Answer any agent question on Parquet.
Scalable search + analytics data infrastructure on object storage, for 10x cheaper.
ClickBench: Infino ranks #1 among search engines for SQL analytics →
trusted by
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- Bazinga Labs
Any agent question can be answered on Infino.
1An agent asks a question
How manyA count. The agent wants a number. SQL does this with COUNT. errors last nightFilters. Keep the error-level rows and the last 24 hours. mention disk fullKeyword search. Match those exact words in the log body. Today this is a call to a search engine. or look like a capacity incidentSemantic search. Match meaning even when the log never says disk full. Today this is a call to a vector database., and are tied to last week’s outageGraph search. Keep the errors linked to the outage’s record through a key they share, like a host or a service, even when they never mention it. Today this is a call to a graph database.? Which serviceA group on the same hits. SQL GROUP BY. Today a second warehouse query after the searches return. had them, and which team owns itA join. Match each service to its owner in a second table. Today the agent looks this up in another system and stitches the answers together.?
2It hides seven jobs
- count
- group
- join
- filter
- keyword search
- semantic search
- graph search
3Infino SQL runs all seven
SELECT o.team, h.service, count(*) AS hits FROM hybrid_search( 'logs', 'body', 'disk full', 'embedding', :q, 1000 ) AS h JOIN graph_walk('logs', 'incidents', [4521], 2, 5000) AS g ON g._id = h._id JOIN owners o ON o.service = h.service WHERE h.level = 'error' AND h.ts > now() - interval '24 hours' GROUP BY o.team, h.service ORDER BY hits DESC;
Replace five systems with Infino and run on one copy of your data.
- 01
Start with search
Swap out Lucene under OpenSearch, or load an Elasticsearch snapshot. Your clients and Dashboards stay as they are.
- 02
Add the next workload
Vectors, graph and analytics land in the same Parquet files in your bucket.
- 03
Consolidate everything
Retire Pinecone, Neo4j, ClickHouse and Snowflake one at a time, until one copy serves every agent.
Faster, cheaper and more scalable than any other retrieval engine in the world.
Keyword · 1B documents
- Infino
- Lucene
Vector · 1M vectors
- Infino
- OceanBase
- Elasticsearch
- Milvus
- Redis
SQL · ClickBench
- Infino
- OpenSearch
- Quickwit
- Elasticsearch
- SigLens
How Infino compares.
| Capability | Infino | Databricks | Snowflake | Elastic |
|---|---|---|---|---|
| Storage | Parquet in your bucket | Delta, plus a vector index | Snowflake or Iceberg, plus a search index | Its own index |
| Search | In the same files | Vector Search endpoints | Cortex Search service | Lucene |
| SQL and joins | Yes | Yes | Yes | Limited (ES|QL) |
| Context for agents | Table cards and join keys | Genie spaces | Semantic models | Agent Builder |
| Cost at 1 PB | 1× | 3.2× | 4.6× | 15.9× |
| Answer accuracy | 73.3% | 60.0% | 57.8% | n/a |
Specs in the cloud or on-prem.
- search
- full-text · vector · hybrid · graph · NL
- index
- BM25 (PFOR-delta, FST) · HNSW · OPANN + Sq16
- engine
- Rust + Small Language Models (SLMs)
- language
- SQL (Apache DataFusion) · REST · Query DSL · MCP · NL
- storage
- object storage, S3 · GCS · Azure Blob · on-prem
- format
- Apache Parquet
- deploy
- on-prem · self-hosted cloud · hosted cloud
- core license
- Apache-2.0
- security
- SOC 2 Type II
- encryption
- TLS in transit · AES-256 at rest
Queries, made simple.
or run it yourself: pip install infino · Apache-2.0 · quickstart →