The enterprise voice AI split: Why architecture — not model quality — defines your compliance posture

For the past year, enterprise decision-makers have faced a rigid architectural trade-off in voice AI: adopt a "Native" speech-to-speech (S2S) model for speed and emotional fidelity, or stick with a "Modular" stack for control and auditability. That binary choice has evolved into distinct market segmentation, driven by two simultaneous forces reshaping the landscape.What was once a performance decision has become a governance and compliance decision, as voice agents move from pilots into regulated, customer-facing workflows.On one side, Google has commoditized the "raw intelligence" layer. With the release of Gemini 2.5 Flash and now Gemini 3.0 Flash, Google has positioned itself as the high-volume utility provider with pricing that makes voice automation economically viable for workflows previously too cheap to justify. OpenAI responded in August with a 20% price cut on its Realtime API, narrowing the gap with Gemini to roughly 2x — still meaningful, but n