Hi Solr Community, We are evaluating Solr for vector search and would appreciate some guidance on performance and sizing.
We have ~2M vector documents, but are seeing *multiple-second latency for the first ~100 queries*. After that, performance improves. we want to scale it to 200 M documents in future. A few questions: - Is the default Solr vector-search configuration production-ready, or does it typically require tuning? - What are the key parameters/settings we should tune for HNSW/KNN performance? - How should we estimate the *CPU, memory, storage, and node requirements* for a given vector count, dimensions, QPS, and target latency? - For larger scale, how does Solr vector search typically compare with a dedicated vector DB such as *Qdrant* in terms of hardware requirements and scalability? - Would this architecture make sense: *Qdrant for vector retrieval → return candidate IDs → Solr for existing business logic, filtering, and ranking*, or is it better to keep the entire flow in Solr? Our preference is to keep vector search in Solr if it can meet the required latency and scale without significantly higher infrastructure cost. Any recommendations or real-world experience would be very helpful. -- Satya Nand Kanodia Architect Search +91-8130189159
