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The inference trap: How cloud providers are eating your AI margins
If you’re unsure about the load of different AI workloads, start with the cloud and keep a close eye on the associated costs by tagging every resource with the responsible team.
The fast and easy access via a service model ensures a seamless start, paving the way to get the project off the ground and do rapid experimentation without the huge up-front capital expenditure of acquiring specialized GPUs. Using the built-in scaling and experimentation frameworks provided by most cloud platforms helps reduce the time between milestones,” Rohan Sarin, who leads voice AI product at Speechmatics, told VentureBeat. Hybrid setups also help reduce latency for time-sensitive AI applications and enable better compliance, particularly for teams operating in highly regulated industries like finance, healthcare, and education — where data residency and governance are non-negotiable.
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