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

Text-to-SQL generation remains a persistent challenge in enterprise AI applications, particularly when working with custom SQL dialects or domain-specific database schemas. While foundation models (FMs) demonstrate strong performance on standard SQL, achieving production-grade accuracy for specialized dialects requires fine-tuning. However, fine-tuning introduces an operational trade-off: hosting custom models on persistent infrastructure incurs continuous costs, even during periods of zero utilization.

The on-demand inference of Amazon Bedrock with fine-tuned Amazon Nova Micro models offers an alternative. By combining the efficiency of LoRA (Low-Rank Adaptation) fine-tuning with serverless and pay-per-token inference, organizations can achieve custom text-to-SQL capabilities without the overhead cost incurred by persistent model hosting. Despite the additional inference time overhead of applying LoRA adapters, testing demonstrated latency suitable for interactive text-to-SQL applications, with costs scaling by usage rather than provisioned capacity.

This post demonstrates two approaches to fine-tune Amazon Nova Micro for custom SQL dialect generation to deliver both cost efficiency and production-ready performance. Our example workload maintained a cost of $0.80 monthly with a sample traffic of 22,000 queries per month, which resulted in cost savings compared to a persistently hosted model infrastructure.

To deploy these solutions, you will need the following: an AWS account with billing enabled, standard IAM permissions, and a role configured to access Amazon Bedrock, Nova Micro model, Amazon SageMaker AI, and a quota for ml.g5.48xl instance for Amazon SageMaker AI training.

The solution consists of several high-level steps: prepare your custom SQL training dataset, start the fine-tuning process on Amazon Nova Micro model, customize Amazon Bedrock model for streamlined deployment, and deploy the custom model on Amazon Bedrock for on-demand inference, removing infrastructure management while paying only for token usage. Validate model performance with test queries specific to your custom SQL dialect and business use cases.