Kanlet vs LakehouseBox

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Kanlet compared with LakehouseBox
Detail	Kanlet	LakehouseBox
What it does	Semantic context layer that helps AI agents understand your business data accurately	Managed Apache Iceberg tables and hosted agents for AI to work with data
Stage	MVP	Startup
Founded	Not given	2026
Based in	Not given	ES
Pricing model	Not given	Freemium
Pricing	Not given	Storage €0.10 per GB-month (5 GB free monthly), Compute €0.12 per CPU-hour (10 CPU-hours free monthly), AI tokens €2.50 per million (5 million free monthly). No request, ingress or egress fees.
Key features	Connects to 15+ data systems including Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Tableau, Looker, Power BI and Postgres Extracts structural metadata including databases, schemas, tables, views, columns and relationship lineage Provides Web-UI, MCP server and REST APIs for accessing semantic context Automatic incremental updates with configurable sync schedules from on-demand to daily or weekly Role-level access controls and data classifications including PII, PHI and SPII Only reads metadata without accessing or moving actual data from systems Integrates with quality monitoring tools including Monte Carlo, Anomalo and Bigeye	Managed Apache Iceberg tables with automatic maintenance and backups Multiple data ingestion methods including files, APIs, webhooks, and scheduled URLs Query with DuckDB, Spark, Trino, Polars, or PyIceberg Public data catalogs served over a CDN Automatic spatial clustering for geometry tables FTP, FTPS and SFTP support for file uploads from cameras and devices Corporate sign-in and granular access control for organizations Audit trail and activity log for organizational use
What makes it different	Eliminates confident AI mistakes by grounding agents in semantic context instead of having them guess from table names Reduces AI token costs and speeds up answers by resolving schema and joins once upstream instead of in each agent query Ensures consistent answers across all agents and teams using the same definitions and business logic Provides complete traceability and auditability of AI decisions with documented context and ownership	Open table format: Apache Iceberg ensures your data remains portable and readable by any compatible engine Agents keep what they observe, decide and do as persistent tables for learning across runs Operated in Europe with data storage in Frankfurt and model inference in Paris Pay only for what you use with no request, ingress or egress fees Serverless platform with no servers or clusters to manage
Free plan	Not given	Not given
Open source	Not given	Not given
Platforms	Not given	Not given
Public API	Not given	Not given
Community votes	0	0
DR (Domain Rating by Ahrefs)	9 +1 (up) since last check	Not checked yet no change since last check
Trust Flow (Majestic)	1	2
Citation Flow (Majestic)	24	26
Referring domains (Majestic)	29	32

4 details filled in for both companies.

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