Perplexica is a privacy-focused AI answering engine that runs entirely on your own hardware, delivering the power of AI-powered search without compromising your personal data. This open source alternative to Perplexity AI combines real-time web search capabilities with advanced language models to provide accurate, cited answers while keeping your search history completely private.
Unlike traditional search engines that track your queries and build profiles, Perplexica ensures complete search privacy through local-first architecture. Whether you're researching academic papers, troubleshooting code, or exploring new topics, this self-hosted search solution puts you in control.
Modern AI search platforms offer impressive capabilities, but they come with significant privacy trade-offs. Perplexity AI and similar cloud-based services analyze your search patterns, store your queries, and potentially share data with third parties. Perplexica takes a fundamentally different approach.
Your data never leaves your machine. Perplexica operates as a self-hosted AI search platform, processing queries locally and maintaining your search history on your own hardware. This privacy-preserving search methodology ensures that your intellectual curiosity, research topics, and professional inquiries remain confidential.
Choose the LLM that best fits your needs:
This flexibility allows you to balance performance, cost, and privacy based on each query's requirements.
Perplexica includes six purpose-built search modes that optimize results for specific content types:
| Focus Mode | Best For | Key Benefit |
|---|---|---|
| Academic | Research papers, citations, scholarly articles | Access peer-reviewed sources |
| YouTube | Video tutorials, demonstrations, courses | Find timestamp-specific content |
| Community discussions, user experiences | Real-world insights and opinions | |
| Wolfram Alpha | Mathematical calculations, data analysis | Computational knowledge engine |
| Writing | Content creation, editing, research | Citation-rich responses |
| General Web | Broad searches, current events | Comprehensive web coverage |
Balanced Mode delivers optimal results for everyday queries, combining speed with accuracy. Fast Mode prioritizes quick responses when you need immediate answers. The upcoming Quality Mode will provide deep research capabilities for complex investigations.
Built on modern web technologies, Perplexica leverages:
This conversational search interface understands context from your chat history, refining queries intelligently and providing progressively better results as conversations evolve.
This architecture ensures you receive accurate answers with source citations while maintaining complete privacy throughout the pipeline.
The fastest way to get started uses Docker for containerized deployment:
docker run -d -p 3000:3000 \
-v perplexica-data:/home/perplexica/data \
-v perplexica-uploads:/home/perplexica/uploads \
--name perplexica \
itzcrazykns1337/perplexica:latest
This single command pulls the latest image, configures persistent storage, and starts Perplexica with the bundled SearxNG search engine.
For even simpler setup, Perplexica supports instant deployment through:
For developers who prefer full control:
System requirements: Modern CPU, 4GB+ RAM, and storage for search history and uploads.
The open source community has embraced Perplexica with impressive engagement:
This level of adoption validates Perplexica as a production-ready alternative to commercial AI search platforms.
Developers use Perplexica to research API documentation, troubleshoot errors, and explore new frameworks without exposing proprietary project details. The YouTube focus mode excels at finding tutorial videos with specific code examples.
Students and researchers leverage the Academic focus mode to discover peer-reviewed papers, track citations, and explore scholarly literature while maintaining research confidentiality.
Journalists, lawyers, healthcare professionals, and anyone handling sensitive information benefit from Perplexica's guarantee that search queries never leave their infrastructure.
Machine learning practitioners compare different LLM providers for search applications, testing local models through Ollama against cloud-based alternatives to optimize the LLM integration strategy.
Traditional search engines like Google return lists of links, requiring you to visit multiple websites to synthesize information. Perplexica provides direct answers with citations, saving time and reducing context switching.
While cloud-based AI search platforms offer convenience, they collect extensive data about your queries, interests, and behavior. Perplexica delivers equivalent capabilities while ensuring zero data collection or tracking.
ChatGPT excels at reasoning and generation but lacks real-time web access in most configurations. Perplexica specializes in current information retrieval, always pulling the latest data from the web.
After installation, configure your preferred AI provider in the settings panel. If you're using Ollama for local inference, ensure your models are downloaded. For cloud providers, add your API keys.
The interface presents a clean chat window. Type your question naturally - Perplexica understands conversational queries:
Select the appropriate focus mode before searching, or stick with General Web for broad queries. Results appear with source citations you can click to verify information.
Local LLM inference requires significant computational resources. An Ollama setup with a 7B parameter model needs at least 8GB RAM. Larger models (13B, 70B) demand proportionally more resources. Cloud API usage eliminates these requirements but introduces usage costs.
