Why the era of relying on dozens of “purpose-built” databases is finally coming to an end

The New Stack

by Tim Rottach

February 20, 2026

AI-Generated Deep Dive Summary

The era of relying on numerous "purpose-built" databases is gradually coming to an end as businesses recognize the need for more efficient, scalable, and cost-effective data management solutions. The rise of artificial intelligence (AI) across industries has made data platforms a critical component for operational success. Traditional database architectures, which often involve managing multiple systems tailored for specific tasks, are becoming increasingly complex and costly to maintain. This complexity is compounded by the demands of AI integration, real-time decision-making, and scalability requirements. One major challenge businesses face is scaling their infrastructure without incurring excessive costs. The shift toward cloud adoption, combined with growing data volumes and the need for advanced AI capabilities, often leads to overprovisioning or inefficient resource allocation. Many organizations struggle to find a data platform that can scale effectively while keeping expenses in check. Additionally, the need for sub-millisecond response times has become non-negotiable for applications requiring real-time processing, such as fraud detection or high-frequency trading. Traditional systems, which often rely on slower disk-based storage, fall short of these performance expectations. Another issue is the complexity introduced by disparate data systems. Many businesses operate with a patchwork of tools and databases that are difficult to manage, leading to increased costs and inefficiencies. Integrating AI into this fragmented landscape further complicates operations, as it requires seamless interaction across siloed technologies. This lack of alignment between development goals and operational needs hampers scalability and innovation. For businesses aiming to thrive in an AI-driven world, adopting modern data strategies is essential. Memory-first architectures, which prioritize in-memory processing, offer a solution by eliminating disk bottlenecks and enabling sub-millisecond response times. These systems not only improve performance but also reduce infrastructure complexity, making them ideal for applications that require real-time decision-making. From a DevOps perspective, the shift toward unified data platforms aligns with the need for agility, scalability, and cost-efficiency in cloud environments. By addressing these challenges, businesses can streamline their operations, reduce costs, and better position themselves to leverage AI-driven innovations. The transition away from purpose-built databases represents not just a technological evolution but also a strategic opportunity for organizations to enhance their operational capabilities and deliver superior outcomes.

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Originally published on The New Stack on 2/20/2026