Self-hosted AI search promises control, citations, and privacy, but only if you choose the right architecture. After deploying Perplexica with SearXNG, Redis, and local Ollama models, I learned where the orchestration layer delivers value and where a lean SearXNG deployment is the smarter bet. The debate is not academic: the wrong choice bloats infrastructure, slows queries, and breaks the citation trail you need for trustworthy answers. For a deeper look at local runtime choices, revisit Ollama vs LocalAI Which Local Model Server Should You Choose? or the broader How to Run AI Models Locally Without Expensive Hardware guide.

Architecture Depth vs Minimal Footprint

Perplexica bundles a frontend, backend orchestrator, Redis cache, and a local LLM. It routes every query through SearXNG, aggregates sources, and asks your model to produce cited summaries. The result feels like an AI-native search experience with controllable prompts and multimodal routing.

SearXNG by itself is a fast, privacy-minded meta-search engine. It aggregates results from Google, Bing, DuckDuckGo, and dozens of niche providers without touching an LLM. You receive clean JSON or HTML outputs that you can feed into your own tooling if needed.

The trade-off is obvious: Perplexica’s additional services create an intelligent layer on top of SearXNG, while SearXNG alone remains a lightweight backend you can deploy in under five minutes.

Setup Experience and Operational Load

Standing up Perplexica demands more deliberate configuration. You clone the repo, adjust config.yaml to point at SearXNG, select Ollama models like Phi-4, and tune prompt templates so responses remain grounded. Docker Compose spins up multiple services, and you monitor logs to ensure each container authenticates correctly.

Running SearXNG alone is closer to flipping a switch. Pull the official image, expose port 4000, and edit the .yaml engine list if you want to disable certain providers. There is no Redis to manage, and you can run it comfortably on low-resource hardware or a home lab VM.

If you want an AI assistant with citations on day one, accept Perplexica’s heavier footprint. If you simply need a privacy-respecting meta-search, SearXNG alone keeps operations painless.

Answer Quality and Citation Integrity

Perplexica shines when you need synthesized answers with traceable references. Larger Ollama models, such as Phi-4, deliver paragraph summaries annotated with footnotes that link back to the exact source. The built-in UI even handles image agents, pulling Bing or Google visuals alongside textual results.

SearXNG alone returns raw search results. You can still guarantee privacy because queries route through your server, but there is no synthesis or citation overlay unless you build it yourself. This is perfect when your downstream workflow already handles summarization or when you want to retain manual control over result interpretation.

Choose Perplexica when stakeholders expect ready-to-use answers with citations. Stick to SearXNG when you prefer the raw feeds and plan to craft your own post-processing pipeline. If you intend to build a full retrieval stack on top of either option, study the blueprint in Implement RAG Systems Tutorial Complete Guide.

Resource Management and Model Selection

Perplexica’s intelligence depends on your local LLM. Running Phi-4 through Ollama provides far better grounding than smaller 7B models, but it also increases VRAM and download requirements. You must balance latency, accuracy, and hardware availability. Redis and the frontend add further memory overhead.

SearXNG has modest requirements. Because it proxies API calls, CPU usage remains low, and you can comfortably deploy it alongside other services. It is ideal for edge devices, low-power servers, or situations where you do not want to allocate GPUs to search.

Treat Perplexica as an investment in richer answers; treat SearXNG as the dependable building block for any search or retrieval workflow.

When to Choose Each Stack

• Deploy Perplexica when: you want AI-written summaries with enforceable citations, you already run Ollama models locally, or your team needs multimodal answers from a single interface.
• Deploy SearXNG alone when: you prioritize minimal infrastructure, you are feeding search results into a separate retrieval pipeline, or latency and resource usage trump synthesized prose.
• Combine both when: you start with SearXNG as the backend and layer Perplexica for power users who need AI assistance on top of the same search corpus.

The right choice depends on the level of orchestration you can maintain and the quality of answers your audience expects.

See the full Perplexica build, including the SearXNG configuration and Ollama Phi-4 integration, in the detailed walkthrough on YouTube: https://www.youtube.com/watch?v=QghWYA5hg2M. Want practical feedback on deploying self-hosted search? Join the AI Engineering community where experienced Senior AI Engineers share architectures, prompt templates, and debugging tactics for production-ready stacks.