ChatGPT
6 alternatives — 3 easy, 3 medium
Why people leave ChatGPT
- Conversations stored on OpenAI servers
- Subscription cost for GPT-4 access
- Data may be used for model training
- No control over model versions or availability
Comparison
| App | Difficulty | RAM | Docker | Mobile | Status | |
|---|---|---|---|---|---|---|
| AnythingLLM Self-hosted AI workspace for document chat, agents, connectors, and local-first knowledge bases. | easy | 2GB | — | Active | ||
| Dify Self-hosted platform for building AI agents, RAG apps, workflows, and LLM-powered products. | medium | 4GB | — | Active | ||
| Flowise Visual builder for AI agents, chains, and LLM workflows. | medium | 2GB | — | Active | ||
| LocalAI OpenAI-compatible local inference server for LLMs, images, audio, and multimodal models. | medium | 4GB | — | Active | ||
| Ollama Local model runtime for running open LLMs behind a simple API. | easy | 4GB | — | — | Active | |
| Open WebUI Self-hosted AI workspace for local models, OpenAI-compatible APIs, RAG, tools, and multi-user chat. | easy | 1GB | — | — | Active |
Detailed Look
AnythingLLM Top Pick
Self-hosted AI workspace for document chat, agents, connectors, and local-first knowledge bases.
Pros
- + Friendly all-in-one workspace for teams and individuals
- + Supports many model providers and local models
- + Document chat and agent features are built in
Cons
- - Less flexible than developer-first LLM platforms
- - Advanced integrations can require paid/hosted features
- - Resource use grows with document volume and model choice
Dify
Self-hosted platform for building AI agents, RAG apps, workflows, and LLM-powered products.
Pros
- + Visual workflow builder for AI apps and agents
- + Supports RAG, datasets, tools, and multiple model providers
- + Useful bridge between prototypes and production AI apps
Cons
- - More complex than a simple chat UI
- - Several backing services to operate
- - License and edition boundaries should be reviewed for commercial use
Flowise
Visual builder for AI agents, chains, and LLM workflows.
Pros
- + Visual workflow canvas for LLM apps and agents
- + Good for prototyping multi-step AI automations
- + Supports many providers and vector stores
Cons
- - Production deployments need careful secrets and scaling setup
- - Visual flows can become hard to maintain
- - Some advanced functionality depends on fast-moving AI integrations
LocalAI
OpenAI-compatible local inference server for LLMs, images, audio, and multimodal models.
Pros
- + OpenAI-compatible API for many local model types
- + Can run without a GPU for smaller models
- + Covers text, image, voice, and multimodal use cases
Cons
- - Model setup is more technical than Ollama
- - Performance varies heavily by backend and hardware
- - Not a polished end-user chat app by itself
Ollama
Local model runtime for running open LLMs behind a simple API.
Pros
- + Simple way to run local models on laptops and servers
- + Works with Open WebUI and many OpenAI-compatible tools
- + Large model library and active ecosystem
Cons
- - Model quality and speed depend heavily on hardware
- - Large models need significant RAM or VRAM
- - Not a full chat application by itself
Open WebUI
Self-hosted AI workspace for local models, OpenAI-compatible APIs, RAG, tools, and multi-user chat.
Pros
- + Polished ChatGPT-like UI that works out of the box
- + Supports Ollama, OpenAI-compatible APIs, RAG, tools, and functions
- + Multi-user with role-based access control
- + Active development with huge community (130k+ GitHub stars)
Cons
- - RAM usage depends entirely on the LLM backend — local models need significant resources
- - License changed from BSD-3 to a custom license requiring branding preservation in v0.6.6+
- - Advanced tool and RAG setup can get complex
- - No native mobile app
Can't decide? Compare AnythingLLM, Dify, Flowise side by side →