The Un-Engineered Data Layer - Cloud Costs & AI ready data (Part 1)

Tessell presents its multi-cloud database platform as a way to manage Oracle, SQL Server, PostgreSQL, MySQL, and other database workloads across AWS, Google Cloud, and Azure with automated provisioning, lifecycle operations, security controls, high availability, disaster recovery, cost visibility, and data governance. In the first of a three-part series, Chief Strategy Officer Jeff Carter argues that conventional cloud migrations can create a “cloud performance trap,” in which organizations must purchase larger compute instances to obtain needed storage IOPS, thereby increasing core-based Oracle and SQL Server licensing costs, especially for high-availability deployments. He says Tessell’s NVMe-backed architecture separates storage performance from compute sizing while preserving durability through synchronization and logging, allowing high IOPS without scaling CPU capacity. The piece also contends that manually managed transactional databases can limit real-time AI and RAG applications because unpredictable agent queries may disrupt production systems; it recommends persistent, continuously synchronized read replicas using native database replication or change data capture to provide current operational data without placing AI workloads on primary databases.

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