AI Lakehouse Architecture

Build an AI Lakehouse on VAST

Build an AI lakehouse while preserving the open analytics ecosystem your teams already know. VAST simplifies the architecture beneath Spark, Trino, SQL, and Python, enabling continuously updated analytics with dramatically less infrastructure letting you design an AI Lakehouse for today’s workloads.

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AI Lakehouse
The AI Lakehouse Challenge

The Lakehouse Solved Yesterday’s Problem

The lakehouse simplified analytics by reducing data movement and embracing open ecosystems. But modern lakehouses increasingly support AI alongside analytics, requiring continuous updates, operational data, streaming, and vector retrieval that drive additional infrastructure complexity.

Immutable Files

Open table formats simplify analytics but depend on immutable files, metadata coordination, and compaction as data changes. Continuous updates become increasingly expensive and operationally complex.

Infrastructure Creep

Streaming systems, warehouses, operational databases, vector stores, and synchronization pipelines quietly recreate the complexity the lakehouse was intended to eliminate.

Performance Tradeoffs

Organizations increasingly maintain separate analytical and operational systems because file-based lakehouses weren't designed for continuously updated analytical workloads.

We were usually bottlenecked in the past, getting maybe 10,000 rows per second. With an in-house solution on VAST, we are now ingesting over 1,000,000 rows per second. That makes a massive difference in achieving low data latency.

Aaron Pritchard
Technical Team Lead, Evodian
Why Now

Why Modern Lakehouses Need a Better Foundation

The modern lakehouse should remain open. Spark should remain Spark. Trino should remain Trino. Medallion architectures should remain familiar. What needs to change is the analytical data layer beneath them.

VAST replaces immutable analytical files with a native analytical database built on VAST’s Disaggregated Shared Everything (DASE) architecture. Existing analytical engines continue operating through familiar catalogs 
and APIs, while workloads requiring continuous updates, point lookups, 
and analytical scans gain the performance characteristics of a flash-native VAST DataBase rather than a file format.

Organizations can continue using Iceberg where it makes sense while adopting VAST DataBase for performance critical analytical workloads, 
or consolidate entirely as their lakehouse evolves.

Why Modern Lakehouses Need a Better Foundation