Perplexity

What is Perplexity?

Perplexity is an AI answer engine that combines real-time web retrieval with large language models to produce direct, cited answers rather than a ranked list of links. When a query is submitted, Perplexity searches the live web, reads the most relevant pages, and generates a synthesized response that includes inline citations linking back to the original sources. This differs from a traditional search engine (which surfaces links) and from a general-purpose chatbot (which answers from static training data): Perplexity does both simultaneously.

The core product has two search modes. Standard Search handles everyday questions quickly. Pro Search applies multi-step reasoning — breaking a complex question into sub-queries, iterating on results, and producing a more thorough answer. Deep Research extends this further: it autonomously executes dozens of sub-searches, reads retrieved documents at length, and outputs a structured, footnoted report suitable for professional research. Perplexity also offers Spaces (shared team workspaces with persistent context), Perplexity Pages (published research summaries with citations), and — on the Max plan — Perplexity Computer, an agentic layer that coordinates 19 specialist AI models to handle end-to-end workflows. In February 2026, Perplexity discontinued its ad-supported model and moved to a pure subscription structure, explicitly to keep results free from advertiser influence.

Who is it for?

Perplexity is most useful for anyone whose work requires fast access to sourced, current information — rather than general text generation or creative tasks. It processes over 1 billion queries per month, but its design suits a narrower set of heavy use cases.

• Researchers and analysts who need to survey a topic quickly, validate claims, or produce a cited briefing document without manually reading dozens of tabs — Deep Research handles this on the Pro plan.
• Journalists and fact-checkers who need to verify specific claims against live web sources and cannot rely on a model’s training-data cutoff.
• Developers and engineers using the Sonar API to build applications that require grounded, real-time answers rather than static LLM responses.
• Knowledge workers and students on the free tier who ask frequent factual questions and want answers with traceable sources rather than bare ChatGPT-style responses.