Cost-optimizing AssemblyAI speech-to-text inference on AWS with Amazon EC2 Spot | Amazon Web Services
Speech-to-text inference is computationally intensive. Each audio file triggers a pipeline of deep learning models (acoustic models, language models, and post-processing stages) running on NVIDIA GPUs to meet latency requirements. At scale, this means thousands of GPU instances running continuously.
AssemblyAI needed a way to run GPU-intensive inference workloads cost-effectively at scale, without sacrificing the availability and latency guarantees their customers depend on. Running entirely on On-Demand Instances would be cost-prohibitive. Spot Instances offer significant savings, while Amazon Elastic Compute Cloud (Amazon EC2) can reclaim them with a 2-minute warning when capacity is needed elsewhere. AssemblyAI needed an architecture that absorbs Spot interruptions gracefully while maintaining service level agreement (SLA) commitments. Additionally, customers in regulated industries, including healthcare, finance, and legal, must keep audio data within specific geographic boundaries. They implement strict data residency configurations in European AWS Regions.
Available on AWS Marketplace, AssemblyAI provides an API for transcription,...
Copyright of this story solely belongs to aws.amazon.com. To see the full text click HERE
