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. 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. 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.