Q: What are the best self-hosted Perplexity alternatives I can run locally? A: The article highlights Vane, Morphic, Scira, and Verity as self-hosted Perplexity alternatives you can run locally. These tools let you perform real-time searches, get citations, summarize documents, and keep queries private while working offline. Q: What hardware and software do I need to set up these local search tools? A: You should have a recent CPU (a modest GPU helps) and about 16GB of RAM, and install Docker and Ollama to run local models. Some apps bundle SearXNG while others ask you to install it, and you should keep everything on your LAN and avoid exposing ports for privacy. Q: How does Vane work and what features does it offer compared to Perplexity? A: Vane runs on your hardware via Docker with toggles for Web, Academic, and Social searches, and it bundles SearXNG so web search works out of the box. It uses Ollama with models like Gemma 4 to provide real-time web checks, social sentiment, citations, and summaries while keeping queries local. Q: Is Morphic harder to set up and what can it do once installed? A: Morphic is a flexible open source tool that’s slightly more complicated to self-host than Vane, and the GitHub guide helps but you may encounter errors during setup. Once running it supports both cloud LLMs and local models via Ollama, includes SearXNG, and can do real-time searches, draft emails, summarize uploaded files, and cite sources. Q: What makes Scira a unique self-hosted Perplexity alternative? A: Scira is the easiest to install and offers 17 search modes for YouTube, Spotify, GitHub, Reddit, X, and more, along with a user-friendly interface. It relies on Exa AI for web search rather than SearXNG, and supports PDF uploads, URL extraction, running Python, Supermemory, and several LLMs like Qwen, DeepSeek, and Mistral. Q: Do I need to install SearXNG for all of these tools? A: No — Vane and Morphic bundle SearXNG within their Docker setups while Scira uses Exa AI for web search. Verity requires you to install SearXNG separately before pointing Verity at a local endpoint. Q: Which LLMs or models are recommended for general use and research with these local tools? A: For balanced general Q&A and summaries the guide recommends pulling Gemma 4 into Ollama, while Qwen, DeepSeek, and Mistral families are suggested for coding and reasoning tasks. Verity ships configured with Jan-nano 4B for research workflows but you can point any supported model via Ollama, llama.cpp, or an OpenAI-compatible server. Q: What privacy and performance tips help keep local searches private and fast? A: To keep searches private run everything behind your router, avoid forwarding ports, and use HTTPS on your LAN if you share across devices. Prefer SearXNG for transparent local search aggregation and cache models in Ollama rather than repeatedly pulling weights, and enable GPU support in Docker and limit sources per query to improve speed.