Head-to-head comparison
Deepgram vs AssemblyAI
Two developer-first speech-to-text APIs with published pricing and open documentation, separated by streaming behaviour and downstream language features.
Deepgramvs
AssemblyAIShort answer
Deepgram vs AssemblyAI: which should you choose?
Deepgram and AssemblyAI both publish open documentation, self-serve keys and per-minute pricing, and both will sign a business associate agreement. Deepgram is the stronger fit where real-time streaming latency and deployment flexibility decide the design; AssemblyAI is stronger where post-call language features on top of the transcript reduce what you build.
Cite as: Deepgram vs AssemblyAI comparison, Compare Healthcare API, last reviewed 2026-09-01.
Key findings
- Neither vendor produces a clinical note — both leave note generation, template adherence and review workflow entirely with you.
- Both publish per-minute pricing openly, which makes this the rare comparison you can model accurately before contacting sales.
- Streaming versus asynchronous batch is the practical dividing line: live encounter capture and post-visit processing have different failure modes.
- Medical vocabulary accuracy is claimed by both and reproducibly benchmarked by neither; test on your own de-identified audio.
Score by criterion
Same rubric, same evidence tiers, applied to both vendors.
| Criterion (weight) | Deepgram | AssemblyAI |
|---|---|---|
| API & SDK maturity22% | 9.6 | 9.2 |
| Clinical accuracy & output quality20% | 7.8 | 7.4 |
| EHR & FHIR interoperability16% | 3.2 | 3.0 |
| Compliance & security posture16% | 8.3 | 8.0 |
| Latency & streaming behaviour10% | 9.4 | 8.6 |
| Specialty & template coverage8% | 5.4 | 5.2 |
| Commercial terms & transparency8% | 9.2 | 8.9 |
| Weighted total | 7.6 | 7.3 |
Structural differences
The differences that decide this comparison are commercial and architectural, not marketing claims.
| Dimension | Deepgram | AssemblyAI |
|---|---|---|
| Primary strength | Low-latency real-time streaming recognition | Asynchronous transcription with downstream language features |
| Output | Transcript with timestamps, diarisation and formatting | Transcript plus summarisation and structured language outputs |
| Pricing | Published per-minute audio pricing | Published per-hour and per-feature pricing |
| Deployment | Cloud with self-hosted options discussed for larger contracts | Cloud-hosted service |
| Healthcare posture | BAA available; HIPAA workflows documented | BAA available on qualifying plans |
What this comparison cannot settle
Neither vendor publishes a reproducible word error rate on clinical audio with a stated corpus, speaker mix and scoring method. Vendor-reported accuracy figures across different test sets are not comparable, and general-domain benchmarks tell you little about a cardiology follow-up recorded on a laptop microphone in a noisy room.
The only decision-grade evidence is your own: a few hundred de-identified encounters, scored with a clinically weighted error count that penalises a wrong medication or dosage far more heavily than a dropped filler word.
The layer neither of them covers
Both vendors stop at words. The distance between an accurate transcript and a note a clinician will sign is a system: encounter segmentation, note generation constrained to a template, hallucination detection, structured extraction, an edit and attestation trail, and a clinical-safety review process that never ends.
If that layer is not your product's differentiator, the honest comparison is not Deepgram against AssemblyAI but either of them against a documentation API that returns a note.
Fit verdict
Neither answer is universal. Match the choice to your buyer and your engineering capacity.
Choose Deepgram when
- You capture live encounter or telehealth audio and latency is user-visible.
- You need diarisation and word-level timings as first-class outputs.
- Deployment flexibility or data-locality control is a hard requirement.
- You already own the note-generation layer and want the fastest recogniser under it.
Choose AssemblyAI when
- Your audio is processed after the encounter rather than during it.
- Summarisation and language features on top of the transcript remove work you would otherwise build.
- You want the simplest possible integration path for a first version.
- Your volume is modest and per-feature pricing suits it better than raw per-minute rates.
Evidence & sources
Every factual claim on this page traces to one of the primary references below. Each entry records what it supports and its evidence tier, so documentation can be told apart from judgement.
Deepgram · Vendor documentation · Tier A — primary documentation
Supports: Self-serve access, streaming endpoints, model options and documented rate limits.
Deepgram · Vendor documentation · Tier A — primary documentation
Supports: Published per-minute pricing used in our commercial transparency scoring.
AssemblyAI · Vendor documentation · Tier A — primary documentation
Supports: Self-serve onboarding, streaming, summarisation and PII redaction features.
AssemblyAI · Vendor documentation · Tier A — primary documentation
Supports: Published usage pricing used in commercial transparency scoring.
NIST speech recognition evaluation literature · Methodology · Tier B — published methodology
Supports: Why a headline WER figure without a stated corpus, audio condition and reference-transcript protocol is not comparable across vendors.
U.S. Department of Health & Human Services · Regulation · Tier A — primary documentation
Supports: The contractual clauses a documentation vendor's BAA must contain.
Source tiers are defined on the methodology page. Outbound links are unaffiliated and carry no commercial relationship.
Frequently asked questions
- Which is more accurate on medical audio?
- Unresolved as published. Neither vendor releases a reproducible clinical benchmark, so accuracy claims are not comparable across vendors. Run both on your own de-identified encounters with a clinically weighted error count. How to test.
- Do both sign a BAA?
- Both make a business associate agreement available. A BAA is a contractual floor, not a configuration: retention, logging, access control and training-on-PHI terms still need to be confirmed in writing. Compliance checklist.
- Can either produce a clinical note?
- No. Both return transcripts and language outputs. Note generation, template adherence, clinician review and chart write-back remain your responsibility with either vendor. Build vs buy.