The cloud built from the ground up for robotics.
Train, fine-tune, and serve robot models on a low-latency GPU cloud. Stream an observation in, get an action back in under 30 ms.
Built on the stack your team already runs
Faster training. Faster inference. Simpler fleets.
One platform from data to deployed model, built so AI teams ship robots without an MLOps detour.
Faster inference than the edge
State-of-the-art inference speed. Reflex fused kernels beat torch.compile on H100s, so a robot observation round-trips faster than running the model on the bot.
Faster training
BetaFine-tune pi0.5, ACT, or your own VLA on managed GPUs. The same fused kernels that speed inference cut training step time too, so you pay for seconds, not nodes.
One deploy, every robot
Push a model once and it rolls out to your whole fleet over a single WebSocket. No SSH, no flashing, no drift between robots.
Three calls. That's the integration.
One WebSocket. Pick your model and LoRA, stream observations, execute actions. No serving stack to run.
01import reflex02 03@reflex.policy(04 model="pi0.7-flash",05 lora="pick-and-place",06 cameras=["wrist", "scene"], hz=50,07)08class Controller:09 @reflex.observation10 def observe(self): return robot.observe()11 @reflex.action12 def execute(self, action): robot.execute(action)13 14Controller().run()Your robot's brain, one network hop away.
Robots stream observations to a colocated GPU pool. Reflex runs the model and streams actions back, inside the control loop's latency budget.
From the factory floor to the summit of Everest.
Reflex serves frontier models to any robot on any network. Same API, same latency budget, anywhere Starlink reaches.
Take it for a spin.
Tell the arm what to do. Be gentle — robots have feelings too.
- “pack the container”
- “fold the towel”
- “pick up the cube”
- “move blocks to spell AI2”
- “unpack the container”
Ship the model.
Skip the infrastructure.
Train, serve, and roll out robot models on one low-latency cloud. Start free, scale per second.