Vector Database Deployment

Qdrant for Rust-powered performance and simplicity. Milvus for massive-scale distributed deployments. Weaviate for built-in ML model integration. pgvector for teams already on PostgreSQL who need vector search without new infrastructure. We benchmark your actual query patterns and index sizes against each option — the right choice depends on your scale and existing stack, not marketing pages.

HNSW parameters (M, efConstruction, efSearch) get tuned for your recall/latency tradeoff. We benchmark with your actual vectors — not synthetic data. For large indices, we configure IVF or scalar quantization to reduce memory requirements. Segment sizes and flush intervals balance write throughput with query performance. A misconfigured index can be 10x slower than a tuned one.

Production deployments run replicated with automatic failover. We configure read replicas for query-heavy workloads and sharding for indices that don't fit on a single node. Data gets backed up to object storage on a schedule. Kubernetes deployments use StatefulSets with proper PV management so a pod restart doesn't trigger a full re-index.

We wire the vector database into your application — embedding generation pipeline, ingestion workers, and query API. Monitoring covers query latency distributions, index size growth, memory usage, and segment health. Alerting fires on slow queries, disk pressure, or replication lag. You get a vector search backend your application can depend on.