Smarter, Not Bigger: Rethinking Vector Search for Resource-Constrained Environments

Vector search often performs well in early stages. Then scale changes the equation.

As datasets grow from thousands to hundreds of thousands of vectors, relevance begins to drop, latency increases, and infrastructure costs start to rise. The typical response is to scale hardware. More memory. Larger instances. Higher spend. But in many cases, the root problem isn’t capacity, it’s architecture.

This whitepaper examines how vector search systems behave under scale in resource-constrained environments. It explores why accuracy degrades, where bottlenecks emerge, and how algorithmic optimisation can outperform brute-force infrastructure upgrades.

Inside, you’ll find:

• Why similarity scores decline as vector collections grow
• The technical trade-offs between IVF and HNSW indexing
• How embedding model choice directly impacts discrimination and relevance
• Why naive query construction limits semantic accuracy
• A practical framework for bounded re-ranking without excessive latency
• Migration, monitoring, and benchmarking guidance for production systems
• Measured performance improvements achieved on a 16GB environment

This is not a theoretical discussion. It’s a practical guide for engineering leaders and architects responsible for AI-driven search systems who need better accuracy without escalating infrastructure costs.

Download the full whitepaper to understand how smarter design decisions can stabilise search performance at scale without expanding your hardware footprint.