In this post, we demonstrate 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 costs savings compared to a persistently hosted model infrastructure.

To demonstrate this approach in practice, we provide two complete implementation paths that address different organizational needs. The first uses the managed model customization of Amazon Bedrock for teams prioritizing simplicity and rapid deployment. The second uses Amazon SageMaker AI training jobs for organizations requiring more granular control over hyperparameters and training infrastructure. Both implementations share the same data preparation pipeline and deploy to Amazon Bedrock for on-demand inference. The following are links to each GitHub code sample:

The following architecture diagram illustrates the end-to-end workflow, which encompasses data preparation, both fine-tuning approaches, and the Bedrock deployment path that enables serverless inference.

Our demonstration uses the sql-create-context dataset. This dataset is a curated combination of WikiSQL and Spider datasets containing over 78,000 examples of natural language questions paired with SQL queries across diverse database schemas. This dataset provides an ideal foundation for text-to-SQL fine-tuning due to its variety in query complexity, from simple SELECT statements to complex multi-table joins with aggregations.