Real-Time Context Beats Web Search
The Incord Team·May 2026·5 min read
Web-search APIs crawl the open web on every query. Pre-embedded real-time context is faster, cheaper, and ranked. Here's the difference.
How web-search APIs work
Search APIs like Perplexity and Tavily crawl and rank the open web in response to each query. Every call spins up a fresh fetch, scrapes pages, and returns links or snippets your model then has to parse.
It's flexible, but it's slow, it's billed per crawl, and it has no concept of structured, validated data like asset prices.
Pre-embed, then retrieve
Incord does the expensive work once, ahead of time. The world is ingested, embedded, and ranked continuously, so a query is just a vector lookup plus a rerank, sub-second, every time.
Results come back ranked by relevance and freshness, with six filter dimensions and confidence hints, so your agent gets signal instead of a pile of pages.
When to use which
Open-ended web research is still a job for a search API. But for the data agents need constantly, live prices, breaking news, market events, a pre-embedded real-time layer wins on latency, cost, and trust.
And because Incord ships drop-in MCP, Tavily, and OpenAI-embeddings endpoints, you can point an existing agent at it with a single URL change and feel the difference.