Stateful Sandboxes Compute for Agents
Run agents and tool calls in isolated sandboxes, keep state between runs, wake them on demand, and give them the real software stack they need to do useful work.
Sandboxes for Running Agent Harnesses
Run the agent itself inside an isolated, stateful computer instead of on the app server. This is the sandbox mode for browsing agents, research harnesses, and long-running sessions that need files, bash, packages, and working state.
Give the agent its own filesystem, shell, packages, and processes instead of sharing the app server runtime.
Sandbox sleeps on inactivity, wakes instantly when invoked
Near-SSD speed in a VM, 2x faster than Vercel, 5x faster than E2B
Near-SSD speed in a VM, 2x faster than Vercel, 5x faster than E2B
Isolated Execution Environments for Running Tools
Keep the agent harness outside the sandbox and create isolated sandboxes only when a tool needs risky or heavy execution. This is the pattern for code interpreters, browser helpers, and tool-calling agents that should not run untrusted code inside the harness itself.
Execute code, browsers, or system tasks in a separate sandbox so the main agent never shares its runtime.
Predictable throughput means fresh sessions spin up immediately, even when a thousand others are mid-task.
Run generated code safely. LLMs write code to compute answers, transform data, query databases. Every session gets its own sandbox so untrusted code can't touch system integrity or leak data across sessions.
Infrastructure for RL Environments
Snapshot a prepared environment and reuse it across workers.
Keep files, packages, processes, and seeds consistent across runs.
Snapshot a prepared environment and reuse it across workers.
Choose the CPU, memory, GPU, and image each environment needs.
Sandbox-Native Orchestration for Agents
Once sandbox usage turns into a real application, Orchestrate is the layer that coordinates it. This is where you add application endpoints, durability, fan-out, retries, and application-level observability on top of sandbox execution.
Expose sandbox-backed workflows as callable applications instead of stitching together raw VM APIs.
Predictable throughput means fresh sessions spin up immediately, even when a thousand others are mid-task.
Run generated code safely. LLMs write code to compute answers, transform data, query databases. Every session gets its own sandbox so untrusted code can't touch system integrity or leak data across sessions.
Run generated code safely. LLMs write code to compute answers, transform data, query databases. Every session gets its own sandbox so untrusted code can't touch system integrity or leak data across sessions.
Run generated code safely. LLMs write code to compute answers, transform data, query databases. Every session gets its own sandbox so untrusted code can't touch system integrity or leak data across sessions.
Tensorlake is the Agentic Compute Runtime the durable serverless platform that runs Agents at scale.
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Tensorlake built for world-class enterprises
Bring Tensorlake Into Your Cloud
Run sandboxes and applications in your own cloud or private environment when you need lower egress, stricter network boundaries, dedicated capacity, or more predictable performance.
Keep code and data inside your preferred cloud boundary when shared SaaS deployment is no longer acceptable.
Keep compute closer to your data and tighten the runtime behavior for latency-sensitive agent workloads.
Move from usage-based hosted infrastructure to capacity you can plan, reserve, and operate more predictably.
Security Built for Agentic and AI Data Workflows
Full traces of every function and tool call — with logs, timing, and structured execution paths.
Tool calls run in isolated sandboxes, making them safe for LLM-generated code.
Each agent harness executes inside an isolated sandbox to keep sessions safe and independent.
Each project’s data lives in its own isolated bucket with full audit trails and strong RBAC controls.
