GoKawiilInfino details architecture for building search index directly on object storage
Infino describes how it built a retrieval engine whose full-text, vector and SQL search index lives on S3, GCS or Azure Blob Storage rather than local disk. The company organizes data into Parquet-based 'superfiles' grouped into 'supertables,' each with a manifest and term index, to reduce the number of random reads needed when querying object storage.
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Traditional search engines like Lucene assume cheap, fast local disk reads, but object storage turns every random read into a costly network request, so Infino's design choices suggest a broader shift in how search infrastructure must be rethought for cloud-native, object-store-first data architectures. This could matter for teams running agents or applications over large-scale unstructured data already parked in cloud storage, since it may reduce the need to duplicate data into separate search-optimized systems.
- Infino builds its search index directly on object storage (S3, GCS, Azure Blob) instead of local disk.
- The system uses Parquet-based 'superfiles' and 'supertables' with manifests and term indexes to minimize random reads.
- Object storage reads are costlier and slower than local SSD reads, requiring different optimization priorities for search engine design.
Source: infino.ai, 2026-10-15
Published there as: “What changes when your search index lives on S3?”
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