Our sandboxes have file systems which are close to SSDs in performance. This enables agnets to process data and compile code faster
The data plane can be deployed in VPCs. Data doesn't leave your network. You use your own reserved instances and pricing.
We have designed a state of the art cluster scheduler to enable users to run upward of 100k+ sandboxes in parallel.
Sandboxes spread across AWS, GCP, Azure, or bare metal automatically. We ensure the CPU family of your sandbox is consistent so there is no variation in performance.
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.
Execution Environment for 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.
Sandboxes Powered by
a state-of-the-art Scheduler
We built a scehduler from the ground up to spin up 100 sandboxes/second on a single host and can bring up 1000s of sandboxes/second on large clusters in parallel. This is 10-17x higher than Kubernetes.
The scheduler uses a copy-on-write memory to keep cluster state in memroy for low latency scheduling
The scheduler understands tenancy of workloads and can schedule workloads on both dedicated and shared clusters.
The scheduler is fully event driven to respond to cluster topology changes faster or to allocate a new sandbox request.
We use Firecracker MicroVM and CloudHypervisor depending on the workload and hardware resource asks of sandboxes.
Built for workloads that hit disks
Bring Tensorlake Into Your Cloud
Tensorlake data plane can deploy in 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.”
