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 no longer ...