How to run millions of concurrent AI sandboxes

Operating millions of concurrent AI sandboxes requires distinguishing live resource-consuming environments from start volume, paused workspaces, and total lifecycle activity, then designing for each measure separately. The proposed approach uses bounded regional cells rather than a single Kubernetes cluster, with global admission handling identity, quotas, policy, and routing while local schedulers place workloads and asynchronous controllers manage retry-safe creation, pausing, recovery, cleanup, and deletion. Capacity planning should account for memory, CPU, storage, networking, burst rates, failover reserves, and end-to-end time to interactive rather than VM boot time alone. Security relies on selecting isolation appropriate to workload trust, such as containers, gVisor, or hardware-virtualized microVMs, alongside default-deny networking, short-lived credentials, resource limits, external durable storage, and independent mechanisms to terminate or quarantine workloads. The text presents Northflank as a platform offering sandbox lifecycle APIs, microVM or gVisor isolation, managed or bring-your-own-cloud deployment, persistence, networking, governance features, and agent operations, and cites its reported experience operating millions of microVMs monthly and reaching 100,000 cold-start CPU sandboxes in 24 seconds in a 2026 burst test.

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