Compare Features, Cost, and What You Actually Pay at Scale

Speech-to-text pricing in 2026 is more confusing than it looks.

Most providers advertise a per-minute rate. Some bundle minutes into subscriptions. Others abstract costs behind credits or tokens. Very few explain how pricing behaves once you factor in real-time streaming, concurrency limits, and production features.

This guide compares the actual pricing models used by popular speech-to-text AI APIs, using real plan data, and explains which models work best for real-world use cases like call centers, voice agents, and live transcription.

The Main Pricing Models Used by Speech-to-Text APIs

Despite different marketing language, nearly every voice-to-text platform follows one of four pricing models.

1. Pay-As-You-Go, API-First Pricing

This model charges per minute of audio processed and is common among developer-first APIs.

Providers like AssemblyAI, Deepgram, and Speechmatics fall into this category.

AssemblyAI advertises rates as low as $0.0025 per minute, while Deepgram’s real-time ASR starts closer to $0.0092 per minute. Speechmatics prices higher, around $0.0117 per minute, reflecting its enterprise focus.

What’s often overlooked is concurrency. Free and lower tiers typically allow only a handful of concurrent streams, while production workloads require dozens or hundreds. Scaling concurrency usually requires plan upgrades or custom contracts.

This model works well for developers getting started, but costs rise quickly for real-time or high-volume use.

2. Subscription Plans With Minute Caps

Some platforms bundle speech-to-text into monthly subscriptions with fixed minute limits.

This is common among creator- and product-led platforms like ElevenLabs, Cartesia, and Fish Audio.

For example, ElevenLabs’ Starter plan includes 750 minutes per month, while higher tiers unlock more minutes but still impose caps and additional per-hour charges. Cartesia’s plans scale from hundreds to over 100,000 minutes per month, with concurrency increasing by tier.

These plans are convenient for predictable workloads, but they break down for continuous audio, call centers, or real-time voice agents where usage fluctuates.

3. Base Pricing Plus Feature Add-Ons

This is the most common — and most expensive in practice — pricing model.

Many providers advertise a low base rate, then charge extra for features that are effectively mandatory in production. These often include real-time streaming, speaker diarization, enhanced phone models, word-level timestamps, or higher concurrency.

Deepgram, Google, and Speechmatics all use versions of this model. Once add-ons are enabled, the effective cost can be 2–4× higher than the advertised base price.

This is where teams most often underestimate their true speech-to-text costs.

4. Tokenized or Opaque Pricing

Some platforms abstract speech-to-text pricing behind tokens or credits.

The most visible example is OpenAI, where audio input and output are priced separately. Input audio can cost roughly $0.06 per minute, while generated output may exceed $0.24 per minute, depending on usage.

While flexible for experimentation, this model makes it extremely difficult to forecast costs at scale. Concurrency limits and latency guarantees are often unclear.

Where Pulse Fits — and Why Teams Switch

This complexity is why many teams eventually move to Pulse Speech-to-Text.

Pulse uses an all-inclusive, infrastructure-first pricing model designed for real-time production workloads. Core features such as streaming, speaker diarization, word timestamps, and language detection are included by default rather than sold as add-ons.

This makes it significantly easier to forecast costs for voice agents, call centers, and live transcription systems, where pricing surprises can become expensive very quickly.

Pulse is often cheaper in production, especially once real-time features and concurrency are required.

Speech-to-Text API Pricing Comparison (2026)

The bottomline is, APIs with low headline prices often become expensive once real-time features and concurrency are required. Pulse’s advantage is that production features are not add-ons, which keeps cost predictable as usage scales.