About VectorOpsReport
VectorOpsReport covers vector search as an operational problem rather than a machine learning one. Three questions get most of the attention here: how much memory an index will cost before you build it, which index type fits a given corpus and write pattern, and why an index that looks healthy is returning the wrong neighbours.
It is for people running similarity search at a size where index parameters start to matter, and for the person who has to size the instance. Figures come from published papers, project documentation and vendor specifications, cited on the page so the original can be checked instead of this site's summary of it.
Nothing here is a benchmark result. Where a number appears it is either quoted from a cited source or it is arithmetic shown in full, so it can be recomputed rather than trusted.
The one interactive thing on the site is the HNSW index RAM sizer, which runs the memory arithmetic described in vector database memory sizing against a corpus you describe. It deliberately reports no latency estimate, because query latency does not follow from corpus size and dimension.
What is covered here
- Comparison
- Fundamentals
- Guide
- Index Selection
- Index Sizing
- Indexing
- Operations
- Troubleshooting
- Vector Search
14 articles are published so far. New articles are announced on the RSS feed; there is no fixed publishing schedule and this site does not promise one.
How these articles are produced
Articles are researched from primary sources: vendor and project documentation, published standards and specifications, release notes, advisories, and measurements published by the people who took them. Drafts are produced with AI assistance and then edited against those same sources before anything is published. Where a figure comes from a datasheet or a third-party measurement, the article names the source and links to it so you can check the original rather than take this site's summary of it.
Everything here is published under the VectorOpsReport Editorial byline. That is an editorial desk, not a person, and no article on this site claims hands-on lab testing, benchmarking, or first-hand measurement. Nothing here should be read as a report of something this site physically tested.
Corrections
Getting it right matters more than getting it first. If something on this site is wrong, out of date, or missing the source it should cite, email hello@vectoropsreport.com with the page and the specific claim. Substantive corrections are made on the page itself rather than quietly dropped.
How this site is funded
This site currently runs no affiliate links, no sponsored content, no paid placement, and no display advertising. Nothing on it earns a commission. If that changes, this page and the disclosure page will say so before any such link appears.
The full position is on the disclosure page. Read it before acting on anything here that reads like a buying recommendation.
Related sites
VectorOpsReport is run alongside a small number of other single-topic sites:
- OllamaLab - Local LLM hardware and inference speed.
- RAGStackGuide - Mastering Retrieval-Augmented Generation & Vector Indexing.
- LlamaIndex Hub - Build and debug RAG retrieval pipelines
Contact
Email: hello@vectoropsreport.com
Site: vectoropsreport.com
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