Deepgram vs AssemblyAI: Choosing the Right STT for Your AI Calling Agent

Why STT choice matters

Speech-to-text is the first stage of every AI call. If the STT misunderstands what the caller said, every downstream component — LLM reasoning, TTS response, action logging — operates on corrupted data.

STT errors compound. A single misrecognized phone number or address means the entire call was wasted. A misheard "no" as "yes" means the lead is moved to the wrong pipeline stage.

Two providers dominate AI calling deployments in 2026: Deepgram and AssemblyAI. Here's when to choose each.

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Deepgram

Public pricing (April 2026):

• Nova-3 (flagship): $0.0043/minute for streaming, $0.0043/minute for pre-recorded
• Nova-2: $0.0043/minute (same)
• Whisper Cloud: $0.0048/minute
• Free tier: $200 in credits

Where it wins:

• Lowest latency among major providers. Typical time-to-first-transcript: 100–250ms for streaming.
• Purpose-built for real-time applications like phone calls, meeting transcription, live captioning.
• Strong at handling phone audio quality (compressed 8kHz audio is a first-class use case, not an afterthought).
• Custom model fine-tuning available at higher tiers — train on your industry's vocabulary (medical terms, product names, etc.) for dramatic accuracy improvements.
• Endpointing detection (knowing when the caller finished speaking) is best-in-class. This directly affects conversational flow.

Where it loses:

• Fewer value-added features beyond transcription itself. If you want speaker diarization, sentiment analysis, PII redaction, topic detection — AssemblyAI has a richer feature set built-in.
• Documentation is solid but less extensive than AssemblyAI's for non-transcription features.

Best for: Real-time phone calls, IVR systems, live transcription. Deployments where sub-300ms latency is critical.

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AssemblyAI

Public pricing (April 2026):

• Universal-2 streaming: $0.0037/minute
• Pre-recorded (Nano tier): $0.12/hour ($0.002/minute)
• Pre-recorded (Best tier): $0.37/hour ($0.0062/minute)
• Add-on features (auto highlights, topic detection, etc.): priced per feature

Where it wins:

• Richer built-in features: speaker diarization, sentiment analysis, PII redaction, auto-chapters, topic detection, entity extraction. For use cases beyond pure transcription, this saves building custom post-processing.
• Better value for batch transcription (pre-recorded audio). Meeting recordings, voicemails, and call analytics workflows benefit.
• LeMUR (LLM-over-transcript) lets you run custom prompts on transcribed content natively.
• Stronger accuracy for non-American English accents in some test comparisons.
• Excellent documentation and developer experience.

Where it loses:

• Streaming latency is solid but typically 50–150ms higher than Deepgram for equivalent tasks. For most applications this is irrelevant; for AI calling it can be noticeable.
• Endpointing is less tunable than Deepgram's.

Best for: Call analytics pipelines, meeting transcription, content processing. Use cases where speaker diarization, sentiment, or advanced feature extraction matter.

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Head-to-head: accuracy

Both providers publish accuracy benchmarks on their own data, which makes comparison difficult. In my own testing with real phone audio from AI calling deployments:

• English clean audio: Both are effectively equivalent. Word error rates typically under 5%.
• English with background noise / phone compression: Deepgram's phone-audio-specific training shows a measurable edge.
• Accented English (Indian, Latin American, Southern US): Varies by accent. AssemblyAI has slight edge on some accents, Deepgram on others. Test with your specific lead population.
• Technical vocabulary (medical, legal, financial): Both benefit significantly from custom vocabulary / keyword boosting. Neither is meaningfully better than the other when configured.

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The features that matter for AI calling

When choosing STT specifically for an AI caller (VAPI, Retell, Bland), these features matter most:

1. Streaming latency to first partial transcript Deepgram: 100–250ms typical AssemblyAI: 200–400ms typical Winner: Deepgram for real-time conversation.

2. Endpointing (detecting when caller finished speaking) Deepgram's endpointing parameter lets you tune this from 10ms to 2000ms. Critical for natural turn-taking. AssemblyAI's equivalent is less configurable. Winner: Deepgram.

3. Interim results Both providers stream interim (incomplete) results so your AI can react faster. Parity here.

4. Keyword boosting Deepgram's custom vocabulary lets you weight specific terms. AssemblyAI's word boost works similarly. Parity for most use cases.

5. Multi-language support Both support 30+ languages. Check specific language quality for your market.

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Pricing at AI-calling scale

Example: 10,000 minutes of streaming STT per month.

• Deepgram Nova-3: 10,000 × $0.0043 = $43/month
• AssemblyAI Universal-2: 10,000 × $0.0037 = $37/month

The cost difference is minor. STT is one of the smallest line items in an AI calling cost breakdown.

At 100,000 minutes/month, Deepgram is $430 vs AssemblyAI at $370. Still small relative to LLM and TTS costs at that volume.

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The decision framework

Choose Deepgram if:

• Your primary use case is real-time AI calling
• You're running on VAPI / Retell / Bland (Deepgram is often the default choice these platforms recommend for this reason)
• Latency and endpointing matter more than post-call analytics features
• You need custom model fine-tuning

Choose AssemblyAI if:

• You need speaker diarization, sentiment analysis, or PII redaction out of the box
• Your primary use case is call analytics pipelines (transcribing recorded calls for insight)
• You want to run LLM prompts on transcripts natively (LeMUR)
• Streaming isn't your primary workload

Use both if:

• Your system has both real-time (calling) and batch (analytics) workloads. It's common to use Deepgram for live and AssemblyAI for post-call processing where the added features pay off.

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What neither provider solves

Any STT will struggle with:

• Extremely noisy backgrounds (construction sites, noisy cafes)
• Multiple simultaneous speakers without clear separation
• Very low-quality audio (poor phone connections, low bitrate VoIP)
• Code-switching between languages mid-sentence

For these scenarios, audio quality improvement on the input side (noise cancellation on your AI platform, higher-quality telephony) matters more than STT provider choice.

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Sources

Pricing data from deepgram.com/pricing and assemblyai.com/pricing as of April 2026. Latency numbers are from each provider's published specifications and my own testing on VAPI deployments. Accuracy observations are based on deployment experience across 13+ client projects.

Need help benchmarking which provider works best for your specific audio conditions? Get in touch — I can run a side-by-side test on sample recordings.