Batch transcription at scale: turnaround, throughput, and concurrency

Operating batch transcription at scale involves addressing challenges such as throughput, turnaround, concurrency limits, and cost, which become critical when dealing with large volumes of audio files. While per-file latency matters for live calls, throughput—how many hours of audio can be processed per hour—is more crucial for large backlogs. High concurrency, allowing many files to be processed simultaneously, significantly accelerates the process and is more beneficial than model speed for batch operations. Providers like AssemblyAI, which offer unlimited concurrency, can handle large audio volumes efficiently without throttling. Techniques such as splitting long files into chunks for parallel processing can reduce turnaround time significantly. Cost considerations include per-second billing, with no minimums, making it essential to select the right model based on the specific needs of accuracy and volume, rather than solely on speed.

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