Introduction

Speech-to-text (STT) isn’t just about transcripts- it’s the foundation for voice assistants, live captions, analytics dashboards, and more. Choosing the right API affects latency, accuracy, cost predictability, and global reach.

Today, speech systems sit inside live products- voice agents, copilots, compliance engines, AI workflows that react in real time. In these systems, speech is not just converted to text; it becomes an active input that drives decisions instantly. And that shift has quietly exposed the limits of older speech architectures.

This comparison looks at Deepgram Nova 3, one of the most established players in the space, and Smallest Pulse STT, a system built specifically for this new real-time reality.

Deepgram: The Company That Defined Modern STT

Founded in 2015, Deepgram was one of the first companies to abandon traditional ASR pipelines and train end-to-end deep learning models directly on raw audio. This was a meaningful leap forward at the time. By removing hand-engineered acoustic and language models, Deepgram was able to scale accuracy across noisy environments, accents, and domains.

Over the years, Deepgram became a trusted default for enterprises. Their Nova model line now including Nova-2 and Nova-3, reflects years of refinement, and their speech-to-text product is widely used across contact centers, media transcription, and analytics-heavy workflows. The APIs are stable, the documentation is solid, and the ecosystem is mature.

Deepgram’s success is well earned. But its architecture reflects the era it was built for: speech as something you process, analyze, and store- and then create transcripts and summaries. 

The Problem: Speech Is No Longer Passive

Modern voice systems don’t wait for audio to finish.

They interrupt speakers.
They respond mid-sentence.
They feed language models continuously.

In these systems, speech-to-text sits directly on the critical path. A few hundred milliseconds of delay no longer feels like a technical detail- it becomes a product flaw. This is where many teams start to feel friction with legacy STT setups, even when accuracy is high.

Pulse STT exists because of this shift

Pulse STT: Designed for Real-Time AI, Not Just Transcription

Smallest Pulse STT was built with a different assumption: speech is a live signal, not a file. Everything—from model behavior to infrastructure—has been optimized for streaming, concurrency, and predictable latency.

This approach has already proven itself in production. Pulse STT has replaced Deepgram, Google Speech, and AssemblyAI in multiple large enterprise deployments, not as an experiment, but as core infrastructure. The system is now being opened up more broadly because it consistently performs where real-time systems break.

Latency: Where the Difference Becomes Impossible to Ignore

The most immediate difference between the two systems shows up in real-time behavior.

Pulse STT delivers a time-to-first-byte of roughly 64 milliseconds. Partial transcripts arrive fast enough to support natural conversational turn-taking, interruption handling, and live AI reasoning.

Deepgram supports real-time streaming as well, but in practice partial results often land closer to the 200–300 millisecond range. 

That’s perfectly acceptable for captions or near-real-time analytics. But when speech feeds an LLM and then loops back into audio, that delay compounds.

Scale and Concurrency Without Surprises

Another difference emerges under load.

Pulse STT is designed to maintain stable latency even as concurrency increases. Hundreds of simultaneous WebSocket streams and REST requests behave predictably, which is essential when speech is embedded inside products rather than processed in batches.

Deepgram scales well and reliably, but concurrency behavior is more closely tied to quotas and plan limits. For teams running large batch workloads, this is rarely an issue. For teams running live systems at scale, predictability matters more than raw throughput.

Pulse’s infrastructure assumes that everything is streaming. Deepgram’s assumes that streaming is one of several modes.

Language Handling That Matches How People Actually Speak

Language support is another area where architectural intent shows through.

Pulse STT supports over 30 languages across Europe, Asia, and Latin America, with automatic language detection and live switching mid-stream. This means a speaker can move between languages naturally without restarting sessions or forcing configuration changes. Accents and regional variations are treated as first-class concerns, not edge cases.

Deepgram supports a comparable number of major languages and performs well across them, but dynamic code-switching is more limited. For global products, especially in markets where multilingual speech is the norm Pulse removes an entire layer of orchestration logic.

More Than Words: Treating Speech as Structured Data

One of the most meaningful differences is how each system treats speech output.

Pulse STT doesn’t stop at transcription. It extracts structure and signal directly from audio: speaker attribution, emotion, age and gender indicators, numeric normalization, and automatic redaction of sensitive information. These capabilities are available directly in the transcription output, without requiring separate analysis pipelines.

Deepgram focuses primarily on producing high-quality text with formatting and diarization support. More advanced insights typically require additional tooling or downstream processing.

Pulse collapses what is often a multi-stage speech pipeline into a single real-time layer.

Enterprise Reality: Compliance, Deployment, Control

Both platforms meet enterprise compliance standards, including SOC 2, GDPR, and HIPAA. Where Pulse differentiates itself is deployment flexibility.

Pulse STT supports on-prem deployment as a first-class option. For regulated industries, this is often non-negotiable. Combined with direct onboarding and hands-on support, it makes adoption simpler for teams with strict security requirements.

Deepgram is primarily cloud-first, with enterprise arrangements available for specialized needs.

Choosing Between Them

Deepgram remains a strong choice for teams that value ecosystem maturity, batch processing, and established enterprise integrations. It is a reliable, proven platform.

Pulse STT, however, is built for a different world, one where speech is live, reactive, and deeply intertwined with AI systems. Enterprises switching to Pulse aren’t chasing marginal accuracy gains; they’re responding to new product realities.

Beyond Pulse STT, enterprises can also access Smallest AI’s ecosystem of products which includes a state of the art text to speech and voice agent platform. 

Final Thought

Deepgram helped define what modern speech-to-text looks like.

Pulse STT represents what speech infrastructure looks like after real-time AI becomes the default.

If speech in your product needs to feel invisible, immediate, and globally adaptable, Pulse STT is built for that future.

Benchmarks and open-source evaluations are available, and teams can be personally onboarded to test the system under real production conditions.