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CubeSandbox vs tensorlake

Hardware-isolated microVM sandboxes for AI agents — sub-60ms boot, <5MB overhead, E2B-compatible API, self-hosted on your own KVM nodes. — versus — Serverless platform for agent sandboxes: stateful Firecracker microVMs with snapshots, cloning, auto suspend/resume and network policy, plus fan-out orchestration functions. Python SDK and CLI.

The curated verdict

Same job, microVM sandboxes for agent code, but CubeSandbox is self-hosted on your KVM nodes with an E2B-compatible API while Tensorlake is a managed cloud.

CubeSandboxtensorlake
Stars13k1.0k
Forks1.2k148
LanguageRustPython
LicenseNOASSERTIONApache-2.0
Last activitytodaytoday
Topicssandboxes, agents, localsandboxes, agents
Curated connections174

CubeSandbox — the curator's take

Reach for CubeSandbox the moment your agents execute model-generated code and "just run it in Docker" stops feeling safe — it gives every tool call a disposable hardware-isolated microVM with E2B's SDK ergonomics, minus the SaaS bill, plus snapshot/rollback of any sandbox state. The catch: it's real infrastructure — you need KVM-capable Linux hosts and someone willing to operate them. Prototyping a single local agent? A container or E2B's hosted tier is less machinery. It's a runtime, not a framework — you still bring LangGraph/AutoGen/whatever on top.

tensorlake — the curator's take

Pick Tensorlake when you want hosted sandboxes that behave like real machines: stateful Firecracker VMs that suspend when idle and resume with memory intact, snapshot and clone mid-run, live-migrate, and take per-sandbox egress allowlists, plus a serverless function runtime to fan out agent work with each function in its own sandbox. This repo is the SDK and CLI; the runtime is Tensorlake Cloud, so it is an API key, not something you self-host. The filesystem benchmark against E2B, Modal and Daytona is their own. Need it on your own hardware? cubesandbox or agent-sandbox. Running one agent's code locally? A container may be enough.