It's 11pm, you're deep in a design doc for the payment settlement service. You've been explaining the idempotency logic for four minutes, getting clearer with each sentence. Then the voice tool stops recording. Word cap hit. You look at the transcript: 412 words. Your thought isn't done. You reopen the editor and type the rest, which takes 8 minutes and feels disconnected from the voice draft.
This is the everyday friction that most voice-to-text tools optimize for someone else's business model, not your workflow.
The pricing model is hidden but it's there
Wispr Flow caps free tier at 500 words per month. Superwhisper, $8.49/month, caps at 2000 words per day. Willow, $12/month, same day cap. The limitation is structural: those companies run speech-to-text on servers. Server inference costs money. Even on tiny models, multiply it across thousands of users, and the cost is real. So they cap the free tier and measure usage obsessively.
But the cap itself tells you something about their cost structure. They're rationing a resource that costs them money.
Local-first changes what's rationed
Whisper runs on your device. Not on a server. That shifts the constraint. Once it's installed, it doesn't cost the maker anything for you to transcribe 50,000 words or 500,000. The variable cost is zero. The bottleneck moves from "expensive server resources" to "does the model exist" (yes, Whisper does) and "can it run fast enough" (yes, Whisper large v3 on modern hardware processes 30 seconds of audio in 2 to 3 seconds).
When there's no variable cost, there's no reason to count.
Recitey uses Whisper locally. No word limit on free tier. No metering. No gate guarding the transcription pipeline.
That's not pricing strategy. That's the logical conclusion of running the work on your machine instead of theirs.
The second reason: code stays code
Marcus is a backend engineer at a Series B fintech. He designs APIs in prose first, explaining how the settlement retry logic should behave, what state machine ensures idempotency, when to backoff. That explanation is his thinking. It's also intellectual property. Code comments, design docs, incident postmortems: they stay on his device.
Cloud transcription means uploading that.
"Encrypted in transit" is the assurance every vendor gives. But for Marcus, even the question "is the data leaving my device?" is disqualifying. It's not paranoia. It's IP policy. Some teams have it, some don't. For the ones that do, the vendor's privacy story doesn't matter. The policy is simpler: if it's code-adjacent, it doesn't touch the internet.
Local Whisper solves that at the architectural level. The audio file never leaves your computer. The transcript never leaves your computer. The only thing that moves is your choice to copy the final text.
Where cloud still makes sense
This is not a universal argument against cloud tools. Superwhisper, Otter.ai, Dragon NaturallySpeaking: they all do things Recitey doesn't. Otter builds searchable archives across your conversations. Superwhisper integrates with notes apps. Dragon handles specialized vocabularies better if you train it on your domain.
But if your constraint is "I need to transcribe design docs, PR descriptions, and Slack threads without worrying about word caps or where the audio goes," local-first wins. Full stop.
The audience for that is specific: developers in IP-sensitive companies, people who draft more than they type, anyone building with Claude or Cursor where the bottleneck isn't speech-to-text speed but the sheer volume of context and intent you need to articulate.
What changes after you stop counting
Marcus did the math. Design docs used to be 300 to 400 words. Now they're 800 to 1200, because he's explaining intent to Claude the same way he'd explain to a junior engineer. The voice tool that caps at 500 words per month isn't a speed upgrade. It's a friction point. He switches to Cursor specifically because Cursor's tab-complete reduces the number of voice rewrites he has to do. That keeps him in the flow.
When he stops worrying about whether the next sentence will trigger the cap, the thinking changes. The prose gets longer, less edited-for-brevity, closer to how he actually explains things. The cleanup still happens (voice is never as clean as typing), but it's refinement, not assembly.
That's what an uncapped free tier enables: you write the way the work now demands, not the way the tool permits.