Key takeaways
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88.4% of teams in the early integration phase lack visibility into their AI unit economics, even as costs continue to scale.
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“Fraud detection in production more than doubles once teams transition to self-hosted AI, as control over data unlocks workloads too sensitive for hosted APIs.”
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Centralized compute struggles to meet global latency targets; edge-based inference closes the gap.
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AI infrastructure adoption follows four phases: Integrate (API-first), Operate (in-house), Optimize (global SLAs), and Serverless (edge native), with each phase moving compute closer to users.
Frequently Asked Questions (FAQ)
Frequently Asked Questions (FAQ)
40.9% of API-first teams cite sensitive data leakage as their top security and governance risk.
Fraud and risk decisioning workloads in production more than double, jumping from 20.9% for API-first or centralized teams to 46.9% for self-hosted or on-premises teams.
84.4% of self-hosted teams target 99.9% or higher availability for their workloads.
42.2% of teams fall back to non-ML rules and heuristics, while 50% simply retry the same model, hoping it recovers.
80% of organizations serving AI at a global scale require sub-250 ms response times for their top use case.
60% of edge native teams need the architecture to support model rollback within 15 minutes, and 34% need it immediately.