Editor’s note: Today we hear from SeaVerse, a gaming startup from SeaArt that is building a platform for playable AI experiences, where users can open lightweight games, character chats, and interactive apps, or create their own experiences from a prompt. To support that creative loop, SeaVerse needed infrastructure that could run dynamic, multi-tenant sandbox workloads with strong isolation, low latency, better observability, and more flexible costs. Google Kubernetes Engine (GKE) and GKE Agent Sandbox gave SeaVerse the managed foundation from which to execute these AI workloads, helping the team reduce their infrastructure costs by up to 60%, while giving creators a faster path from idea to playable experiences. Read on to learn more.
What if AI were a playground? Welcome to SeaVerse, a creation-first platform for playable AI experiences. Here, an AI creation can be as peaceful as drawing a path for a snake to follow, or as chaotic as a music-backed stickman simulation. Some people come to play lightweight games. Others come to chat with AI characters, try interactive apps, create visual patterns, share what they made, or remix an idea into something new.
We built SeaVerse around a simple promise: Every experience should feel immediate and easy to share. A creator should be able to describe an idea in plain language, refine the result, and publish it in moments, without a traditional coding workflow.
Delivering that simplicity requires serious infrastructure. Every creation that users make moves through the same chain: generate, run, preview, debug, publish, remix. If any part of that chain is slow, unstable, or poorly isolated, users feel it immediately. That’s why we turned to GKE and GKE Agent Sandbox.
The infrastructure challenge of instant interaction
What looks effortless to a user is anything but on our end. Every creation on SeaVerse runs as a distinct workload and is expected to behave reliably from the first interaction.
Because each workload runs in its own environment, we needed clear security boundaries between users, creations, and sandboxes. But overly strict isolation could slow the very creative loop we were trying to protect, and when something went wrong, diagnosing it was costly. Our engineers had to trace problems across multiple parts of the execution chain with little visibility into what was happening inside the environment.
We explored existing sandbox approaches, but needed deeper kernel-level isolation and native observability at scale to support fast diagnosis across multi-tenant environments. Something had to change.
Building on GKE and GKE Agent Sandbox
We chose GKE because we needed a reliable, secure way to operate Kubernetes without turning our engineering team into a cluster maintenance team. GKE brought together the proven ecosystem and operational tooling we needed, freeing us to focus on building the platform rather than managing the infrastructure beneath it.
As a Kubernetes primitive designed for agent code execution and computer use, GKE Agent Sandbox addressed our requirement for strong isolation, enforcing strong security boundaries without slowing down the creation experience. By utilizing GKE Agent Sandbox with Kata Containers+Cloudhypervisor (microVM), we’ve achieved the perfect balance of multi-cloud flexibility and robust security, option to switch isolation runtime between microVM and gVisor, running our AI sandboxes safely. GKE empowers us to scale toward our long-term vision of supporting over a million sandboxes. Built on gVisor, it provides kernel-level isolation for dynamic sandbox workloads while preserving the Kubernetes orchestration model, so that they can be managed through the same scheduling, monitoring, and operations as the rest of the cluster.
With SeaVerse, users can generate interactive experiences from a single prompt. After an experience is generated, GKE Agent Sandbox supports the run, test, integration, and verification steps needed to make it ready to preview, refine, and publish. At general availability, it supports allocating up to 300 sandboxes per second, per cluster, with 90% of allocations completing in 200 milliseconds. Together, GKE and GKE Agent Sandbox gave us a reliable foundation for AI-generated interactive workloads that helped keep our team focused on the product experience.
From black box to glass box
Before GKE Agent Sandbox, a failed sandbox workload could feel like flying blind. We could often see that something had gone wrong, but didn’t have enough runtime status, metrics, or failure signals to understand why.
Now, Google Cloud’s native logging and monitoring reach directly into those sandboxed environments, giving us a clearer view of workload behavior, faster issue resolution, and a stronger foundation for managing multi-tenant workloads.
That visibility matters to developers, but it also matters to the platform’s users: A creator never sees the logs, the cluster, or the orchestration layer. They see whether an experience opens quickly, whether it responds when they draw, click, chat, or share, and whether they can keep building without friction.
Flexibility that translates to savings
GKE Agent Sandbox also changed how we think about cost. Previously, running secure sandboxed environments meant stronger dependencies on specific server types, which limited how precisely we could match resources to each workload. With GKE Agent Sandbox, we can run secure, isolated workloads on appropriately sized cloud VMs. This gives us greater flexibility in resource allocation and helped us cut our infrastructure costs by up to 60%.
That same flexibility extended to storage. Not all SeaVerse creations are built in a single session. Some evolve over time as creators return to refine them, build on earlier ideas, or invite others to remix what they’ve made. Our previous architecture didn’t support the persistent file-system capabilities those more complex use cases demanded, but that gap is gone now. We can attach persistent storage where workloads require it while maintaining the isolation boundaries that multi-tenant AI experiences need. For creators, that means experiences that are fast to open and easier to refine, revisit, and build on over time.
The next remix
Supporting creations that can evolve and deepen is central to what we’re building. It’s still early in what playable AI can become. As the platform grows, we need to keep strengthening what matters most: stability, observability, elastic scaling, and cost efficiency, all in service of a creator experience that stays fast, reliable, and expressive.
We’re also exploring additional Google Cloud tools to support smarter analytics and creation assistance. Gemini and agent models could help operators and creators better understand how experiences perform. BigQuery AI and ML capabilities can support use cases such as churn prediction, LTV and ROI prediction, and user segmentation. Multimodal tools such as Imagen and Veo on Gemini Enterprise Agent Platform open up new possibilities for material analysis, creative generation, and AI interactive content production.
Our goal is to make AI experiences feel immediate, expressive, and connected. With GKE and GKE Agent Sandbox, we have a stronger foundation for the next generation of playable AI.