Over the past year, enterprise decision-makers have been faced with a challenging architectural trade-off in voice AI. The choice between adopting a “Native” speech-to-speech (S2S) model for speed and emotional fidelity or sticking with a “Modular” stack for control and auditability has evolved into distinct market segmentation. This shift has been driven by two forces reshaping the landscape: the need for governance and compliance as voice agents move into regulated, customer-facing workflows.
Google has become a dominant player in the voice AI market by commoditizing the “raw intelligence” layer with the release of Gemini 2.5 Flash and Gemini 3.0 Flash. This has positioned Google as a high-volume utility provider with pricing that makes voice automation economically viable for workflows that were previously too cheap to justify. OpenAI has responded with a 20% price cut on its Realtime API, narrowing the pricing gap to roughly 2x, making it a more competitive option in the market.
On the other side, a new “Unified” modular architecture is emerging. Companies like Together AI are co-locating the disparate components of a voice stack – transcription, reasoning, and synthesis – to address latency issues that have hampered modular designs in the past. This approach delivers native-like speed while retaining the audit trails and intervention points that regulated industries require.
These forces are collapsing the historical trade-off between speed and control in enterprise voice systems. For enterprise executives, the strategic choice is now between a cost-efficient, generalized utility model and a domain-specific, vertically integrated stack that supports compliance requirements.
There are three distinct architectures that have emerged in the enterprise voice AI market, each optimized for different trade-offs between speed, control, and cost. S2S models like Google’s Gemini Live and OpenAI’s Realtime API achieve latency in the 200 to 300ms range, closely mimicking human response times. Traditional chained pipelines have aggregate roundtrip latencies that frequently exceed 500ms, while the Unified infrastructure from companies like Together AI collapses total latency to sub-500ms.
The difference between a successful voice interaction and an abandoned call often comes down to milliseconds. Metrics like Time to first token (TTFT), Word Error Rate (WER), and Real-Time Factor (RTF) define production readiness and user tolerance.
For regulated industries, the modular approach offers control and compliance that native S2S models lack. The text layer between transcription and synthesis enables stateful interventions like PII redaction, memory injection, and pronunciation authority that are critical for compliance and governance.
The enterprise voice AI market has fragmented into distinct competitive tiers, with infrastructure providers like Deepgram and AssemblyAI competing on transcription speed and accuracy, model providers like Google and OpenAI competing on price-performance, and orchestration platforms like Vapi, Retell AI, and Bland AI competing on ease of implementation and feature completeness.
In conclusion, the choice of architecture for enterprise voice AI systems is crucial as it will determine whether voice agents can operate in regulated environments. High-volume utility workflows may benefit from Google’s Gemini Flash models, while complex, regulated workflows may require the control and auditability offered by the modular stack or Unified infrastructure providers like Together AI. Ultimately, the architecture chosen will have significant implications for the success of voice AI implementations in enterprise settings.
