AI’s easy on-ramp has become a costly exit problem for enterprises, says Red Hat
Enterprises thought AI was cheap. Now they're drowning in inference costs.
Why it matters
As AI moves from proof-of-concept to production scale, companies are hitting a hard wall on infrastructure costs and governance complexity—forcing a strategic rethink of cloud architecture and vendor lock-in.
The key facts
9 to knowInference scaling costs forcing infrastructure redesign
Horizontal cloud model emerging as strategic requirement
Hybrid cloud governance becoming central to AI deployment strategy
Red Hat positioning open standards as solution to vendor lock-in
Shift from pilot economics to production economics creating operational pressure
Enterprises moving AI beyond pilot stage facing cost and complexity challenges at inference scale
Open hybrid cloud model positioned as solution for horizontal workload distribution
Infrastructure governance and sourcing emerging as critical pain points
Red Hat positioning multi-cloud strategy as answer to AI scaling economics
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Publisher excerpt: As enterprises push AI beyond the pilot stage, the cost and complexity of running inference at scale are forcing a fundamental rethink of how infrastructure is designed, governed and sourced, putting horizontal cloud — one shared foundation for running workloads across the enterprise — at the…