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

2026-04-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

Amazon Bedrock, utilizing fine-tuned Amazon Nova Micro models, offers a cost-effective solution for text-to-SQL generation, particularly for custom SQL dialects. This approach combines LoRA (Low-Rank Adaptation) fine-tuning with serverless, pay-per-token inference, eliminating the continuous overhead costs associated with persistently hosted custom models. The solution demonstrates two fine-tuning methods: a managed approach via Amazon Bedrock model customization for simplicity and rapid deployment, and an Amazon SageMaker AI training approach for granular control over hyperparameters and infrastructure. An example workload processing 22,000 queries per month incurred a monthly cost of $0.80, significantly less than persistent hosting. Despite a slight increase in inference latency due to LoRA adapters (639 ms cold start, 380 ms warm start), the performance remains suitable for interactive applications, with token generation at 183 tokens per second.

Key takeaway

For MLOps Engineers or Data Scientists building text-to-SQL solutions with custom dialects, consider Amazon Bedrock with fine-tuned Amazon Nova Micro models. This approach offers significant cost savings through on-demand, pay-per-token inference, avoiding the continuous expense of persistent infrastructure. Evaluate whether the managed Bedrock customization or the granular control of SageMaker AI training best fits your team's expertise and operational requirements, ensuring your solution scales efficiently with usage.

Key insights

Fine-tuning Amazon Nova Micro with LoRA on Amazon Bedrock provides cost-efficient, production-ready text-to-SQL for custom dialects.

Principles

• On-demand inference reduces costs for variable workloads.
• LoRA fine-tuning enables cost-effective model customization.
• Managed services simplify ML operations.

Method

Prepare custom SQL datasets, fine-tune Amazon Nova Micro using either Amazon Bedrock's managed customization or SageMaker AI, then deploy for on-demand, pay-per-token inference.

In practice

• Use `sql-create-context` dataset for text-to-SQL training.
• Format data as `bedrock-conversation-2024` JSONL files.
• Invoke custom models via Bedrock Converse API with deployment ARN.

Topics

• Text-to-SQL Generation
• Amazon Nova Micro
• Amazon Bedrock
• LoRA Fine-tuning
• On-demand Inference

Code references

• aws-samples/amazon-nova-samples
• salesforce/WikiSQL
• aws/sagemaker-hyperpod-recipes

Best for: Machine Learning Engineer, Data Scientist, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.