Rethinking Database Architecture to Reduce Costs at Enterprise Scale for Critical Workloads

As enterprise systems scale, so do their infrastructure costs—often exponentially. Traditional database architectures struggle to keep up with the growing demands of modern workloads that span transactional processing, real-time analytics, and AI-driven operations. These systems are often overprovisioned, siloed, or reliant on expensive cloud-native components, leading to ballooning spend and operational complexity.

In this talk, we’ll explore how rethinking database architecture can significantly reduce infrastructure costs while supporting the most critical, latency-sensitive workloads at enterprise scale. We’ll introduce a next-generation architecture that leverages disaggregated storage, including S3-compatible object stores, to achieve massive scalability and elasticity without the traditional price tag.

We'll walk through key design principles—such as separation of storage and compute, unified support for OLTP, OLAP, and vector workloads, and multi-tenant schema isolation—and how they enable consolidation of fragmented systems into a single, cost-efficient platform. You’ll also learn what trade-offs were considered in the move to object storage, and how this model maintains performance and reliability for Tier 0 use cases.

Attendees will leave with a deeper understanding of the architectural shifts enabling modern databases to scale smarter—not just bigger—and actionable insights for designing systems that are both high-impact and cost-conscious.