Tensorlake is the sandbox-native cloud for AI agents — a compute platform for securely running untrusted, LLM-generated code in isolated sandboxes and orchestrating agentic applications at scale.

Each Tensorlake sandbox is an isolated Firecracker microVM, so untrusted or LLM-generated code runs in a hardware-virtualized environment separate from your infrastructure and other sandboxes. You can create one in a few lines with the Python SDK, the TypeScript SDK, or the CLI.

Tensorlake is built for heavy filesystem I/O, sub-second startup, and large-scale fan-out. In SQLite benchmarks (2 vCPUs, 4 GB RAM) it completes in 2.45s versus E2B (3.92s), Modal (4.66s), and Daytona (5.51s), and it supports snapshots, auto suspend/resume, live migration, and up to 5 million sandboxes per project.

Yes. Snapshot a running sandbox at any point to capture both memory and filesystem state, then create a new sandbox from that snapshot to pick up exactly where you left off. Sandboxes also auto-suspend when idle and resume in under a second without losing state.

Sandboxes are created in under a second via Lattice, a dynamic cluster scheduler. For even faster starts, sandbox pools keep warm containers ready to claim instantly.

Yes. Spin up a sandbox as an isolated execution environment for an agent's tools or generated code, run commands or long-running processes inside it, read and write files, and terminate it when done — all from the Python SDK, the TypeScript SDK, or the CLI.

Tensorlake provides a Python SDK, a TypeScript SDK, and a CLI, plus an HTTP API for invoking orchestration applications.

Yes. Tensorlake supports bring-your-own-cloud (BYOC) and on-premise deployments for cost control, security, and compliance, in addition to the managed cloud.

Sign up at cloud.tensorlake.ai, install the SDK, set your TENSORLAKE_API_KEY, and create your first sandbox. See the documentation at docs.tensorlake.ai for full guides.