AssemblyAI: Audio Intelligence as a Platform

Founded in 2017, AssemblyAI has steadily positioned itself as an “AI-complete” speech platform. Transcription is only the starting point. On top of it sits LeMUR, their built-in large language model layer that enables summarization, Q&A, sentiment analysis, topic detection, and content moderation directly on transcripts.

For many teams, this is appealing. You upload audio, call one API, and receive not just text but structured insights. The developer experience is polished, documentation is excellent, and the abstraction layer removes the need to think deeply about model orchestration.

AssemblyAI’s philosophy is clear: audio intelligence should be vertically integrated.

Smallest Pulse STT: Speech as Live Infrastructure

Pulse STT takes a fundamentally different view.

It assumes speech is no longer something you analyze after it happens. In modern systems, voice agents, AI copilots, compliance engines- speech is a live input that must be processed continuously, predictably, and at scale.

Pulse STT is built around that assumption. It focuses on:

• Extremely low latency streaming

• Stability under concurrency

• Broad multilingual and accent coverage

• Structured outputs that downstream systems can act on immediately

Instead of bundling an LLM into the speech layer, Pulse deliberately leaves that choice to the customer.

The philosophy here is simple: do transcription exceptionally well, and let teams choose the intelligence layer.

Architecture: Vertical Stack vs Modular Control

The architectural difference between AssemblyAI and Pulse STT explains almost every tradeoff that follows.

AssemblyAI offers a tightly integrated stack. Speech flows directly into LeMUR, and from there into summaries, insights, and classifications. Everything is unified—billing, APIs, outputs. For teams that want fast time-to-value and minimal decisions, this works well.

Pulse STT is intentionally modular. Speech is streamed, structured, and returned as fast as possible. From there, teams plug it into their own LLMs- Claude, GPT-4, Llama, or custom models depending on cost, performance, or compliance needs.

Neither approach is “better” in isolation. But the implications become clear once latency, scale, and cost enter the picture.

Accuracy in the Real World (Not Just Clean Audio)

On clean, studio-quality audio, both platforms perform well. The differences emerge when conditions become less ideal—phone calls, accented speech, multilingual conversations.

Across real-world benchmarks, Pulse STT consistently shows lower word error rates, especially on:

• Call center audio (8kHz)

• Indian English and other accented speech

• Mixed-quality recordings
  
  

This gap widens as conditions degrade. AssemblyAI performs reliably on podcasts and controlled recordings, but struggles more as audio becomes conversational and noisy.

For teams building consumer or global products, this difference matters more than benchmark wins on pristine datasets.

Latency: Where the Philosophies Collide

Latency is where the contrast becomes unavoidable.

Pulse STT is designed to stay below the 200ms “instantaneous” threshold even at high percentiles. Partial transcripts arrive quickly enough to support interruption handling, live reasoning, and natural turn-taking.

AssemblyAI supports streaming, but real-time latency often lands closer to the mid-300ms range. For batch transcription, this is irrelevant. For interactive systems, it is noticeable.

This isn’t a technical shortcoming so much as a design choice. AssemblyAI optimizes for post-processing intelligence. Pulse optimizes for live responsiveness.

If speech feeds an LLM which then feeds a TTS engine, that difference compounds quickly.

Language Coverage and Code-Switching

Pulse STT currently supports more than three times as many languages as AssemblyAI, with automatic detection and live switching mid-stream. This allows speakers to move naturally between languages without restarting sessions or forcing configuration changes.

AssemblyAI supports major languages well, but dynamic code-switching is limited. For global teams, especially in Asia, Africa, and multilingual markets, Pulse removes complexity that would otherwise live in application code.

Compliance and Why Streaming Matters More Than Features

One of the most overlooked differences between these platforms is how they handle compliance.

AssemblyAI’s model works well for post-hoc analysis detecting sensitive content after transcription completes.

Pulse STT enables something different: real-time compliance.

Because speech is streamed with extremely low latency, systems can:

• Detect and redact PII or PCI data as it is spoken

• Monitor emotional escalation live

• Intervene before violations occur

• Reduce the storage of raw sensitive audio
  
  

In regulated industries, this distinction is critical. Compliance is no longer just about audits, it's about preventing violations in real time. Streaming at Pulse’s latency level makes that possible.

Cost at Scale: Where Architecture Shows Up on the Invoice

AssemblyAI’s pricing reflects its all-in-one approach. You pay more per minute, but you get transcription.

Pulse STT is significantly cheaper per minute and deliberately unbundled. When combined with modern LLM pricing, this often results in meaningfully lower total cost, especially at scale.

For teams running thousands of hours per month, or continuous streams, the difference isn’t marginal, it's strategic.