Scale your AI workloads faster and more efficiently with GKE Pod snapshots

When running modern AI workloads, there’s often a conflict between performance and cost. Workloads like large language models (LLMs) load massive files, and may serve thousands of AI agents that need to execute code instantly. If each component is starting “cold” with a full data-load process, all this provisioning takes time, often forcing organizations to overprovision their infrastructure just to meet scaling requirements.