Memory Tiers in Qdrant: What to Use and When

Qdrant manages growing vector collections by allowing dense vectors, HNSW graphs, quantized copies, payloads, and indexes to use pinned, cached, or cold memory tiers according to their performance and cost requirements. Cached storage is a simple, fast choice while the full working set comfortably fits in RAM, whereas cold storage reduces RAM use but can introduce disk-read latency, especially for unquantized vectors. Quantization compresses vectors so searches can score candidates with smaller representations, reducing disk I/O and enabling a compressed copy to be pinned in RAM for predictable, high-speed performance with a limited memory footprint. Pinning quantized vectors is recommended when RAM capacity becomes the limiting factor, while caching both full-precision and compressed vectors should be reserved for systems with ample memory. The guidance also warns that HNSW inline storage combined with quantization can substantially increase disk usage depending on vector dimensions and graph connectivity, making workload-specific testing essential. Across all configurations, deployment decisions should be based on the current RAM working-set ratio rather than collection size alone, since performance can change sharply as data approaches available memory limits.

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