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 insurmountable.

On the other side, a new "Unified" modular architecture is emerging. By physically co-locating the disparate components of a voice stack-transcription, reasoning and synthesis-providers like Together AI are addressing the latency issues that previously hampered modular designs. This architectural counter-attack delivers native-like speed while retaining the audit trails and intervention points that regulated industries require.

Together, these forces are collapsing the historical trade-off between speed and control in enterprise voice systems.

For enterprise executives, the question is no longer just about model performance. It's a strategic choice between a cost-efficient, generalized utility model and a domain-specific, vertically integrated stack that supports compliance requirements — including whether voice agents can be deployed at scale without introducing audit gaps, regulatory risk, or downstream liability.

Understanding the three architectural paths

These architectural differences are not academic; they directly shape latency, auditability, and the ability to intervene in live voice interactions.

The enterprise voice AI market has consolidated around three distinct architectures, each optimized for different trade-offs between speed, control, and cost. S2S models — including Google's Gemini Live and OpenAI's Realtime API — process audio inputs natively to preserve paralinguistic signals like tone and hesitation. But contrary to popular belief, these aren't true end-to-end speech models. They operate as what the industry calls "Half-Cascades": Audio understanding happens natively, but the model still performs text-based reasoning before synthesizing speech output. This hybrid approach achieves latency in the 200 to 300ms range, closely mimicking human response times where pauses beyond 200ms become perceptible and feel unnatural. The trade-off is that these intermediate reasoning steps remain opaque to enterprises, limiting auditability and policy enforcement.

Traditional chained pipelines represent the opposite extreme. These modular stacks follow a three-step relay: Speech-to-text engines like Deepgram's Nova-3 or AssemblyAI's Universal-Streaming transcribe audio into text, an LLM generates a response, and text-to-speech providers like ElevenLabs or Cartesia's Sonic synthesize the output. Each handoff introduces network transmission time plus processing overhead. While individual components have optimized their processing times to sub-300ms, the aggregate roundtrip latency frequently exceeds 500ms, triggering "barge-in" collisions where users interrupt because they assume the agent hasn't heard them. 

Unified infrastructure represents the architectural counter-attack from modular vendors. Together AI physically co-locates STT (Whisper Turbo), LLM (Llama/Mixtral), and TTS models (Rime, Cartesia) on the same GPU clusters. Data moves between components via high-speed memory interconnects rather than the public internet, collapsing total latency to sub-500ms while retaining the modular separation that enterprises require for compliance. Together AI benchmarks TTS latency at approximately 225ms using Mist v2, leaving sufficient headroom for transcription and reasoning within the 500ms budget that defines natural conversation. This architecture delivers the speed of a native model with the control surface of a modular stack — which can be the "Goldilocks" solution that addresses both performance and governance requirements simultaneously.

The trade-off is increased operational complexity compared to fully managed native systems, but for regulated enterprises that complexity often maps directly to required control.

Why latency determines user tolerance — and the metrics that prove it

The difference between a successful voice interaction and an abandoned call often comes down to milliseconds. A single extra second of delay can cut user satisfaction by 16%. 

Three technical metrics define production readiness:

Time to first token (TTFT) measures the delay from the end of user speech to the start of the agent's response. Human conversation tolerates roughly 200ms gaps; anything longer feels robotic. Native S2S models achieve 200 to 300ms, while modular stacks must optimize aggressively to stay under 500ms.

Word Error Rate (WER) measures transcription accuracy. Deepgram’s Nova-3 delivers 53.4% lower WER for streaming, while AssemblyAI's Universal-Streaming claims 41% faster word emission latency. A single transcription error — "billing" misheard as "building" — corrupts the entire downstream reasoning chain.

Real-Time Factor (RTF) measures whether the system processes speech faster than users speak. An RTF below 1.0 is mandatory to prevent lag accumulation. Whisper Turbo runs 5.4x faster than Whisper Large v3, making sub-1.0 RTF achievable at scale without proprietary APIs.

