How Fieldy AI Achieved Reliable AI Memory with Qdrant - Qdrant

This post details Fieldy AI's migration from Weaviate to Qdrant to address reliability issues (5xx errors) and achieve fault tolerance for their AI memory platform. Key technical contributions include: reusing existing vector schema, batching embeddings locally before ingestion, disabling indexing during bulk import for throughput optimization, using Qdrant's gRPC interface, and implementing hybrid search with BM25 and Reciprocal Rank Fusion (RRF) for relevance scoring. The migration resulted in 100% reliable recall, reduced latency, eliminated network errors, and a two-thirds reduction in infrastructure costs, enabling scalability to tens of millions of embeddings with minimal operational intervention.