5 Self-Hosted ChatGPT Alternatives — Run LLMs on Your Own Server (2026)
ChatGPT Team costs $25 per user per month. At 10 users that is $3,000 per year — before you factor in rate limits, data residency concerns, and the fact that every prompt you send trains future OpenAI models unless you opt out. Self-hosting an LLM stack eliminates per-seat costs, keeps prompts on hardware you control, and lets you run models tuned for your domain.
The five options below range from a single-command model runner to a full LLM application platform. Each one eliminates the per-user ChatGPT fee. The right choice depends on whether you need a chat interface, an API proxy, or a workflow builder.
1. Ollama + Open WebUI
The easiest production-ready combination: Ollama serves the model via an OpenAI-compatible API, and Open WebUI wraps it in a polished ChatGPT-style interface.
Ollama
- Stars: ~140k
- License: MIT
- What it does: Downloads and serves models (Llama 4, Llama 3, Mistral, Gemma 3, Qwen, and 100+ more) via a local REST API. One command:
ollama run llama3. - Hardware floor: 4 GB RAM VPS for 7B models (quantized); L40S GPU server for 70B models at production throughput
- Setup guide: Self-Host Ollama
Open WebUI
- Stars: ~135k
- License: BSD-3-Clause (custom branding-preserving terms — read before white-labeling)
- What it does: ChatGPT-style interface for Ollama and OpenAI-compatible APIs. Supports multi-user auth, conversation history, RAG document uploads, image generation, and plugin tools.
- Minimum VPS: 2 GB RAM (UI is lightweight; the model runtime is the bottleneck)
- Setup guide: Self-Host Open WebUI
Why pair them: Ollama handles model management — pull, update, and swap models without touching the UI. Open WebUI handles the user experience — auth, history, and sharing. Together they replicate what ChatGPT Team offers at zero per-seat cost once your hardware is provisioned.
Cost comparison: ChatGPT Team at 10 users = $3,000/year. A Liquid Web 4 GB Managed VPS running Ollama + Open WebUI with a 13B model = ~$480/year. For heavier inference, a Liquid Web L40S GPU server runs 70B models at production throughput.
2. LiteLLM
LiteLLM (~40k stars, MIT) is an OpenAI-compatible proxy that routes requests to 100+ model providers — Ollama, OpenAI, Anthropic, Groq, AWS Bedrock, Azure, and more — from a single endpoint. It is not a chat interface; it is the routing and governance layer underneath one.
- License: MIT
- Stack: Python (FastAPI) + PostgreSQL (optional, for usage tracking)
- Idle RAM: ~300 MB (standalone proxy mode)
- What it solves: You want to give your team OpenAI-compatible API keys with per-key budgets, fallback routing (e.g., "use Ollama first, fall back to GPT-4o if it fails"), and a usage dashboard — without exposing your actual provider keys.
- Setup guide: Self-Host LiteLLM
When to choose LiteLLM: Your team already uses OpenAI or Claude and you want to add Ollama as a local fallback, apply per-project spend caps, or centralize API key management. It pairs naturally with Open WebUI (point Open WebUI at your LiteLLM endpoint instead of Ollama directly).
3. Dify
Dify (~100k stars, Apache-2.0) is a full LLM application platform: chatbots, RAG pipelines, agents, and visual workflow automation. It goes beyond being a ChatGPT replacement — it is closer to a self-hosted version of the OpenAI Assistants API plus a visual builder.
- License: Apache-2.0
- Stack: Python + Node.js + PostgreSQL + Redis + Weaviate (optional vector store)
- Idle RAM: ~2–3 GB
- Minimum VPS: 8 GB RAM (16 GB for production with vector search)
- Setup guide: Self-Host Dify
What you get that ChatGPT Team doesn't offer: Visual workflow editor, multi-model routing from a single workspace, native RAG over your own documents (PDF, Notion, websites), and an API for embedding chatbots into your own products.
When to choose Dify: You want to build customer-facing chatbots, internal knowledge assistants, or multi-step AI workflows — not just give your team a ChatGPT replacement. Dify is overkill if all you need is a private chat interface.
4. LocalAI
LocalAI (~28k stars, MIT) is a drop-in OpenAI API replacement that runs entirely on your hardware. It supports a wider range of model formats than Ollama — GGUF, GPTQ, ONNX — and handles not just text generation but also speech-to-text (Whisper), image generation (Stable Diffusion), and embeddings from a single API surface.
- License: MIT
- Stack: Go binary + model files
- Idle RAM: ~150 MB (the binary itself; models load into memory as needed)
- Minimum VPS: 4 GB RAM for 7B text models; GPU recommended for image generation
- What it solves: You need an OpenAI-compatible API that supports multimodal workloads (text + audio + images) from a single endpoint, and you want maximum model format compatibility.
When to choose LocalAI over Ollama: You need Whisper transcription or Stable Diffusion image generation alongside text inference, or you need to load GGUF models that Ollama does not yet support. If you only need text models, Ollama's tooling and model library are more polished.
5. LibreChat
LibreChat (~21k stars, MIT) is a ChatGPT UI clone that connects to multiple backends simultaneously: OpenAI, Anthropic, Google Gemini, Ollama, LocalAI, and custom OpenAI-compatible endpoints. It supports plugins, function calling, image generation, and multi-user authentication.
- License: MIT
- Stack: Node.js + MongoDB + Redis
- Idle RAM: ~600 MB
- Minimum VPS: 4 GB RAM
- What it solves: You want a ChatGPT-identical interface that lets users switch between GPT-4o, Claude 4 Sonnet, and your local Ollama instance from a single conversation UI — with full conversation history and team auth.
When to choose LibreChat: You want the most feature-complete ChatGPT UI clone with multi-provider support, and you don't want to build your own auth system. LibreChat handles user signup, admin panels, and usage controls out of the box.
Which one fits your situation?
| Situation | Recommended stack |
|---|---|
| Just want a private ChatGPT for your team | Ollama + Open WebUI on a 4 GB VPS or GPU server |
| Need per-user API keys and budget limits | LiteLLM (proxy layer) + Open WebUI or LibreChat |
| Building chatbots or RAG apps for customers | Dify |
| Need multimodal API (text + audio + images) | LocalAI |
| Want multi-provider UI (GPT-4o + Claude + Ollama) | LibreChat |
| Running Llama 3 70B at production throughput | Liquid Web L40S GPU server + Ollama |
All five tools are free and open-source. The only cost is the server — a Liquid Web 4 GB Managed VPS for small models, or a GPU server for 70B+ inference at team scale.
Not at useful throughput. Llama 3 70B at Q4 quantization requires about 40 GB of VRAM or RAM. A CPU-only VPS with 64 GB RAM can technically run it, but inference is 5–10 tokens per second — too slow for interactive use. A Liquid Web L40S GPU server (48 GB VRAM) runs 70B models at 30–50 tokens per second, which is comfortable for team use. For a VPS, stick to 7B or 13B models.
For small models (7B or 13B quantized), a 4 GB Liquid Web Managed VPS runs Ollama at acceptable speed for light team use. For production inference with 30B+ models, or if more than 3–4 users are hitting the endpoint simultaneously, a GPU server is the right tier. LiteLLM is an option if you want to route overflow requests to OpenAI or Groq automatically.
Open WebUI uses a custom license derived from BSD-3-Clause that adds branding restrictions: you cannot remove the 'Open WebUI' name or logo from the UI when distributing it as a product to third parties. For internal team use, there are no practical restrictions. If you plan to white-label it as a product, review the license terms at github.com/open-webui/open-webui/blob/main/LICENSE before deploying.