Private AI vs cloud AI: choose architecture by data and workload
Compare private and cloud AI across data control, performance, cost, operations, models and hybrid architectures.
Cloud AI
Cloud models often provide rapid innovation, strong model quality and less infrastructure work, but data may leave your own environment depending on the service.
Private AI
Local or dedicated models increase control but require hardware, runtime and operational expertise.
Hybrid
Many companies benefit from a hybrid approach: sensitive workloads private, general tasks through approved cloud models.
Decision criteria
Data sensitivity, latency, cost, model requirements, auditability and operational resources should be evaluated together.
Define the next step
Use the configurator or one of the free tools to define a controlled starting point for a pilot.
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