AssemblyAI vs Deepgram

For high-volume call center transcription, per-minute cost is the dominant factor. AssemblyAI charges $0.0035 per audio minute for batch versus Deepgram at $0.0043, a meaningful difference at scale. The gap widens with diarizationdiarization - Splitting a transcript by speaker, so you can see who said what.: AssemblyAI's add-on costs $0.000333 per minute versus Deepgram's $0.002, roughly 6x more expensive. PII redaction follows the same pattern, with AssemblyAI at $0.001333 per minute against Deepgram's $0.002. On concurrency, AssemblyAI supports 200+ async concurrent jobs versus Deepgram's 50 REST concurrent, providing more headroom for call volume spikes. Both tools offer sentiment analysis. Across all three heaviest cost attributes, AssemblyAI is consistently cheaper, and the diarization gap alone is decisive for call center economics.

On batch pricing, the heaviest attribute, AssemblyAI charges $0.0035 per audio minute versus Deepgram at $0.0043 per audio minute, giving AssemblyAI a meaningful cost edge at scale. Both tools offer websocket streaming APIs and word-level timestampsword-level timestamps - The API reports exactly when each word is spoken in the audio, for subtitles and highlighting., so those attributes are even. On SDKs, Deepgram publishes six official SDK languages (JS/TS, Python,.NET, Go, Java, Rust) compared to AssemblyAI's two official SDKs (Python and JavaScript/Node), which favors Deepgram on that attribute. For supported formats, Deepgram covers 100+ formats versus AssemblyAI's 30+, another edge for Deepgram. However, batch pricing carries the highest weight and AssemblyAI leads there clearly, which tips the overall verdict.

The available facts cover API-level attributes such as batch pricing, streaming latency, WERWER - Word error rate - the share of words a transcription gets wrong; lower is more accurate., and SDK support, but none of the five attributes that decide the dictation use case are addressed. There are no facts about dictation platforms, whether audio stays on-device or goes to the cloud, AI formatting or editing features, one-time versus subscription app pricing, or app integrations for either AssemblyAI or Deepgram. Without any facts bearing on the heaviest-weighted attributes for this use case, neither tool can be shown to lead the other.

For clinical transcription under US healthcare privacy law, both tools match on every decisive attribute. Both offer a HIPAA BAAHIPAA BAA - A signed Business Associate Agreement that lets you handle protected health information under US healthcare privacy law., both provide PII redaction as a paid add-on, both support custom vocabulary and keyterm boosting, both hold SOC 2 Type II certification, and both offer a self-host option. The only noted difference is that AssemblyAI charges about $0.001333 per audio minute for PII redaction versus Deepgram at $0.002 per audio minute, but pricing is not among the weighted attributes for this use case. Since the tools are genuinely equal on all five attributes that decide this use case, no winner can be named.

For recorded meetings, speaker diarizationdiarization - Splitting a transcript by speaker, so you can see who said what. is the top priority. Both tools offer it as a paid add-on, but AssemblyAI's diarization costs $0.000333/audio-min versus Deepgram's $0.002/audio-min, making AssemblyAI about 6x cheaper for this critical feature. Summarization is tied. On language coverage, AssemblyAI supports 99 languages versus Deepgram's 50, a meaningful advantage for multilingual meeting participants. For long-file handling, AssemblyAI accepts files up to 5 GB and 10 hours per file, while Deepgram caps at 2 GB with a 10-minute processing timeout for Nova, a real limitation for lengthy recorded meetings. Word-level timestamps are tied. AssemblyAI leads on three of the five weighted attributes, two of them decisively.

Both AssemblyAI and Deepgram offer a self-hostself-host - Run the software on your own servers instead of the vendor’s cloud. or on-premises option, so they are equal on the most heavily weighted attribute. For the next four decisive attributes, open weights license, hardware requirements, project maintenance status, and model size, no facts are available for either tool. With the top attribute tied and no data for any of the remaining four weighted attributes, neither tool can be distinguished on the criteria that matter most for this use case.