When you're writing design specs and incident postmortems by voice, word caps feel arbitrary. Cloud-based dictation tools meter free users at 500 or 1000 words, forcing you to think about token budgets mid-flow instead of finishing your thought. That friction breaks the one thing voice is supposed to give you: the ability to capture intention faster than you can type.
The bottleneck shifted
A few years ago, developers typed code. The constraint was how fast your fingers could move. Voice dictation made sense as an accent feature: quick thoughts, commands, short text.
That's not the constraint anymore.
Now the work is prompt engineering and specification writing. You're explaining to an LLM what should be built. That explanation takes longer, requires more precision, and benefits more from the fluency of spoken language than from keystrokes.
Marcus, a backend engineer at a Series B fintech, started writing design docs by voice when Cursor added the feature. Not because he types slowly. Because he explains better when he talks. The rhythm of speech captures nuance that typing doesn't. He'd spend 15 minutes typing a spec, or 5 minutes voicing it and 3 minutes cleaning it up. The math favored voice.
Then he hit Wispr's word cap at 400 words mid-document. Lost his train of thought. Spent the next morning fragmenting the prose. The cap, not his voice speed, became the friction.
Most pricing models are backwards
Here's the economics of voice dictation: Whisper, the model most consumer tools use, runs on commodity GPUs. OpenAI open-sourced it. Running it locally on your machine, which Recitey does on the free tier, costs zero after the initial model download. No per-word cost. No metering. No math.
The real expense is the cloud rewrite polish: taking rough transcription and turning it into clean, structured prose. That's neural compute too. That's where the cost lives.
But most premium voice tools price the dictation itself, not the rewrite.
Wispr charges $14/month after 1000 words. Superwhisper charges $8.49/month for 20 minutes of dictation per day, capped at 600 words/month on the free tier. Willow prices by the minute. They're all metering something that costs zero to run locally.
The pricing model reveals the truth: they're not charging for compute. They're charging for distribution, for the brand, for the idea that voice dictation is a premium feature. It isn't. The compute is cheap. The distribution cost is the thing.
Recitey's model inverts that. Free tier runs Whisper locally with no word limit, because the cost is actually zero. Premium is for cloud rewrite polish if you want it. The pricing follows the actual cost structure.
For Marcus, that meant no more thinking about caps. No more fragmenting a thought across two sessions. The workflow flowed.
Code IP is not a small concern
Marcus refused to use cloud dictation at first, not because of speed, but because he works on payment settlement systems. Code he speaks gets no second pair of eyes before it leaves the device.
This is not paranoia. Enterprise compliance, IP protection, and regulated-industry code all have the same constraint: what you dictate needs to stay yours until you've edited it.
Most cloud voice tools note privacy in their ToS like it's optional. Recitey's model makes it structural: speech-to-text runs locally. No transmission. The only time anything leaves your device is if you explicitly choose the cloud rewrite, and even then, only the rough transcript, not the source code context.
For developers in regulated spaces, that's not a feature. It's a requirement.
How the pieces fit together
Recitey's technical choice, running Whisper on your local machine, shapes everything downstream.
Local processing means: no latency, no metering, no cloud dependency, no IP concerns, no 'what if they change the pricing' anxiety.
The rough transcript quality is the same as cloud tools. Whisper-large-v3 hits 96.3% accuracy on LibriSpeech, regardless of where the model runs. The difference is what happens next.
If you want the transcript polish and structural cleanup, Recitey can do that in the cloud. Optional. Your choice. But the baseline, the speech-to-text that costs zero, is always local.
Who this is for, and who it isn't
This model works for developers and builders who write longer-form specs, docs, and communication by voice. People whose bottleneck is getting intention out of their head into a tool faster than typing allows.
It doesn't work for someone who uses voice for quick text messages or short-form content. Those people hit the caps anyway, and they might prefer a tool that just handles the full stack end-to-end without decisions about local vs cloud.
And if you work in an environment where policy requires all processing to stay on-device, Recitey's local-first approach is a fit. If you're okay with code leaving your machine for polish, cloud tools work fine.
The truth is simpler: most voice dictation tools price wrong because they price for a problem that was solved years ago. The bottleneck now is intention writing, not voice speed, and tools that understand that difference change how you work.