By Sagar Shankaran, Founder of CallSphere
A cascaded Deepgram STT + LLM + TTS stack lands at $0.05–$0.15 per minute. End-to-end Realtime APIs run $0.10–$0.30. The honest tradeoff is latency, not just cost.
Key takeaways
A cascaded Deepgram STT + LLM + TTS stack lands at $0.05–$0.15 per minute. End-to-end Realtime APIs run $0.10–$0.30. The honest tradeoff is latency, not just cost.
flowchart LR
Browser["Browser / Phone"] -- "WebSocket /ws" --> LB["Load Balancer<br/>sticky session"]
LB --> Pod1["Node A · Socket.IO"]
LB --> Pod2["Node B · Socket.IO"]
Pod1 -- "pub/sub" --> Redis[("Redis cluster")]
Pod2 -- "pub/sub" --> Redis
Pod1 --> AI["AI Worker · OpenAI Realtime"]
Pod2 --> AIThere are two broad architectures for voice agents in 2026: end-to-end speech-to-speech (gpt-realtime, ElevenAgents Premium), and cascaded pipelines (STT → LLM → TTS, often Deepgram + GPT-4o-mini + Aura-2). The end-to-end stacks are simpler and lower-latency for short turns; the cascaded stacks are usually cheaper, especially with a small/cheap LLM in the middle.
The cost gap can be 2–4× depending on how you wire it. But cost is not the only axis — latency, voice quality, and barge-in behavior all change with architecture.
Deepgram's pricing page (May 2026) lists:
Pretend a typical 5-minute support call with 60/40 caller-agent split.
Cascaded DIY pipeline (Deepgram Nova-3 + GPT-4o-mini + Aura-2):
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Deepgram Voice Agent Standard tier (bundled):
OpenAI gpt-realtime cached:
ElevenAgents Turbo:
The cascaded DIY pipeline is the cheapest at $0.019/min — about 4× cheaper than gpt-realtime cached and 5× cheaper than ElevenAgents Turbo. But you give up something: latency adds up across hops (STT TTFT + LLM TTFT + TTS TTFB), and you have to do your own VAD, barge-in, and turn-taking logic.
In our internal benchmarks, voice-to-voice latency by architecture:
So the DIY stack saves you 75% on cost but adds 90–290ms of latency. For short FAQ flows, that is fine. For empathetic healthcare intake, it is not.
CallSphere uses the cascaded approach for the Sales agent's outbound discovery flow because the prompt is small (3.5k tokens, mostly objection handling) and most calls last under 4 minutes — the end-to-end TTFT advantage is wasted on short turns. We use Deepgram Nova-3 for STT, GPT-4o-mini with 90% prompt caching for the brain, and Aura-2 for TTS. Net: $0.024/min on Sales — a substantial save vs Realtime.
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For Healthcare we go end-to-end on OpenAI Realtime PCM16 24kHz because the 22k-token clinical prompt and emotional barge-in tolerance demand it. The cost is higher per minute but the post-call NPS gap (8.4 vs 7.1 in our internal A/B last quarter) justified it.
Across the 6 verticals — 37 agents, 90+ tools, 115+ DB tables — about 60% of agent-minutes run cascaded and 40% end-to-end. The pricing tiers ($149 / $499 / $1499) are designed so even the cheap-tier customers can run the end-to-end stack on the calls that matter. Try it on the 14-day no-card trial.
Is cascaded always cheaper than end-to-end? At small prompt sizes, yes. At very large prompts, end-to-end with prompt caching can match or beat it because the cache rate is so steep ($0.40/M vs $4/M).
Why is Deepgram's bundled Voice Agent more expensive than DIY? You pay for orchestration, hosted VAD, barge-in handling, and the support contract.
Can I mix providers — Deepgram STT + OpenAI text LLM + ElevenLabs TTS? Yes, this is a very common production stack. CallSphere does it on Sales.
Does latency really matter that much? For empathetic flows yes — every 100ms over 600ms reduces "felt naturalness" measurably. For order-status FAQs, less so.
What about Deepgram Aura-2 vs ElevenLabs v3? Aura-2 is faster (sub-100ms TTFB) and cheaper per char. v3 is more expressive. Pick by use case.
Written by
Sagar Shankaran· Founder, CallSphere
Sagar Shankaran is the founder of CallSphere, where he builds production AI voice and chat agents deployed across healthcare, hospitality, real estate, and home services. He writes about agentic AI, LLM engineering, and shipping voice agents that handle real calls in production.
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