How Infino compares
to the alternatives.
Today, full-text and vector workloads run in cluster-managed search engines and vector databases — proprietary formats, separate copies of your data, separate compute, separate ops. Infino embeds BM25 and vector indexes inside a valid Apache Parquet file. One copy, one format, on object storage.
Side by side.
Infino is built for retrieval. Schema evolution and time travel are supported, with the focus on lower compute, data, and token costs at scale through hybrid search and vectors native to Parquet.
| Iceberg / Hudi / Delta | Snowflake / Databricks | Elasticsearch / OpenSearch | Vector databases | Infino | |
|---|---|---|---|---|---|
On-disk format | Parquet on object storage. No search or vector indexes, so retrieval queries fall back to full scans. | Proprietary micro-partitions or Delta tables. Optimized for warehouse-scale BI. | Inverted-index segments on cluster disks. Designed for an always-warm cluster. | Vector index files (HNSW, IVF) on cluster disks. Designed for an always-warm cluster. | Parquet file with BM25 and vector indexes embedded. One object, one range-read path, optimized for retrieval. |
Full-text search | Full scan — latency grows linearly with corpus size. | Available through a separate managed index service alongside the warehouse. | Sub-second BM25 on a warm cluster sized for peak load. | Limited or secondary to ANN. | PFOR-delta postings, FST term dictionary, BlockMaxWAND + MaxScore-BMM. |
Vector / ANN | No index. Vectors stored as f32 columns, queried by brute force. | Available through a separate managed vector index service alongside the warehouse. | k-NN plugin alongside text indexes. | Sub-second ANN on a warm cluster sized for peak load. | IVF with 1-bit RaBitQ codes (32× compression) plus precision rerank with Sq8Residual. |
Query / execution | Read by DataFusion, DuckDB, Spark, Trino — performance depends on the engine you bring. | Warehouse-scale SQL, optimized for BI workloads in the seconds-to-minutes range. | Query DSL through a translation layer with a limited optimizer. | Vector-search API; joins and aggregates run in a separate engine. | Optimized DataFusion executor. BM25, vector, and SQL execute in a single plan over the same Parquet bytes. |
Storage model | Object storage. Storage and compute decoupled. | Object storage underneath, accessed through vendor compute. | Cluster disks. Storage and compute scale together. | Cluster disks or managed service. Storage and compute scale together. | Object storage with stateless compute that can scale to zero between queries. |
Freshness | Commit-based. New data is visible after a snapshot commit; streaming sources land through a separate ingestion job. | Micro-batch ingest through Snowpipe / Auto Loader. Visibility lags by seconds to minutes depending on the pipeline. | Near real-time. Documents are queryable shortly after indexing, governed by refresh interval and merge pressure. | Upserts visible after index build completes for the affected segment. | Near real-time on object storage. Records are queryable shortly after they land, against the same Parquet file used for historical reads. |
Format compatibility | Standard Parquet, readable by any analytics engine. | Vendor format; search and vector indexes are not portable. | Engine-specific segment files. | Engine-specific index files. | Standard Parquet bytes — any engine reads the table as a columnar dataset; Infino readers use the embedded indexes. |
Same Parquet, but optimized for agents.
Agents have fundamentally different query patterns from humans, with implications for storage architectures. On the left below, a typical stack relies on separate search and vector systems alongside a columnar table format, forcing agent pipelines to join results across multiple stores. On the right, Infino embeds both indexes directly into the Parquet file, so vector, full-text, and SQL retrieval execute against the same bytes. The Infino reader is built on DataFusion, enabling hybrid queries from agents without maintaining separate search or vector infrastructure.
The table format and the search system are two different things. A hybrid query has to talk to both, then merge results client-side.
The table and its indexes are the same object. Any Parquet engine still reads it as a columnar table; an Infino-aware reader uses the embedded indexes to answer hybrid search against the same bytes — no second store, no merge.