You're 11 minutes into a design doc. The thought is still hot. You're speaking faster than you'd type, explaining how the payment settlement flow should handle race conditions if two requests hit the same account simultaneously. Three more minutes of thinking out loud. Then your dictation stops. Word limit reached. You have 247 words of good prose and three minutes of lost context.
Tomorrow morning you'll stare at those 247 words and try to reconstruct the context. You'll add three sentences that don't quite match the cadence of the voice draft. You'll rewrite twice more before it feels continuous. What took 11 minutes to think out loud takes 30 minutes to manually assemble.
This is the word cap problem.
Cloud Transcription Became the Default
Voice transcription tools launched as premium features. Then they became table stakes. Wispr Flow arrived at $14 per month. Willow at $12. Both offered free tiers to build the habit. Neither wanted you using them at scale without paying.
Wispr caps the free tier at 1500 words per month. Willow similar. Superwhisper at $8.49 per month, also capped. The free tiers felt generous at first. Then you hit the cap mid-sentence.
The word cap isn't a technical limitation. It's a pricing mechanism. Cloud transcription does cost money: API calls to a hosted model, storage of audio for training, infrastructure. A user who dictates 100,000 words per month costs the service more than a user who dictates 1500. The cap enforces a ceiling on server load and keeps marginal costs predictable.
But that pricing logic makes sense only if you believe cloud transcription is the only approach. Most SaaS companies built on hosted APIs because that's how software worked for the last decade. The subscription model followed naturally: you use more, you pay more. The free tier becomes a loss leader that drives upgrades.
For most users, that economics works. For developers, it doesn't.
Why the Word Cap Breaks Developer Workflows
Developers understand cost structure. You've provisioned Postgres and seen the difference between on-device compute and API calls. You know that running a language model locally on your laptop costs exactly zero dollars after the initial model download. No API calls. No per-word metering. No variable cost. The cap isn't protecting expensive cloud infrastructure; it's a business decision to force payment.
For developers, the word cap creates a specific kind of pain: cognitive interruption at the moment of highest flow.
You're writing a design doc at 11pm. The thought is fast. You're explaining a race condition, then a retry strategy, then the idempotency key approach, then the database transaction scope. You're not dictating one idea at a time. You're thinking out loud, building on the previous idea, correcting yourself, moving forward. The draft is rough but coherent. You're still hot.
Then the tool stops. Word limit.
You have three choices:
- Stop mid-thought and continue in a second doc (loses continuity)
- Stop and switch to typing (loses speed)
- Stop and come back tomorrow (loses the context and you'll have to reconstruct it)
None of those choices are acceptable when you're in flow.
Recitey: The Structural Difference
Recitey runs Whisper locally on your Windows device. No cloud upload. No word limit. No counter ticking down while you explain intent to your AI pair. The free tier isn't metered. It isn't capped.
The reason is structural. Whisper's base model runs entirely on your machine. It doesn't call an API. It doesn't upload audio for processing or storage. You speak. The local model transcribes. The text appears on your device. That's all.
Locally processed speech-to-text has one hard cost: the time the model takes to process. Whisper-large-v3 hits 96.3% accuracy on LibriSpeech, which is the benchmark everyone uses for comparing transcription accuracy. That accuracy comes with latency. You speak a sentence. The model processes for roughly one second. Then the text appears.
There is no per-word cost. There is no usage-based cap. You can dictate 10,000 words, 100,000 words, a million words. The cost is the same: zero dollars in subscriptions. Zero data leaving your device.
The Trade-off: Local Processing Speed vs. Cloud Responsiveness
Cloud transcription is faster. Google Cloud Speech-to-Text processes while you're still speaking, sometimes returning text before you've finished the sentence. Whisper processed locally is slower. You stop speaking. The model processes. The text appears.
If you're used to cloud transcription, the local latency is noticeable. It's not a bug. It's not a glitch. It's the inherent trade-off of on-device processing.
But that trade-off only matters if the alternative isn't already breaking your workflow.
If you're hitting a word cap mid-design-doc, losing your flow, fragmenting your prose across multiple sessions, and then manually stitching it back together the next morning, the one-second latency on local Whisper is not the problem. The problem is already solved. The interruption is gone. You can dictate the full design doc in one sitting.
Who This Matters For
Marcus works as a backend engineer at a fintech in Stockholm. He writes payment settlement logic. He builds in Cursor specifically because Cursor's tab-complete reduces voice rewrites compared to VS Code. He refuses to use cloud transcription for design docs because the settlement algorithms are proprietary. The IP concern matters.
He also values uninterrupted thinking. His design docs are long. His explanations are detailed. A design doc for a new settlement feature might run 3000 to 5000 words when dictated. Wispr's 1500 word free cap is completely inadequate. He'd hit it before finishing the first major section.
For Marcus, Recitey's local Whisper with no cap isn't a feature upgrade. It's a different category of tool. It's the tool that lets him think out loud for the full duration of the thought, without interruption, without worrying about metering.
For developers building in Claude Code or Cursor, where the new bottleneck is explaining intent to your LLM, local Whisper without a word cap is the right tool for the workflow. Not faster. Not prettier. Not cloud-first. Just unconstrained in the moment when you need to think.