Parallel

Infrastructure for intelligence on the web. Start here → https://t.co/0Enegj3L73 Try our Turbo demo → https://t.co/sCEKz26K26

Parallel Search Turbo is a new low-latency mode of Parallel Web Systems' search API, aimed at developers building voice agents, chat apps, and other AI systems where users are waiting on an answer. It runs at a median 200ms per request and is priced at roughly $1 per 1,000 requests, which the company positions as up to 14x cheaper than the default web search built into frontier models while holding similar or better accuracy on benchmarks like BrowseComp, SimpleQA, and WebWalker. Results come back as LLM-ready excerpts rather than raw SERP links, matching how agents actually consume the web. The launch matters because grounded retrieval has been a real bottleneck for realtime AI. Voice and chat products have typically had to choose between fast responses and thorough web search, and Turbo is pitched at closing that gap. Parallel also frames it as useful for deep research fan-outs that want to run many more queries per task, for reinforcement learning rollouts that depend on millions of live searches, and for small local models that lean heavily on external context. Parallel Web Systems is led by founder Parag Agrawal, the former CEO of Twitter, and has raised roughly $100 million to build search and retrieval infrastructure aimed at AI agents rather than human browsers. Turbo sits alongside the company's earlier Search API, which launched in November, and extends the same proprietary web index toward workloads where latency and unit cost, not just answer quality, decide whether search gets used at all.

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