SearchQ vs Perplexity: Private AI Answers Compared

Both ground answers in live web search with inline citations, and Perplexity is the stronger pure answer engine for cited research. SearchQ trades a little of that polish for privacy: a three-level dial from cloud to an encrypted enclave to a free, in-browser Local mode, plus auto-routing across many models.

What are SearchQ and Perplexity?

Perplexity is an answer engine: it runs a live web search and replies with a synthesized answer plus numbered source citations. SearchQ is a privacy-focused, multi-model chat app that also searches the web, but its defining feature is a privacy dial spanning cloud, an encrypted enclave, and fully in-browser inference, with answers routed across many models.

Perplexity has spent years polishing the cited-search workflow and surrounds it with products like Pages, Spaces, Labs, and the Comet browser. SearchQ is narrower and newer, built around the idea that you should be able to slide a conversation from cloud convenience down to fully offline privacy without changing apps, and without giving up frontier-model quality.

How do they compare on privacy and data retention?

Both let you stop training on your chats, but the defaults differ. Perplexity uses consumer chats to improve its AI by default, with an opt-out toggle, and its Sonar API is strictly zero-retention. SearchQ never trains on your conversations, and its privacy modes layer on zero data retention, an encrypted enclave, and fully on-device inference.

To Perplexity's credit, its data handling is clearly documented: you can turn off training in your account settings, deleted-account data is removed within roughly 30 days, and both its Sonar API and Enterprise plans are excluded from training entirely. On the developer side it is unusually explicit - Perplexity states "we do not retain any data sent via the Sonar API" and that "we absolutely do not use any customer data to train our models" (Perplexity).

SearchQ approaches privacy as a default rather than a setting. It never trains on your data and exposes three levels of protection: Cloud (synced to your account, never used for training), Encrypted (inference inside a confidential-compute enclave that the server cannot read), and Local (models run in your browser, offline, so nothing leaves the device). An incognito toggle adds short-lived chats with zero data retention enforced at the provider. Concern here is widespread: in a 2026 Pew Research Center survey of 5,119 U.S. adults, 71% expected AI to make personal information less secure, versus just 3% who expected it to make information more secure (Pew Research Center).

How do they compare on model choice and accuracy?

Perplexity lets you pick a model, its own Sonar family plus selectable frontier models such as GPT-5.2, Claude Opus 4.6, and Gemini 3 Pro on paid plans. SearchQ leans the other way: Best-Model auto-routing chooses the best model per prompt, and a multi-model council can have several models answer, then synthesize a consensus.

These are two reasonable philosophies. Perplexity's manual model switcher gives power users direct control over which engine handles a query. SearchQ's auto-routing makes that choice for you by default, though you can still pin a specific model, and when accuracy matters it can run several models in parallel and add inline verification, where a peer model fact-checks a reply and flags unsupported or wrong claims. Neither approach is strictly "more accurate" - both ultimately ground answers in live web results, and quality depends heavily on the question. The honest summary is that Perplexity optimizes for fast, sourced single answers, while SearchQ optimizes for cross-checking and choice.

How do they handle web search and citations?

This is Perplexity's home turf. It was built as a citation-first answer engine, weaving numbered footnotes into every reply and surfacing the sources prominently, arguably the cleanest cited-search experience available. SearchQ's web search is always-on too and cites its sources, but search is one capability among many rather than the entire product.

If your daily work is "ask a question, get a sourced answer, click through to verify," Perplexity is hard to beat and deserves its reputation. SearchQ covers the same ground, always-on web search that cites sources, and adds adjacent capabilities such as chatting with your own documents (RAG/OCR) and voice input. So the difference is one of emphasis: Perplexity is a focused research tool, while SearchQ is a broader chat workspace that happens to search well.

How do they compare on price?

Both have a free tier and a ~$20/month plan. Perplexity Pro is $20/month (or $200/year) and Max is $200/month, unlocking model selection, higher limits, and agentic tools. SearchQ also offers a free tier plus paid plans, and uniquely its Local mode runs models in your browser free and unlimited.

For most individuals the practical comparison is Perplexity Pro at $20/month against SearchQ's free and paid tiers (Perplexity). Perplexity's $200/month Max tier targets heavy users who want unlimited Labs and agentic workflows. SearchQ's free, unlimited Local mode is the genuine outlier on price: because inference runs on your own machine, there is no per-query cost and no usage cap, though, as with any local model, quality depends on your hardware.

Dimension	SearchQ	Perplexity
Default privacy posture	Never trains on your data; privacy modes add an encrypted enclave, on-device Local, and zero-retention incognito	Trains on consumer chats by default; opt-out toggle; Sonar API zero-retention
Model choice	Auto-routes across many models + multi-model council	Selectable: Sonar family + GPT, Claude, Gemini
On-device option	Yes, in-browser Local mode (free, offline)	No, all queries processed in the cloud
Web search & citations	Always-on web search that cites sources	Citation-first answer engine (its core strength)
Price	Free tier + paid plans; Local mode free & unlimited	Free; Pro ~$20/mo; Max ~$200/mo

Which should you choose?

Choose Perplexity if cited web research is your main job and you want a polished, citation-first answer engine - it is genuinely best-in-class there. Choose SearchQ if privacy is the priority: you want a cloud-to-local dial, no training on your data, model breadth, and built-in verification, while still getting web search with sources.

Many people will be well served by either. If you mostly need sourced answers fast, Perplexity is an excellent, mature tool and the model switcher is a real plus. If your bar is "nothing sensitive should sit readable on a shared server," SearchQ's Encrypted and Local modes give you options Perplexity does not, without locking you to a single vendor's models. Whichever you pick, take a minute to check the retention policy and turn off training-by-default where it applies.

Methodology

This comparison relies on Perplexity's own documentation and help center for its pricing, model lineup, and data-retention policy, plus a 2026 Pew Research Center survey for the privacy-sentiment figure. SearchQ's capabilities (Best-Model auto-routing, the multi-model council, inline verification, and the Cloud, Encrypted, and Local privacy dial) are described from its current product behavior. Competitor features, models, and prices change often, so confirm the latest details on each vendor's site. All figures were last verified in June 2026.

Sources

• Perplexity - Privacy & Security (Sonar API zero retention, no training)
• Perplexity - Which subscription plan is right for you (pricing)
• Perplexity - Data collection at Perplexity (training default + opt-out)
• Pew Research Center - Americans and AI 2026