Starburst, the successor to Trino/Presto, is no longer just selling a query engine. The vendor is now pushing an Enterprise Intelligence Platform designed to query governed data without moving or duplicating it. The idea is easy to grasp and powerful on paper: create a single AI context layer capable of powering BI, internal copilots, business chatbots, and AI agents alike. As an added bonus, Starburst is highlighting AIDA, now generally available, to bring AI directly into workflows.
The SME Opportunity
For an SME or mid-market enterprise that already has some data structure in place, this deserves a real business look. Why? Because a shared context layer eliminates the need to rebuild the same integrations for every AI project. Instead of connecting a model to an ERP on one side, a CRM on the other, and business files scattered everywhere, you centralize rules, permissions, and data definitions once.
The result: less time wasted rebuilding connectors, lower integration costs, and, most importantly, more reliable AI because it responds from a consistent business context. This is exactly the kind of approach that can accelerate a RAG initiative, a sales assistant, or augmented reporting. In plain terms: you improve ROI without necessarily chasing a larger or more expensive model.
Another very practical benefit: governance becomes cleaner. If the context layer is designed properly, the same quality rules, vocabulary, and access controls can serve both BI and AI. That avoids endless debates between data, business, and security teams.
The Watchout
The flip side is complexity. Starburst is not something you drop in between two coffees. You need a solid data foundation, SQL and distributed data skills, governance, and strong project management. For a smaller organization, that is often overkill.
You also need to watch the hidden cost. Querying multiple sources in real time, especially when they are split between cloud and on-prem, can drive infrastructure spending up quickly. And the more business logic you encode into the platform, the higher the functional lock-in risk becomes. Switching tools later gets a lot less pleasant.
Finally, if AI agents are allowed to read this layer without tight controls, permissions, masking, and filters must be handled with real discipline. Otherwise, AI can become an excellent assistant... for exposing what it should never see.
The Compliance Angle
Because this is about data and AI, compliance is absolutely central. You need to map any exposed personal data, document usage, apply data minimization principles, log queries, and plan how corrections or deletions will be handled within context datasets. If the solution is deployed in EU or Swiss regions, that is a strong point, but you still need to contractually govern any potential data flows to the vendor's managed services or the hyperscaler's services. And if the context layer feeds a high-risk AI system, governance and documentation requirements become even stricter.
Conclusion & Cohesium Support
Starburst reflects a broader trend: enterprise AI is not worth much without context, governance, and reliable data. For a mid-market enterprise, it is a serious option. For a leaner SME, the better move is often a simpler, more sovereign, and better-sized architecture.
Rather than improvising, Cohesium AI can audit your data landscape, define an AI context layer tailored to your scale, frame the impact of GDPR/nLPD/AI Act, and design your first RAG or business-agent use cases on a clean, usable foundation. Contact us