Robotics customer reports low GPU switching cost
Voltage Park customer at robotics company on GPU pricing and robotics computing needs
Low switching cost means raw GPU cloud is still an infrastructure commodity, not a sticky software platform. For this robotics team, moving providers mostly means spinning up new machines, reinstalling its own stack, and reconnecting workloads, which can be done in one or two days because the provider is supplying compute, not deeply embedded workflow software. That makes price, availability, and contract terms the main retention levers.
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The team uses Voltage Park like rented hardware. It provisions its own clusters, installs its own software, and does not depend on higher level managed tooling. That keeps migration work small because most of the valuable setup lives in the customer’s own code and operations, not in the provider’s product surface.
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This matches the broader market split between raw GPU clouds and platforms like Fireworks or Together. Raw providers sell GPU hours and reservations. Higher layer platforms bundle serving, APIs, observability, and model access, which creates more workflow dependence but is less useful for custom robotics and scientific computing jobs.
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Even among infrastructure providers, the customer describes little loyalty and limited differentiation. Teams shop for discounts, capacity, and reliability, then move when a better deal appears. The notable exceptions are reserved capacity, guaranteed pricing, newer chips, and enterprise controls like SLAs and security processes, which can make leaving more operationally painful.
The market is likely to separate more clearly into cheap, fungible GPU supply on one side and higher lock-in software layers on the other. Infrastructure vendors that want stronger retention will need to add reservation products, enterprise assurances, and selective platform features, while customers with custom workloads will keep treating most GPU clouds as interchangeable compute sources.