You're explaining the settlement architecture at 11pm. Voice is faster than typing, and you're not stopping to rephrase the technical detail.
Then the app stops listening. You've hit the word cap.
The next morning, the doc is fragmented. Some sections are dense with thinking. Others are bullet points you meant to expand into full paragraphs. The flow was clear when you were speaking. Now it's broken into pieces.
The Moment This Happens
Marcus is a backend engineer at a fintech in Stockholm. His Tuesday nights are usually spent building design documents for payment reconciliation, the state machines, the edge cases, the retry logic.
He switched to Cursor (not VS Code) specifically because Cursor's tab-complete catches the intent he didn't finish voicing. He doesn't have to repeat himself. The tool understands the direction he's heading.
He used to use a popular cloud dictation tool. Reliable. Clean output. Accurate.
Then he started including code snippets in design doc comments. Architecture patterns. Configuration defaults. Details about how payment settlement works, sensitive in a fintech context. Everything he voiced was traveling upstream. The privacy policy said "for improving the service," which meant training data.
He stopped using that tool.
For six months, he went back to typing. Speed cost him thinking clarity. Then he tried cloud dictation again last month. The speed seemed worth the risk.
He was voicing a design doc on the reconciliation flow. Building momentum explaining the state machine. Describing edge cases around partial payment failure. Walking through retry logic and timeout decisions.
The word counter hit the limit mid-sentence.
He finished the document typing manually the next morning. Sixteen minutes of cleanup. Fragmented prose. Broken thinking.
Why the Limit Exists Everywhere Else
Most voice-to-text companies use the same pricing model.
Wispr Flow charges $14 per month for Pro. The free tier is capped at roughly 600 words per day. Superwhisper is $8.49 monthly, with similar restrictions. Willow caps free users at a lower ceiling still.
They're not being stingy. They're protecting margin.
Cloud speech-to-text requires server infrastructure. Every word you dictate burns compute: running the model, storing the request, streaming the response. If you transcribe 10,000 words, the company is paying for 10,000 words of inference.
The economic logic is simple: limit the free tier, push users to upgrade, and those upgrades fund the infrastructure cost.
That model only works if your marginal cost per word is real.
The Structural Difference: Local Whisper
Whisper (OpenAI's speech recognition model) can run two ways: in the cloud, where it costs the company something, or locally on your device, where it costs nothing after the first download.
Recitey runs Whisper locally on Windows. Your device does the heavy lifting. The model downloads once. After that, the marginal cost of another 10,000 words is zero. Not nearly free. Zero.
No server time. No bandwidth metering. No per-word accounting. No infrastructure cost.
When OpenAI released Whisper in 2022, almost every company built a cloud-hosted wrapper. Recitey chose the opposite: a local-first architecture. Nothing leaves your device unless you explicitly ask for a rewrite.
For developers, this distinction matters deeply. Code snippets don't travel upstream. Architectural decisions stay on your drive. Private conversations with your team aren't fed into a model you don't control.
Latency also changes. Local inference runs at roughly 1 to 2 seconds end-to-end on modern Windows hardware. No server round-trip. No network lag. You speak, the device understands, the text appears.
Why Recitey Has No Word Limit
Most SaaS pricing exists because the company needs one.
Wispr, Superwhisper, Willow need caps because they need revenue reasons to upgrade. Free users hit the limit. They feel the friction. They subscribe to Pro.
That model only works if the company is actually paying per word.
Recitey isn't. The free tier offers uncapped Whisper. Dictate a 2,000-word design doc. No counter. No upsell. Just speech-to-text, running locally, working as long as you need.
The Pro tier exists for the part that costs money: the rewrite. That's cloud-based. LLM compute. Your rough voice notes get polished into structured prose. That part is expensive. That's what Pro covers.
Everything else is free because it costs nothing to give to you.
What Changes in Your Workflow
Marcus stopped thinking about word budgets.
With an uncapped free tier, he's not fragmenting the design doc across multiple dictation sessions. He's not checking the counter mid-thought to see if he has fifty words left. He's not switching to typing halfway through and losing the thread.
He dictates the whole thing. Rough. Sometimes repeated. Sometimes with false starts and backtracking.
Then, if he wants polish, he runs the voice memo through Pro. The prompt is simple: "Fix grammar, keep every technical detail, organize this into clear sections."
That takes thirty seconds. The edit takes two minutes. The typing is zero.
The trade-off he made, from cloud dictation (more accurate) to local dictation (faster, flow-preserving, private), suddenly became worth it. He gained two hours of his night back.
The Honest Trade-Off
Local Whisper isn't perfect. It makes mistakes. Homophones. Technical terms from domains it hasn't seen. Code that garbled mid-sentence.
It's not better than professional transcription services. Dragon NaturallySpeaking is more accurate. Otter.ai catches more edge cases.
But Recitey is faster. For a design doc at 11pm, when preserving your thinking matters more than flawless accuracy, fast is the right trade-off.
Rough voice notes beat fragmented prose written under time pressure. The rewrite catches mistakes if you decide they matter.