The SearxNG configuration significantly impacts result quality. Adding more search engines improves coverage but increases response time. Experiment with the balance that works for your use case.
Perplexica stores search history locally in a SQLite database. Regular backups ensure you don't lose valuable conversation history. The persistent volume mounts in the Docker command handle this automatically.
Beyond web search, Perplexica can analyze:
Limit searches to particular websites or domains when you need information from trusted sources. This feature proves invaluable for technical documentation searches or academic journal access.
As you search, Perplexica learns to provide intelligent query refinements and related topic suggestions, helping you explore tangential areas you might not have considered.
Perplexica represents a growing movement toward privacy-preserving AI tools that deliver cutting-edge capabilities without surveillance. As Large Language Models continue advancing and local inference becomes more accessible, self-hosted solutions like Perplexica will become increasingly viable for mainstream users.
The project's active development and strong community support suggest a bright future. Upcoming features like Quality Mode for deep research and potential integrations with additional AI providers will further enhance capabilities.
Every search you perform stays on your hardware. The SearxNG integration ensures that web searches appear to originate from the SearxNG instance rather than your IP directly. No telemetry, analytics, or usage tracking exists in the codebase - you can verify this yourself through the open source repository.
For organizations handling sensitive information, Perplexica offers compliance-friendly search that doesn't expose queries to third parties. Deploy it on air-gapped networks for maximum security.
The MIT license encourages contributions and customization. Fork the repository to add new focus modes, integrate additional AI providers, or optimize the search pipeline. The TypeScript codebase maintains high code quality standards, making contributions straightforward for experienced developers.
Share your implementations with the community through the Discord channel or GitHub discussions. Whether you've optimized performance, added features, or solved deployment challenges, your insights help the entire ecosystem.
Perplexica proves that AI-powered search doesn't require surrendering your privacy. By combining open source principles with modern AI capabilities, it delivers a self-hosted search solution that respects user autonomy while providing powerful features.
Whether you're a privacy-conscious individual, a development team needing confidential research capabilities, or an organization with strict data governance requirements, Perplexica offers a compelling alternative to cloud-based AI search platforms. The 27,000+ stars on GitHub validate its technical merit and growing adoption.
Deploy Perplexica today and experience the future of private, intelligent search.
Platform
GitHub
Language
TypeScript
License
MIT
Stars
10k+
Perplexica is an open source AI-powered answering engine that runs entirely on your own hardware. It combines real-time web search with language models to provide accurate, cited answers while keeping your searches completely private. Unlike cloud-based alternatives, it supports both local LLMs through Ollama and cloud providers like OpenAI, Claude, and Gemini.
While Perplexity AI operates as a cloud service that processes your queries on their servers, Perplexica is self-hosted and runs on your own hardware. This means your search history never leaves your machine, providing complete privacy. Perplexica also offers more flexibility by supporting multiple AI providers including local models through Ollama.
Perplexica can run with modest requirements when using cloud AI providers - just a modern CPU and 4GB RAM. For local LLM inference with Ollama, you need at least 8GB RAM for 7B parameter models, with larger models requiring proportionally more memory. The Docker installation is recommended for easiest setup.
Perplexica offers six focus modes optimized for specific content: Academic for research papers, YouTube for video tutorials, Reddit for community discussions, Wolfram Alpha for mathematical calculations, Writing for content creation with citations, and General Web for broad searches. Each mode tailors the search and response generation for that content type.
Yes, Perplexica is completely free and open source under the MIT license. However, if you use cloud AI providers like OpenAI or Claude, you will incur API costs from those services. Using local models through Ollama eliminates all ongoing costs after initial setup.
The easiest method is Docker: run the command docker run -d -p 3000:3000 -v perplexica-data:/home/perplexica/data -v perplexica-uploads:/home/perplexica/uploads --name perplexica itzcrazykns1337/perplexica:latest. Alternatively, use one-click deployment platforms like Sealos, RepoCloud, or Hostinger, or manually install with Node.js and SearxNG.
Yes, when configured with Ollama and local language models, Perplexica can operate completely offline. You need to set up a local SearxNG instance and download your preferred LLM models. This provides full functionality without any internet dependency, though results are limited to locally cached data.
SearxNG is a privacy-respecting metasearch engine that queries multiple search engines simultaneously without tracking users. Perplexica uses it to fetch real-time web results while preserving anonymity. Your searches appear to originate from the SearxNG instance rather than your IP directly, adding an extra privacy layer.
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