Qdrant Team here. It depends on you use case. For smaller data amount and just vector similarity search you can use libraries like FAISS, ScaNN, etc. If you are

Qdrant Team here. It depends on you use case. For smaller data amount and just vector similarity search you can use libraries like FAISS, ScaNN, etc. If you are looking for a simple solution without high requirements on performance and just maybe a few millions of vectors you can use traditional solutions like Elastic or Postgres. Dedicated vector db solutions like Qdrant become relevant when you are looking for real-time performance, scalability up to billions and dedicated features that make your life easier. Qdrant works standalone in local (in memory), docker and (managed) cloud mode by just exchanging the host url. Best available performance https://qdrant.tech/benchmarks/ https://qdrant.tech/benchmarks/ Easy to start with https://qdrant.tech/documentation/quick-start/ https://qdrant.tech/documentation/quick-start/ And there is a managed Cloud with 1GB free plan. https://cloud.qdrant.ok https://cloud.qdrant.ok You are welcome to join our vibrant community on Discord http://qdrant.to/discord http://qdrant.to/discord