The modular advantage: Control and compliance

For regulated industries like healthcare and finance, "cheap" and "fast" are secondary to governance. Native S2S models function as "black boxes," making it difficult to audit what the model processed before responding. Without visibility into the intermediate steps, enterprises can't verify that sensitive data was properly handled or that the agent followed required protocols. These controls are difficult — and in some cases impossible — to implement inside opaque, end-to-end speech systems.

The modular approach, on the other hand, maintains a text layer between transcription and synthesis, enabling stateful interventions impossible with end-to-end audio processing. Some use cases include:

• PII redaction allows compliance engines to scan intermediate text and strip out credit card numbers, patient names, or Social Security numbers before they enter the reasoning model. Retell AI's automatic redaction of sensitive personal data from transcripts significantly lowers compliance risk — a feature that Vapi does not natively offer.

• Memory injection lets enterprises inject domain knowledge or user history into the prompt context before the LLM generates a response, transforming agents from transactional tools into relationship-based systems. 

• Pronunciation authority becomes critical in regulated industries where mispronouncing a drug name or financial term creates liability. Rime's Mist v2 focuses on deterministic pronunciation, allowing enterprises to define pronunciation dictionaries that are rigorously adhered to across millions of calls — a capability that native S2S models struggle to guarantee.

Architecture comparison matrix

The table below summarizes how each architecture optimizes for a different definition of “production-ready.”

The vendor ecosystem: Who's winning where

The enterprise voice AI landscape has fragmented into distinct competitive tiers, each serving different segments with minimal overlap. Infrastructure providers like Deepgram and AssemblyAI compete on transcription speed and accuracy, with Deepgram claiming 40x faster inference than standard cloud services and AssemblyAI countering with better accuracy and speed. 

Model providers Google and OpenAI compete on price-performance with dramatically different strategies. Google's utility positioning makes it the default for high-volume, low-margin workflows, whereas OpenAI defends the premium tier with improved instruction following (30.5% on MultiChallenge benchmark) and enhanced function calling (66.5% on ComplexFuncBench). The gap has narrowed from 15x to 4x in pricing, but OpenAI maintains its edge in emotional expressivity and conversational fluidity – qualities that justify premium pricing for mission-critical interactions.

Orchestration platforms Vapi, Retell AI, and Bland AI compete on implementation ease and feature completeness. Vapi's developer-first approach appeals to technical teams wanting granular control, while Retell's compliance focus (HIPAA, automatic PII redaction) makes it the default for regulated industries. Bland's managed service model targets operations teams wanting "set and forget" scalability at the cost of flexibility.

Unified infrastructure providers like Together AI represent the most significant architectural evolution, collapsing the modular stack into a single offering that delivers native-like latency while retaining component-level control. By co-locating STT, LLM, and TTS on the shared GPU clusters, Together AI achieves sub-500ms total latency with ~225ms for TTS generation using Mist v2.

The bottom line

The market has moved beyond choosing between "smart" and "fast." Enterprises must now map their specific requirements — compliance posture, latency tolerance, cost constraints — to the architecture that supports them. For high-volume utility workflows involving routine, low-risk interactions, Google Gemini 2.5 Flash offers unbeatable price-to-performance at approximately 2 cents per minute. For workflows requiring sophisticated reasoning without breaking the budget, Gemini 3 Flash delivers Pro-grade intelligence at Flash-level costs.

For complex, regulated workflows requiring strict governance, specific vocabulary enforcement, or integration with complex back-end systems, the modular stack delivers necessary control and auditability without the latency penalties that previously hampered modular designs. Together AI's co-located architecture or Retell AI's compliance-first orchestration represent the strongest contenders here. 

The architecture you choose today will determine whether your voice agents can operate in regulated environments — a decision far more consequential than which model sounds most human or scores highest on the latest benchmark.