Algolia raises $110M for its search-as-a-service
I’ve always had a hard time understanding the value proposition in the same way I don’t understand the value proposition of e.g. AWS Rekognition.<p>Paying per use certainly doesn’t make sense, because it has to be qualified by the accuracy you get per use.<p>And there’s no serious way to understand the accuracy you get per use (on your specific unusual distribution of queries) without employing the expensive ML &#x2F; stats engineers you probably thought you could avoid hiring by outsourcing to Algolia &#x2F; Rekognition in the first place.<p>But once you need to hire them anyway, you might as well utilize them to build this type of thing in-house in ways that are much more tailored and optimized around your in-house data models and data integration tools.<p>To put in perspective, I’ve worked in several companies (from small start-ups to large ecommerce sites) that have a variety of search needs spanning plug and play Lucene all the way to highly customized joint embedding neural network based nearest neighbor search, and tons in between.<p>The distribution of text in e.g. the support center search use case was totally different than the product search use case or the document store use case, where highly unique word distribution, special words, frequency of required updates to the search index, asymmetric costs of surfacing bad or deleted content items, etc., was the norm.<p>Every search use case was different and needed care to develop unique annotated result sets to measure mean reciprocal rank, NDCG, etc., as well as simple stakeholder subjective opinion of quality.<p>Short of basically hiring Algolia to be a gigantic consultant on all these things, I don’t see how it could actually be valuable.<p>I suspect it’s just an easy sell to CTO types that don’t really understand. They want “search” to be one problem with one little component to drop in to solve it, but it’s just not real.