AssemblyAI uses EC2 Spot to reduce speech inference costs
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When running speech-to-text inference on AWS, AssemblyAI put GPU-equipped Amazon EC2 Spot instances into production, combined with On-Demand instances and Amazon ECS scheduling; when Spot instances are interrupted, a retry mechanism via an Amazon SQS queue takes over the tasks. AWS says this approach reduces inference compute costs by 70% compared with On-Demand pricing. For end customers, the impact of Spot interruptions is a slight increase in processing time, while transcriptions still complete successfully and are delivered as expected. AssemblyAI's speech-to-text API is now available on AWS Marketplace.
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- AWS Machine Learning BlogCost-optimizing AssemblyAI speech-to-text inference on AWS with Amazon EC2 Spot
AssemblyAI runs GPU-based speech-to-text inference on Amazon ECS using a mix of EC2 Spot and On-Demand Instances across g4dn, g5, and g6 GPU families, with an Amazon SQS-based retry mechanism that returns in-flight jobs to the queue when a Spot Instance receives a 2-minute interruption notice. The architecture is deployed independently in us-west-2 and eu-west-2 for data residency, and AssemblyAI reports 70 percent cost savings on inference compute compared to On-Demand pricing while maintaining SLA commitments.
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