We are excited to announce that Redis Query Engine now supports Quantization and Dimensionality Reduction for vector search. This is based on an Intel and Redis partnership leveraging Intel SVS-VAMANA with multiple compression strategies.
Redis has always been the go-to choice for agents and applications demanding blazing-fast performance, and our community knows this comes with a direct relationship: memory usage equals operational cost. As vector search has become increasingly popular for powering AI applications, from recommendation engines to powering specialized agents, we've consistently heard from developers and operations teams about a common challenge: the memory footprint of high-dimensional embeddings can quickly become a budget concern.
Here's the reality: when you're running vector search workloads on Redis, every vector stored in memory directly impacts your infrastructure costs. A typical deployment with millions of 768-dimensional vectors (common with modern embedding models) can consume hundreds of gigabytes of RAM. For organizations scaling their AI applications, this memory requirement often becomes the primary cost driver.
But what if you could dramatically reduce that memory footprint without sacrificing the search quality and performance that made you choose Redis in the first place? With a comprehensive compression strategy - Quantization and Dimensionality Reduction, we can reduce the total memory footprint by 26–37%, while preserving search quality and performance.
Dive into the compression technology
Modern vector similarity search faces a fundamental challenge: as datasets scale to billions of vectors with hundreds or thousands of dimensions, memory footprint becomes the dominant deployment constraint and memory bandwidth emerges as a primary performance bottleneck. Traditional approaches to million-scale similarity search have struggled with the dual pressure of maintaining search accuracy while managing memory footprint, particularly when dealing with the random memory access patterns inherent in graph-based algorithms.
For context, storing 100 million vectors with 1536 dimensions in single-precision floating-point format requires 572GB of memory. This scale makes traditional exact nearest vector search (like using FLAT index in Redis) impractical, necessitating approximate methods that can operate within reasonable memory constraints while maintaining acceptable accuracy levels.
Intel SVS-VAMANA foundations
SVS-VAMANA combines the Vamana graph-based search algorithm, introduced by Subramanya et al., with Intel’s compression technology: LVQ and LeanVec.
Vamana is similar to HNSW in its use of proximity graphs for efficient search. Unlike HNSW’s multi-layered structure, Vamana builds a single-layer graph and prunes edges during construction based on a tunable parameter. Both algorithms are capable of achieving very strong search performance but require substantial memory for both the graph structure and full-precision vectors, making compression essential for large-scale deployments.
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