5 factors should be considered when selecting the Vector Search

Skip to main content

Hashnode Mundher's blog

Google Scholar
About me
External Articles

Command Palette

Search for a command to run...

5 factors should be considered when selecting the Vector Search

UpdatedOctober 8, 2023

•1 min read•View as Markdown

Mundher Al-Shabi, PhD

On this page

Data size:Speed-Recall trade-off:Memory limitation:CPU vs GPU:Building/Indexing time:

Here are 5 factors that should be considered when selecting the Vector Search/Index algorithms.

Data size:

For data sizes under 100K, a brute-force solution utilizing a FLAT index is sufficiently efficient.
Advanced algorithms may not offer significant speed improvements in such scenarios.

Speed-Recall trade-off:

When an exact match is important then brute force is the right solution
Significant query latency reduction is achievable with a minor sacrifice in recall.

Memory limitation:

Some algorithms like HNSW are memory-hungry.
Scalar and Product Quantization significantly reduces storage consumption, at the expense of the Recall.

CPU vs GPU:

Usually moving from CPU to GPU provides a speed boost
Not all algorithms are optimized for GPU

Building/Indexing time:

Sometimes the building/indexing time is crucial.
IVF has a shorter indexing time compared to HNSW.

More from this blog

How to Build Privacy into LLM Agents Without Breaking Their Brains

I've been spending a lot of time lately building autonomous agents with ReAct and RAG frameworks. The shift from stateless chatbots to stateful systems that can actually execute tools is incredibly us

Jul 19, 2026·3 min read

Stacking Agent Memory: Checkpoints, Status Boards, and Active Context

I love the idea of autonomous coding agents. But one of the quickest ways to hit a wall when setting them up is the infinite context problem. Your context window is finite, but the work you want the a

Jul 4, 2026·3 min read

Smart LLM Routing

Building LLM apps is easy, but scaling them without setting a pile of money on fire is hard. You really don't need the massive brainpower of GPT-5 for every single user query. Routing is how we fix th

Jun 26, 2026·4 min read

Using Simulators to Evaluate Multi-Turn AI Agents

Building a multi-turn conversational AI is surprisingly easy right now. Evaluating it is incredibly hard. For single-turn tasks, a standard static dataset works fine: you just feed in a prompt and ass

Jun 19, 2026·4 min read

Why Grep Won't Save Your RAG Pipeline

I’ve been reading through a recent paper titled "Is Grep All You Need? How Agent Harnesses Reshape Agentic Search". It’s a provocative piece with a premise I normally love. The authors claim that simp

Jun 9, 2026·3 min read

Mundher's blog

28 posts

Mundher Al-Shabi

© 2026 Mundher's blog

Members
Archive
Privacy
Terms