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Execution
Set a parallelism budget. Managed pools provision machines for the run; self-hosted hosts dial in when online.
Fleets, fan-out, isolation, schedules, queryable results. The graph decides what runs. The engine decides where.
Managed pools scale to the budget. Enrolled hosts take jobs when online.
One node fans a file into parallel jobs.
Isolated while it executes.
A step starts the moment its inputs are ready.
Enroll a host with one install script. It dials out; nothing opens inbound.
enterprise · self-hosted
Weekly sweeps or on demand. The fleet takes it.
Tool output lands in typed tables you filter and export.
One run can burst across 500 machines. More machines, shorter runs; the canvas never changes.
Burst to 500
One run holds up to 500 machines. They spin up for the spike and vanish when the queue drains.
Scale buys time
More machines, shorter runs. The parallelism budget is a dial on execution time.
Distribute
A distributed node shards a FILE or FOLDER across the fleet. One wordlist, one sweep.
Terabytes of data
Artifacts stream to storage as machines finish. A single sweep can move terabytes, 100 GB per upload.
The whole machine
One job per machine. The tool gets the cores and the memory.
The conversation lives in your browser. The work runs here, in an isolated sandbox on a machine of its own.
One session, one machine, nobody else on the box. Code runs as an unprivileged user, and files, processes, and network stay scoped to the vault that opened it.
Machines wait in warm pools, so a sandbox is ready in seconds. Inside sits a real shell with Node and Python preinstalled. Long commands run detached and survive a closed tab; reattach and the log picks up where it left off.
When the agent ships a dashboard or a report app, the sandbox serves it directly. Every exposed port gets its own preview URL. The result is a link you open, not a file you download.
Workflows, schedules, the CLI, and fleets submit the same runs. Pick the next surface.
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