Text-to-SQL generation remains a persistent challenge in enterprise AI applications, especially when dealing with custom SQL dialects or domain-specific database schemas. While foundation models (FMs) perform well on standard SQL, achieving production-grade accuracy for specialized dialects typically necessitates fine-tuning. However, this introduces an operational trade-off: hosting custom models on persistent infrastructure incurs continuous costs, even during periods of zero utilization.
Amazon Bedrock’s on-demand inference, combined with fine-tuned Amazon Nova Micro models, offers an effective alternative. By integrating the efficiency of LoRA (Low-Rank Adaptation) fine-tuning with serverless, pay-per-token inference, organizations can develop custom text-to-SQL capabilities without the significant overhead of continuously hosted models. Despite the minor inference time overhead introduced by applying LoRA adapters, testing has confirmed latency suitable for interactive text-to-SQL applications, with costs scaling purely by usage rather than provisioned capacity.
This post demonstrates two approaches to fine-tune Amazon Nova Micro for custom SQL dialect generation, delivering both cost efficiency and production-ready performance. For instance, an example workload processing 22,000 queries per month maintained a monthly cost of just $0.80, showcasing substantial savings compared to persistently hosted model infrastructure.
To deploy these solutions, the following prerequisites are necessary:
- An AWS account with billing enabled.
- Standard IAM permissions and roles configured to access:
- Amazon Bedrock Nova Micro model.
- Amazon SageMaker AI.
- Amazon Bedrock Model customization.
- Quota for an
ml.g5.48xlinstance for Amazon SageMaker AI training.
The solution involves these high-level steps:
- Prepare your custom SQL training dataset with I/O pairs specific to your organization’s SQL dialect and business requirements.
- Initiate the fine-tuning process on the Amazon Nova Micro model using your prepared dataset and chosen fine-tuning approach:
- Utilize Amazon Bedrock model customization for streamlined deployment.
- Employ Amazon SageMaker AI for fine-grained training customization and control.
- Deploy the custom model on Amazon Bedrock to leverage on-demand inference, eliminating infrastructure management while paying only for token usage.
- Validate model performance with test queries tailored to your custom SQL dialect and business use cases.
To practically demonstrate this approach, we provide two complete implementation paths addressing different organizational needs. The first leverages Amazon Bedrock’s managed model customization, ideal for teams prioritizing simplicity and rapid deployment. The second utilizes Amazon SageMaker AI training jobs, suitable for scenarios requiring greater control and customization.