Infino is a software retrieval and analytics engine designed to combine full-text search, vector search and SQL over data stored in Parquet files on object storage. For agent builders, its proposition is that one system can retrieve from searchable corpora, agent memory and structured records without requiring a separate search service, vector database and integration layer. That is an architectural pitch—not proof that Infino will be faster or cheaper for every workload.
What Infino is—and what it is not
Infino is an engine, not a physical product. The OpenSearch solutions profile describes it as an open-source retrieval engine written in Rust and built on Apache Parquet and object storage. It can store documents, embeddings and structured data together on S3, Azure Blob Storage or local disk, with storage and compute decoupled, according to the profile. OpenSearch solutions profile
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Parquet is a columnar file format commonly used to store analytical data. Infino’s design aims to make those files part of the retrieval layer rather than requiring builders to move all data into a conventional search index or vector database. Whether existing data can be used directly, or requires preparation or hydration, depends on the deployment and features available.
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An agent question may require more than finding a semantically similar passage. It might need to filter records by a field, match an exact name or phrase, group results, count them, or join related records. Infino’s stated design combines these operations in one retrieval system:
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- BM25 full-text search finds matches based on terms and their relevance, useful when exact words, names or identifiers matter.
- Vector search finds semantically similar content using embeddings, which can help when a user’s wording differs from the wording in the source material.
- SQL supports structured queries such as filtering, counting, grouping and joining records.
For an agent, those query paths can address different parts of a request. A system may need semantic retrieval to find relevant documents, keyword matching to identify a specific entity, and SQL to constrain or aggregate the results. Infino’s homepage illustrates this combined-query idea, but that illustration is the company’s own product demonstration, not independent validation. Infino homepage
How agent builders could use it
Searchable corpora and agent data exhaust
The Infino repository lists searchable corpora and agent data exhaust among its use cases. In practice, that means builders could use the engine to retrieve from document collections or records generated by agent activity, while keeping data in supported storage rather than treating every source as a separate retrieval system. The repository also describes agent memory as a use case; that is a product use case, not a guarantee that Infino provides a complete memory-management system for every agent framework. Infino repository
MCP retrieval
The repository lists an MCP server that exposes keyword, semantic, hybrid and SQL retrieval to compatible clients. It says local embeddings are available and that the MCP integration is read-only by default, with writes requiring an explicit flag. That default matters for builders connecting an agent to data: retrieval access does not by itself imply permission to modify the underlying records. Check the repository’s current setup and configuration before deploying, since implementation details can change. Infino repository
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CLI access
Builders can also use the Infino CLI against a path or bucket, according to the repository. This offers another way to work with data at a storage location; it does not mean every storage format, query capability or integration is available in every edition.
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How Infino fits into the OpenSearch ecosystem
Infino is another retrieval and data-layer approach for agent builders, not the only way to connect agents with OpenSearch. OpenSearch supports external agents through its MCP server and agent skills, and also supports agents running inside an OpenSearch cluster. An OpenSearch blog post dated June 10, 2026, describes an agent server that was experimental in OpenSearch 3.6 and routes requests among specialist agents. OpenSearch agent server blog post
The practical distinction is architectural. Infino emphasizes retrieval over Parquet and object storage with search and SQL in one engine. OpenSearch provides its own search platform and agent-related capabilities. Which approach fits depends on where data lives, which query operations the application needs, and how much infrastructure the team wants to operate.
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Deployment options and feature availability
Infino’s pricing page lists three offerings. The page marks its Cloud service as a beta and identifies some capabilities as Enterprise features; these labels describe the page’s current listing and may change. Infino pricing Infino plan details
| Offering | What the page lists | What to consider |
|---|---|---|
| Open-source core | Apache-2.0, single-node | Suitable to evaluate or self-manage as a single-node deployment; the team owns operation of that deployment. |
| Cloud | Multi-tenant serverless; beta | Usage is measured by storage, write tokens, read tokens and returned bytes. Confirm current beta access and metering before estimating cost. |
| Enterprise | Custom single-tenant deployment | The page marks query DSL compatibility, Parquet hydration and Iceberg/Delta/Hudi integration as Enterprise features. Confirm which features and deployment terms apply to the intended use. |
The pricing page also publishes workload-based comparisons with other vendors. Those figures are Infino’s estimates using selected workload inputs and assumptions, not independent cost studies. They should not be treated as general market prices or a forecast for a different workload. Infino pricing
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What to evaluate before choosing it
Infino’s architecture is most relevant when the team wants to keep data in Parquet or object storage and needs multiple retrieval modes. The architecture alone does not settle deployment maturity, latency, cost or operational fit. A useful evaluation should compare the systems against the same data and queries:
- Data placement: Can the required data remain in the team’s existing Parquet and object-storage setup, or does the chosen plan require a different ingestion or hydration path?
- Query coverage: Test exact keyword matches, semantic retrieval, structured filters and SQL operations that the agent actually needs.
- Operations: Compare self-managed operation with the Cloud beta or a custom Enterprise deployment, including the work required to monitor and maintain the service.
- Feature access: Verify whether essential capabilities are available in the intended edition; some listed integrations and compatibility features are marked Enterprise.
- Workload performance and cost: Measure the application’s own data volume, query mix, write rate and returned data. Company-published estimates are not a substitute for workload-specific testing.
- Agent permissions: For MCP use, account for the repository’s read-only-by-default description and explicitly assess whether any write access is necessary.
Who is behind Infino?
Infino describes its team as “The creators of OpenSearch and engineering leaders across LinkedIn, Google, & Amazon,” and names Ekechi Nwokah, Vinay Kakade, Asif Makhani and Murali Krishna. That wording is the company’s own description; it does not independently establish each named person’s precise role in creating OpenSearch. Infino homepage
The available product and company pages establish the launch framing but do not establish a precise public launch date or venue. No date should be inferred from the headline alone.
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