Infino Blog: engineering notes, case studies, and company news
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- Cost-efficient alternatives for large-scale search and vector databases What sets the cost of search at scale, the alternatives to Elasticsearch and dedicated vector databases, and dedicated engines versus SQL with search extensions.
- What’s the SQLite equivalent for full-text and vector search? Embedded engines that run inside your app with no server, and how five options compare on keyword, vector, hybrid and SQL search.
- Open VDBBench: a fair and open benchmark for vector databases The Zilliz-hosted leaderboard stopped re-running competitors in June 2025. We forked the harness and ran 19 engines on current versions, one Azure VM size, every row dated.
- Choosing a database for AI agents: memory, retrieval and state compared What agent memory needs from its store, and how seven common options compare on hybrid search, SQL, deployment and storage format.
- You don’t need two databases for hybrid search A vector database next to a search cluster is two copies of the corpus and a sync loop. One table, both indexes, one ranked result set.
- Where keyword search and vector search each fail The same queries through BM25 and vector search over 10,003 real banking questions. Each retriever fails where the other is strong.
- Infino: fastest VectorDB at a million, still cheaper at a billion Fastest on VectorDBBench at 1M and 10M — then the same engine reshapes from an in-RAM graph to an object-storage index, roughly 10x cheaper at a billion.
- Fixed grids on vector search - comparing 3 approaches to quantization: FAISS, Turbovec, and Infino Three 4-bit indexes at the same 768 bytes per vector: recall within two points, latency 30× apart.
- What is hybrid search? Two retrievers that miss different things, and the rank fusion that merges their lists.
- Why object-storage vectors use OPANN + Sq16, while RAM uses HNSW One durable vector corpus, with a graph in RAM and bounded range reads on object storage.
- Infino's self-driving vector engine You write a table; the engine inverts it in the background and stamps the ANN dials from your data — 0.99 recall@10 on Cohere-10M, zero knobs.
- Inside a Parquet superfile Why the indexes live inside Parquet, how the footer addresses them, and where the format still hurts.
- Cutting a ten-term BM25 OR from 74.6 ms to 20.0 ms Common terms defeated MaxScore skipping; a 4096-document window removed the per-document cursor merge.
- The 3× to 10× regression from running decode on tokio’s I/O pool Cold vector search went from 1.1 s to 3.7-11 s when CPU-heavy decode starved object-store I/O.
- The three costs of a cold query on S3 File discovery, connection setup, and cache residency on the first query from a new worker.
- A commit on S3 is one compare-and-swap Stage immutable files in parallel; publish the complete table with one conditional pointer write.