Speech-to-text budget guide
Audio Transcription API Cost Guide
Estimate the complete cost of transcribing meetings, calls, interviews, or podcasts. Separate speech recognition from transcript cleanup, summaries, and downstream AI work so a low per-minute quote does not hide the rest of the workflow.
Start with the provider's billing unit
Transcription services may charge by audio minute, audio token, or a combination of input and output tokens. Do not convert every service to text tokens before checking its current pricing page. Use the provider's native unit for speech recognition, then estimate any text processing separately.
Planning formula
Audio processing = billable audio minutes or audio tokens × current model rate
Text processing = transcript input token cost + summary or extraction output token cost
Total workflow = audio processing + text processing + retries + storage, streaming, or orchestration costs
Collect these inputs
- Total audio duration: include every channel or track if the provider bills them separately.
- Recordings per month: meetings, calls, uploads, or live sessions processed in the billing period.
- Transcript size: use a sample recording to measure real words or tokens per minute for your language and speaking style.
- Post-processing: speaker labels, cleanup, translation, summaries, action items, sentiment, or structured extraction.
- Retry rate: failed uploads, poor audio, timeouts, and quality-driven retranscription.
- Live versus batch: real-time streaming may use a different model and rate from uploaded-file transcription.
Worked monthly example
A team records 80 meetings per month at an average of 45 minutes. The speech-recognition baseline is 3,600 billable minutes. If 5% must be retranscribed, plan for 3,780 minutes. Next, measure the transcript from a representative 45-minute meeting and use the calculator's text mode to estimate the separate input and output cost for summaries or action items. Multiply that text estimate by 80 meetings, plus any retries of the post-processing step.
Avoid common underestimates
- Silence, introductions, and hold time may still count toward billable duration.
- Long recordings may need chunking, overlap, and a final merge or cleanup pass.
- Diarization and timestamps may affect model choice even when they are not separate line items.
- Translation or a detailed meeting summary is another model call, not part of basic transcription by default.
- Multilingual speech and noisy rooms can change accuracy and retry assumptions.
Validate before scaling
Run a small sample that reflects your real languages, number of speakers, audio quality, and recording length. Record the provider-reported usage, transcript token count, retry rate, and downstream summary usage. That sample gives a safer monthly forecast than a universal words-per-minute assumption.
Official references
Check the current OpenAI API pricing and Audio API FAQ, or the Gemini API pricing page, before making a purchasing decision. Supported models, limits, token conversions, and rates can change.
For the transcript and summary portion, see the input and output token guide or monthly AI API cost guide.