Text-to-SQL Is Easy to Demo but Hard to Run Safely in Production

Building a basic text-to-SQL prototype takes only a few hours using standard tools, but deploying it reliably in production requires significantly more infrastructure. Developers must add parser-level SQL validators, schema-aware plan caches, and evaluation harnesses to catch regressions when models or prompts change. Without these layers, production systems risk executing unintended write operations, incurring repeated model costs, and silently degrading in accuracy. The build-vs-buy calculus therefore shifts from the demo phase to the long-term maintenance burden of owning that stack. Teams for whom natural-language querying is a core product feature may justify building it, while those adding it as a secondary feature are advised to consider embedding a managed pipeline instead.