Optimizing agentic workflows and sandboxes
GKE Pod snapshots also provide distinct advantages for agentic workflows where agents delegate code execution and computer use to isolated sandboxes. Isolating untrusted, LLM-generated code and commands means one sandbox per user or discrete workflow. In these scenarios, both startup latency and idle sandboxes can result in significant overprovisioning and underutilization.
Pod snapshots addresses both of these challenges:
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To improve startup latency, a snapshot can be captured once of the initial agent sandbox environment, and later used to quickly initialize new sandboxes.
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To reduce idle sandboxes, a sandbox can be suspended when idle, capturing its entire compute resources. Later it can be resumed nearly instantly when the environment is needed.
This approach is showing significant success by our customers. For instance, Retake, an AI-powered photo editing platform built by Codeway, faced a significant performance bottleneck with its GPU workloads. By adopting Pod snapshots, they were able to replace a complex custom caching layer and reduce startup time to seconds.
“At Retake, serving personalized models to millions of users requires a massive, unified pipeline for both fine-tuning training and real-time inference on A3 H100 GPUs. We initially engineered a complex custom caching layer for compiled artifacts, which reduced startup time to 1 minute. However, this solution added significant maintenance overhead and still limited our ability to autoscale aggressively. We resolved this by replacing that complexity with GKE Pod snapshots, slashing startup latency to just 8 seconds. By eliminating the initialization penalty, we can now dynamically spin up H100s for specific fine-tuning or inference jobs instantly and shut them down immediately after, drastically reducing idle GPU costs and simplifying our codebase.” – Ahmet Furkan Çomak, Lead DevOps Engineer, Codeway
Flexible configuration for any workload
We designed Pod snapshots to improve startup performance and fit naturally into existing Kubernetes workflows. Adopting Pod snapshots to your workload is easy: just define a new declarative policy using Pod snapshot CRDs. The policy allows you to define which Pods to snapshot and where to store the data, and handles the end-to-end storage lifecycle and management.
You can take snapshots at any stage of the workload — either at workload startup using a workload signal, or during the lifecycle of the Pod using an on-demand trigger. You can further control storage and restore behavior, setting snapshots retention for cost optimization, choosing between the default behaviour of restoring from the last taken snapshot, or specifying an explicit snapshot during a new Pod deployment.
While the primary use cases for GKE Pod snapshots are AI inference and agent sandboxes, this feature is workload-agnostic. You can use it to speed up any application with a long initialization phase, such as complex Java applications, game servers, or legacy monoliths.
Get started
You can begin optimizing your startup latency today with GKE Pod snapshots. Check out the documentation to learn how to get started and we look forward to your feedback.
