From Trino to Starburst: The Architecture & Performance Advantages Explained

Trino is a powerful open-source query engine, but many organizations need additional capabilities to operationalize it for governed, reliable analytics at scale. Addressing these foundational concerns enables organizations to ensure their AI strategies are built on trusted, secure data. 

Starburst builds on Trino’s foundation and provides the governance, performance, and connectivity required for mission-critical workloads across clouds, regions, and data sources. Starburst integrates with popular AI models and services such as LLMs & MCP and includes its own AI agent that sits on top of our data products foundation.

In this session, we’ll walk through the architectural differences that matter most when teams move from open-source Trino to Starburst. You will see how Starburst improves performance, strengthens security and governance, expands connectivity, and simplifies day-to-day operations. Additionally, you will understand how AI services and features can be configured and governed just like data sources.

You’ll learn how to:

• Understand the key architectural differences between open-source Trino and Starburst.
• Strengthen governance with built-in access controls, auditing, and data product capabilities.
• Expand connectivity across clouds and sources through fully managed, enterprise-grade connectors.
• Simplify operations with automated scaling, improved reliability, and deeper observability.
• Enhance your data platform with popular AI models and services, and leverage our own AI agent.

Whether you are evaluating Starburst for the first time or clarifying how it differs from community Trino, this session provides a clear, practical overview of the advantages organizations gain when adopting Starburst at scale.