Cost-efficient custom text-to-SQL using Amazon Nova Micro and Amazon Bedrock on-demand inference

AWS has introduced a novel architecture for deploying custom text-to-SQL models that dramatically reduces operational costs. The solution leverages Amazon's Nova Micro foundation model, fine-tuned using the LoRA (Low-Rank Adaptation) technique for specific SQL dialects, and deploys it via Amazon Bedrock's on-demand, serverless inference. This approach solves a key enterprise pain point: the high cost of persistently hosting a fine-tuned model, which incurs charges even during periods of zero usage. By shifting to a pay-per-token model, costs now scale directly with application traffic.

In a practical demonstration, AWS fine-tuned Nova Micro on a combined dataset of over 78,000 examples from WikiSQL and Spider. The resulting custom model, deployed on Bedrock, maintained production-ready latency suitable for interactive applications. Crucially, the cost analysis showed that processing a sample traffic of 22,000 queries per month would incur a monthly cost of approximately $0.80. AWS provides two implementation paths: a simplified method using Bedrock's managed model customization and a more granular approach using Amazon SageMaker AI training jobs, both culminating in serverless deployment.