Compile code, run databases, process 5GB files. Real-time cloning to fork a sandbox into multiple copies all the same state.
The data plane deploys directly in your cloud. Data doesn't leave your network. You use your own reserved instances and pricing.
Spin up 200 sandboxes per second. Run 100K at once. Cold start under a second.
Sandboxes spread across AWS, GCP, Hetzner, or bare metal automatically. Consistent CPU bundles across the fleet so RL training runs don't get variance from mismatched hardware.
Full state preserved on suspension or crash. Resume where you left off, debug what happened, or fork into a new sandbox. Auto-suspend watches memory access so you only pay when something is actually running.
Isolated environments for running agents & tools
Run agent harnesses, code interpreters, browser helpers, and tool-calling agents inside isolated, stateful sandboxes — each with its own filesystem, shell, packages, and processes. Keep the agent in the sandbox for long-running sessions, or spin up separate sandboxes only when a tool needs risky or heavy execution.
Infrastructure for RL Environments and add on more line
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.
Why we built our own scheduler
Unlike other providers that use Kubernetes, our scheduler is optimized for rapid sandbox creation and teardown. That's why we achieve 200/sec, while others do 5-10.
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.
Full traces of every function and tool call — with logs, timing, and structured execution paths.
Secure by default for PHI, PII, and sensitive documents.
Built for workloads that hit disks
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.
Tensorlake is the Agentic Compute Runtime the durable serverless platform that runs Agents at scale.
“With Tensorlake, we've been able to handle complex document parsing and data formats that many other providers don't support natively, at a throughput that significantly improves our application's UX. Beyond the technology, the team's responsiveness stands out, they quickly iterate on our feedback and continuously expand the model's capabilities.”
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"At SIXT, we're building AI-powered experiences for millions of customers while managing the complexity of enterprise-scale data. TensorLake gives us the foundation we need—reliable document ingestion that runs securely in our VPC to power our generative AI initiatives."
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“Tensorlake enabled us to avoid building and operating an in-house OCR pipeline by providing a robust, scalable OCR and document ingestion layer with excellent accuracy and feature coverage. Ongoing improvements to the platform, combined with strong technical support, make it a dependable foundation for our scientific document workflows.”
"For BindHQ customers, the integration with Tensorlake represents a shift from manual data handling to intelligent automation, helping insurance businesses operate with greater precision, and responsiveness across a variety of transactions"
“Tensorlake let us ship faster and stay reliable from day one. Complex stateful AI workloads that used to require serious infra engineering are now just long-running functions. As we scale, that means we can stay lean—building product, not managing infrastructure.”
