How I Cut AI Transcription Costs Below $0.10/Hour with AWS Spot Instances and Whisper Large-v3 Turbo

A deep dive into building a cost-optimized, self-hosted AI transcription pipeline on AWS - using Spot Instances, SQS queue-based autoscaling, and Whisper Large-v3 Turbo - that beats the OpenAI Whisper API on price.

Most teams building on top of speech-to-text quietly accept the API bill. I didn't. When a side project started costing me $180/month in transcription API fees, I rebuilt the entire pipeline on AWS and got my processing cost below $0.10/hour - cheaper than the OpenAI Whisper API itself.

This post walks through the architecture, the trade-offs, and the specific decisions that made it work. Everything here powers StrikeScribe, the AI transcription platform I built as a solo founder, so these numbers come from real production workloads, not a benchmark toy.

If you're building anything on top of Whisper, Deepgram, AssemblyAI, or the OpenAI audio API, this is the arbitrage most people miss.

The problem: managed transcription APIs don't scale on cost