The most helpful thing is the open source big data engine, heps to process and serve large scale data in real time with very low latency time.Its content recommendations are very useful for the modern day real-time analysis. Also, it is more flexible and scalable with advanced query techniques which makes it more easy to use.

For our use case in advertising, Vespa leaves Apache Lucene-based products in the dust: - High indexing throughput while searching - Very, very technical team - Best of the best technical support and guidance - Multiple times, discussions were had and the next day the idea was implemented

Vespa provides a comprehensive set of features you would look for in a search engine, particularly in more ranking capabilites (e.g. leveraging ML models) and performance than what Elasticsearch offers out of the box. They're also constantly making advancements in new capabilities that they offer a nice hybrid between vector databases and a conventional search engine. Particularly for our business problem at OkCupid of recommending potential matches to millions of other users based on a myriad of factors and ranking algorithms, Vespa was a great fit to not only meet those use cases, but improve our team's development and iteration workflows in our recs system. The Vespa team is also very active on Slack: https://vespatalk.slack.com/ssb/redirect and genuinely collaborative. In my case, we worked together with an engineer from their team who helped raise improvement changes into the engine to help us meet our use cases.