How Garden Scaled Patent Intelligence with Qdrant - Qdrant

What this post added

This post details how Garden scaled its patent intelligence platform using Qdrant Cloud, specifically highlighting the impact of Qdrant's filterable HNSW capabilities. Garden's use case involves processing over 200 million patents, each with extensive metadata, requiring efficient vector search combined with surgical-grade filtering. The post emphasizes Garden's migration to Qdrant Cloud due to its managed Rust backbone, which offloaded operational burdens while maintaining source-level transparency. Key technical aspects highlighted include the use of 8-bit scalar quantization for efficient memory management (hot vectors in RAM, colder on disk), achieving sub-100ms p95 query latency, and achieving a 10x lower cost per stored GB. The migration process itself was streamlined, involving a weekend of scripted ETL to push embeddings into Qdrant Cloud with minimal code alterations. The business impact includes addressing a 10x larger patent corpus, managing hundreds of millions of vectors, and unlocking a new revenue line through high-confidence infringement detection.