Engineering
The 4 best ways to build a voice AI stack in 2026
Once you spend $10k or more a month on voice AI infrastructure, the trade-offs between latency, control, and cost become too hard to ignore.
When you operate at scale, small architectural differences turn into massive monthly invoices and conversation lag that ruins calls. Here is my breakdown of the leading stacks, their trade-offs, and who each is best for.
The 4 voice stacks
- Managed orchestration for teams that need to launch in days
- Modular pipelines for high-volume products
- Native speech-to-speech for warmth over unit cost
- Enterprise contact center suites for regulated migrations
The 4 stacks at a glance
| Stack | Best for | Standout | Watch-out |
|---|---|---|---|
| Managed orchestration | Validating product-market fit | Live in days, no voice infra hire | 800–1200ms+ baseline latency |
| Modular pipelines | High-volume products | 500–800ms end to end | You own turn-taking and outages |
| Speech-to-speech | Coaching and consumer apps | Tone, laughter, no transcript errors | Often $0.20–$0.30+/min |
| Contact center suites | Regulated enterprises | Legacy SIP and compliance | Long contracts, slow shipping |
1. Managed orchestration platforms
Vapi and Retell AI bundle telephony, speech-to-text, LLM routing, turn detection, and text-to-speech into a single API.
You get prebuilt WebRTC and telephony, managed voice activity detection, and interruption handling. You do not need a dedicated voice infrastructure engineer to start.
Managed orchestration cons:
- A compounded margin stack that gets expensive as you scale
- Extra network hops that create a baseline latency of 800–1200ms+
- Limited control over custom model routing, fallback models, or prompt caching
Best for: teams validating product-market fit who need to go live in days without managing infrastructure.
2. Modular pipelines
You separate every layer. LiveKit or Daily for transport, Deepgram for speech-to-text, Groq or Anthropic for the model, and Cartesia or ElevenLabs for speech. You co-locate servers near telephony gateways and run your own turn-taking.
This is where production latency usually lands, routinely 500–800ms end to end, and where blended cost can come down once volume is real. You also get control over VAD sensitivity, speculative execution, and model switching.
Modular pipeline cons:
- You need distributed systems and WebRTC experience on the team
- Turn detection, state sync, and audio buffering are yours to build
- Outages now span several providers, and you are the one watching them
Best for: high-volume products where shaving 200 milliseconds and cutting unit cost justifies dedicated engineering time.
3. Native speech-to-speech models
OpenAI Realtime and Gemini Live take raw audio in and stream raw audio out. There is no intermediate transcript.
The gain is emotional inflection, natural tone, laughter, and interruptions handled in the audio itself. Accents and domain words stop getting mangled by a separate speech-to-text step.
Speech-to-speech cons:
- Expensive at volume, typically $0.20–$0.30+ per minute or more
- Less deterministic control over structured outputs and tool calling
- Harder to debug hallucinations or timing quirks inside a black box
Best for: consumer applications and open-ended coaching, where human warmth matters more than strict unit economics.
4. Enterprise contact center suites
NiCE CXone, Kore.ai, and Google Agent Studio are built for legacy contact center migration and enterprise compliance. Visual workflow builders let operations teams change flows without a deploy.
Enterprise suite cons:
- Enterprise pricing, long sales cycles, and annual contracts
- Slower development velocity and rigid deployment pipelines
Best for: regulated contact centers that cannot compromise on legacy compliance.
If you are spending $10k+ a month on voice AI infrastructure and want a second opinion on your stack, DM me.
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