Qbeast is an efficient, format-agnostic, multi-dimensional clustering engine for the lakehouse, now delivered as a distributed service. Our latest release marks a major milestone: Qbeast runs on all three major cloud providers (AWS, Google Cloud, and Azure) and supports all three open table formats (Apache Iceberg, Delta Lake, and Apache Hudi), with fully automated deployment and management inside the customer's account. The release brings substantial performance gains in both querying and indexing, and introduces a Model Context Protocol (MCP) server that enables direct integration with AI tools—making Qbeast a first-class data source for agentic workflows.
Interesting direction — “AI-native access” sounds promising, but curious what that actually means in practice beyond marketing. Is it query optimization, semantic layers, or something else?
Thanks for catching the point on AI-native access. Previously, operating a Qbeast-backed lakehouse required terminal commands; we've now added MCP connectivity to enable agentic coding tools, see the linked post for details and screenshots. Query optimization comes via Qbeast's indexing and auto-optimize features. Semantic layers aren't on our radar yet, but we're open to adding them to the roadmap. Keep in mind we're an early-stage startup, so we're just getting going.