The Container paradox: Why the Inference Cloud Demands a “Decoupled” Database

The Container paradox: Why the Inference Cloud Demands a “Decoupled” Database The Inference Cloud demands a new standard The “stateful” friction Why Managed Kubernetes + Managed Databases (the “attach” architecture) are the cheat code for the Inference Cloud Focus on your core, not the complex “plumbing” Ready to simplify your stack? About the author(s) Try DigitalOcean for free By Kang Xie , Nicole Ghalwash , and Zach Peirce Published: February 10, 2026 5 min read Kubernetes has won the cloud-native war for a reason: it’s one of, if not the most powerful tool we have for scaling applications and ensuring they stay up when unexpected things happen. But as we move into the era of the Inference Cloud, we’ve fallen into a trap. We’ve become so enamored with “everything-as-code” that we’re forcing our most sensitive data inside the cluster. At DigitalOcean, we see thousands of enterprises building on DigitalOcean Kubernetes (DOKS). The most successful ones have realized a counter-intuitive truth: To manage your Kubernetes clusters effectively, you must stop managing your databases inside them. Just because you can run your database in a container, doesn’t mean you should. In 2026, the stakes have changed. We’re no longer just scaling web services, we’re scaling data-intensive inference workflows. AI-driven applications require massive bursts of compute and near-instant access to vector data, metadata, and user context. When your database competes for resources inside your Kubernetes cluster, your inference latency suffers. That’s why DigitalOcean Managed Kubernetes and DigitalOcean Managed Databases (fully-managed PostgreSQL, MySQL, MongoDB, Caching for Valkey, and OpenSearch database services) are the two essential pillars of our inference cloud, working to solve this issue. Managed Kubernetes acts as the execution layer, while Managed Databases acts as the memory layer.