Open WebUI, LibreChat, Dify: which one to choose on a VPS?

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.

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.

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.

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.

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.

Deployed 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.

Open 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.

If you also need to connect OpenAI or Anthropic alongside your local models, move on to the next question — LibreChat solves this without abandoning Ollama.

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.

Its 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.

LibreChat 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.

Recommended 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.

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.

Dify 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.

Dify 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.

This 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.

The decision takes less than five minutes with this framework.

Q1 — Do you need exclusively local models (Ollama) and total data privacy?
Yes → 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.

Q2 — Do you need to connect multiple AI providers (OpenAI, Anthropic, Ollama…) from a single interface, or to build MCP agents?
Yes → LibreChat. It is the most flexible and least resource-intensive configuration. Two vCPU and 2 GB RAM are enough to get started.

Q3 — Do you want to build AI applications or workflows without writing code, and automatically expose an API?
Yes → 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.

If 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.

Here are the recommended VPS configurations for each tool in production conditions, without a GPU.

Open WebUI + Ollama — 2 vCPU / 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.

LibreChat — 2 vCPU / 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.

Dify — 4 vCPU / 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.

Knowing the limits avoids surprises at deployment time.

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.

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.

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.