Deepgram vs ElevenLabs for Voice AI in 2026: Why You Probably Need Both

Published: April 28, 2026 Updated: April 28, 2026 Reading time: 7 minutes

If you Googled "Deepgram vs ElevenLabs," you're probably new to the voice AI stack. That's fine — the answer is short: they do different things and a typical production voice agent uses both.

• Deepgram is a speech-to-text (STT) provider. It listens to the caller and turns their speech into text.
• ElevenLabs is a text-to-speech (TTS) provider. It takes the agent's response text and turns it into spoken audio.

You can run a voice agent without one of them only if you replace it with a competing provider in the same layer. You cannot use ElevenLabs to do STT and you cannot use Deepgram to do TTS in any meaningful way.

This post explains the layers, then walks through how the two providers compare against their actual peers and how to combine them.

The voice AI stack in 30 seconds

A production voice AI call has four layers running in real time:

1. Telephony — Twilio, Telnyx, Vonage. The phone line.
2. STT (speech-to-text) — Deepgram, AssemblyAI, Whisper, Azure Speech. Listening.
3. LLM — OpenAI, Anthropic, Groq, Google. Thinking.
4. TTS (text-to-speech) — ElevenLabs, Cartesia, OpenAI TTS, Azure Neural. Talking.

Deepgram lives in layer 2. ElevenLabs lives in layer 4. They both ship streaming APIs because real-time voice is unforgiving — anything over 1.5s of total latency starts feeling broken.

When you say "Deepgram vs X," X is usually...

• AssemblyAI Universal Streaming — competitive on accuracy, less competitive on latency for English voice agents.
• OpenAI Whisper-large-v3 — strong accuracy, no streaming endpoint that meets voice-AI latency budgets.
• Azure AI Speech — great if you're already on Azure for compliance reasons.
• Google Cloud Speech-to-Text — similar story to Azure.

For voice AI in 2026, Deepgram Nova 3 is the default pick. Sub-300ms streaming latency, ~6.84% median WER on real-world audio, $0.0043/min on the standard plan. Almost every voice agent we benchmark eventually lands on Deepgram unless there's a specific compliance requirement.

When you say "ElevenLabs vs X," X is usually...

• Cartesia Sonic — meaningfully faster TTFB, competitive English quality, ~30% cheaper. See Cartesia vs ElevenLabs.
• OpenAI TTS-1 — fine for prototypes, single voice library, decent latency, less expressive than ElevenLabs.
• Azure Neural TTS — workhorse for enterprise/compliance scenarios, doesn't reach ElevenLabs' emotional range.
• Amazon Polly — older-feeling voices, fine for IVR readback, not great for conversation.

For voice AI in 2026, the TTS pick depends heavily on your latency and quality budget. ElevenLabs Flash v2.5 closes most of the latency gap to Cartesia while keeping ElevenLabs' voice quality. ElevenLabs Multilingual v2 is the only sane choice if you need 30+ languages with native-sounding accents.

Combining Deepgram and ElevenLabs

The standard production combo for a high-quality English voice agent in 2026:

That's roughly $0.07–$0.085 per minute in provider passthrough alone, before any platform fee. Burki adds $0.03/min on top in BYO mode (zero markup on providers), so end-to-end you're at ~$0.10–$0.115/min for a premium voice agent.

If you swap ElevenLabs Flash for Cartesia Sonic, that drops to ~$0.07–$0.085/min total. Same architecture, different TTS, ~30% cost reduction.

When Deepgram is the wrong call

• Strict regional residency — if the audio cannot leave a specific region, Azure or a self-hosted Whisper deployment may be required.
• Languages outside Deepgram's 30+ supported set — Deepgram coverage is broad but not exhaustive.
• Offline / air-gapped deployments — Deepgram is cloud-only; you'd need Whisper-large or a NeMo-based stack.

When ElevenLabs is the wrong call

• Sub-100ms TTFB requirement — Cartesia Sonic is faster.
• Tight cost budget at high volume — Cartesia is ~30% cheaper.
• Microsoft-stack-aligned compliance — Azure Neural TTS keeps everything in Azure.

How Burki integrates both

Both Deepgram and ElevenLabs ship as first-class integrations in Burki. You set the provider per assistant in the dashboard or via API, and BYO mode means you pay each provider directly at their wholesale rate — Burki adds zero markup on top, only the $0.03/min platform fee.

The standard "production English voice agent" template in Burki is:

# Per-assistant config:
# STT -> Provider: deepgram
# STT -> Model: nova-3-general

# LLM -> Provider: openai
# LLM -> Model: gpt-4o-mini

# TTS -> Provider: elevenlabs
# TTS -> Model: eleven_flash_v2_5
# TTS -> Voice: <voice_id>

That stack does sub-700ms total response latency in production, runs at ~$0.10/min all-in, and sounds genuinely good.

Recommendation

If you're building a voice AI in 2026:

• Use Deepgram Nova 3 for STT unless you have a specific compliance reason not to.
• Use ElevenLabs Flash v2.5 for TTS if voice quality is meaningful, Cartesia Sonic if latency or cost is the binding constraint.
• Don't pick "Deepgram OR ElevenLabs" — they're different layers.

Burki ships native adapters for both and ~50 other providers. Try the combo on a production call: Start Free.