and a reverse proxy. In one command, you have an operational chat interface on your domain, with a valid TLS certificate. You can connect OpenAI via the compatible API if you want to test an external provider without migrating to LibreChat. If your use case evolves toward MCP agents or no-code workflows, migrating to LibreChat or Dify on a second VPS does not require reinstalling Open WebUI — the two can coexist.",{"type":36,"title":135,"body":136},"Detailed requirements depending on your choice","Here are the recommended VPS configurations for each tool in production conditions, without a GPU.\n\n**Open WebUI + Ollama** — 2 vCPU \u002F 4 GB RAM without a local model loaded. With a 7B model in 4-bit quantization (Llama 3.1 7B, Mistral 7B), plan for at least 8 GB RAM — the model loads entirely into memory. A 13B model requires 16 GB. A dedicated GPU reduces inference latency but is not required to get started. The interface alone consumes around 512 MB RAM — the rest serves Ollama and loaded models.\n\n**LibreChat** — 2 vCPU \u002F 2 GB RAM. The Docker stack is lightweight: LibreChat delegates inference to external providers or to Ollama, running on the same server or another. MongoDB handles conversation persistence. Configuration is done via the `librechat.yaml` file, where you declare the providers and MCP servers to activate.\n\n**Dify** — 4 vCPU \u002F 8 GB RAM minimum. The Docker stack includes the interface, the Python backend (`api` and `worker`), the vector database (Weaviate or Qdrant depending on configuration), Redis for task queues, PostgreSQL for persistence and an internal `nginx`. Under-provisioning Dify causes slowdowns during RAG document indexing and in workflows with multiple LLM nodes in sequence. In production with multiple teams, 8 vCPU and 16 GB RAM provide a comfortable margin.",{"type":36,"title":138,"body":139},"What each tool does not do","Knowing the limits avoids surprises at deployment time.\n\n**Open WebUI** does not natively manage multiple AI providers in a routing logic: you can connect an OpenAI-compatible API, but there is no automatic provider selection based on the task. Managing complex agents with external tools depends on the local model's capabilities, not a dedicated orchestration layer.\n\n**LibreChat** is not an application builder: it does not expose an API to integrate its conversations into a third-party system, and it does not allow assembling multi-step workflows without programming. If you want to produce a standalone interface for your end clients, Dify is more appropriate.\n\n**Dify** is not a universal chat interface: its canvas is powerful but requires a learning curve. For solo use discussing with local models, Open WebUI is more direct. Dify shines when multiple workflows must coexist, share knowledge bases, and be consumed by third-party systems via API.","Deploy your AI interface on a VPS","Open WebUI is available in the ServOrbit Marketplace with Ollama pre-configured. Deploy it on your Cloud VPS in minutes and keep full control over your conversations, documents and models.","View the Open WebUI template","\u002Fmarketplace\u002Fartificial-intelligence\u002Fopen-webui",[145,160,177],{"id":146,"slug":147,"slugs":148,"title":152,"excerpt":153,"readTime":154,"views":16,"isPinned":17,"publishedAt":155,"category":156,"categories":157,"featuredImage":27,"bgImage":28,"posterImage":159,"relatedSolution":27},316,"open-webui-ollama-on-vps-multi-user-interface-for-local-llms",{"fr":149,"en":147,"ar":150,"es":151},"open-webui-ollama-vps","واجهة-ويب-open-webui-مع-ollama-على-vps-للفرق","open-webui-ollama-en-vps-interfaz-web-multiusuario-para-llm","Open-WebUI + Ollama on VPS: multi-user interface for local LLMs","Give your entire team a multi-user web interface for your Ollama models: RAG, model management, SSO via OpenID Connect — no cloud dependency.",9,"2026-08-30T00:00:00+00:00",{"id":20,"name":21,"slug":22,"color":23,"icon":24},[158],{"id":20,"name":21,"slug":22,"color":23,"icon":24},"\u002Fblog\u002Fcovers\u002Fopen-webui-ollama-vps-poster.svg",{"id":161,"slug":162,"slugs":163,"title":167,"excerpt":168,"readTime":169,"views":16,"isPinned":17,"publishedAt":170,"category":171,"categories":172,"featuredImage":27,"bgImage":28,"posterImage":174,"relatedSolution":175},2,"deploying-dify-on-a-vps-ai-workflows-under-control",{"fr":164,"en":162,"ar":165,"es":166},"deployer-dify-vps","نشر-dify-على-vps-سير-عمل-الذكاء-الاصطناعي-تحت-السيطرة","desplegar-dify-en-un-vps","Deploy Dify on a VPS: Complete Guide from Installation to Plugins","Install Dify on a ServOrbit VPS: hardware requirements, Docker Compose setup, offline plugins, reverse proxy and troubleshooting.",8,"2026-02-11T00:00:00+00:00",{"id":20,"name":21,"slug":22,"color":23,"icon":24},[173],{"id":20,"name":21,"slug":22,"color":23,"icon":24},"\u002Fblog\u002Fcovers\u002Fdeployer-dify-vps-poster.svg",{"categorySlug":22,"appSlug":176},"dify",{"id":178,"slug":179,"slugs":180,"title":184,"excerpt":185,"readTime":186,"views":16,"isPinned":17,"publishedAt":187,"category":188,"categories":189,"featuredImage":27,"bgImage":28,"posterImage":191,"relatedSolution":192},13,"how-to-host-librechat-on-a-vps",{"fr":181,"en":179,"ar":182,"es":183},"heberger-librechat-vps","كيفية-استضافة-librechat-على-خادم-vps","como-alojar-librechat-en-un-vps","How to Host LibreChat on a VPS","Host LibreChat on your VPS: a private, multi-model ChatGPT interface. Docker, MongoDB, RAG, reverse proxy and SSL step by step.",3,"2026-06-07T00:00:00+00:00",{"id":20,"name":21,"slug":22,"color":23,"icon":24},[190],{"id":20,"name":21,"slug":22,"color":23,"icon":24},"\u002Fblog\u002Fcovers\u002Fheberger-librechat-vps-poster.svg",{"categorySlug":31,"appSlug":193},"librechat",1788538489852]