Comparison
How Infino compares to Elasticsearch and OpenSearch.
Elasticsearch and OpenSearch are excellent search engines, designed for applications with trusted callers. Infino was designed for agents at scale: identity, permissions, and audit built into the engine, with full-text indexes and vector embeddings inside Parquet so you keep one copy of your data. All on object storage. Here is how that compares.
Fig. 02 / Capability matrix
Side by side.
Identity, permissions, storage, query shapes, and audit — across Elasticsearch, OpenSearch, and Infino.
| Elasticsearch Serverless | OpenSearch Serverless | Infino | |
|---|---|---|---|
Storage | Inverted index in a proprietary on-disk format. A copy of your source-of-truth data. | Same inverted index format as Elasticsearch. Still a copy of your source data. | Parquet on object storage. Full-text indexes and vector embeddings are stored alongside the data inside the Parquet files — one copy, queryable directly. |
Data duplication | Requires a second indexed copy of your source data. | Same — a second indexed copy of your source data. | Single copy. The Parquet file is the source, the index, and the vector store. |
Query shapes | Full-text strong. Vector (kNN) supported. SQL is a translation layer over DSL. | Full-text strong. Vector via the k-NN plugin. SQL is a translation layer. | Full-text, vector, and SQL on the same Parquet — combined in one request. |
Serverless model | Decoupled storage and compute on Elastic Cloud, but data is still ingested into Elastic's proprietary object-storage format. You pay for VCUs even when idle, and the data lives inside Elastic. | AWS-managed OCUs for indexing and search. Auto-scales, but minimum OCUs are always running and data is copied into an OpenSearch-managed store. | True spin-up-on-demand. Compute attaches to your Parquet on your object storage, runs the query, and releases. Scale to zero when idle. Pin a hot tier only for what needs sub-second latency. |
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