Token Optimization Strategies for LLM-Based Oracle-to-PostgreSQL Migration

Researchers present twelve token optimization strategies for using LLMs to migrate Oracle databases to PostgreSQL, addressing cost and quality degradation challenges. Adaptive routing emerges as the optimal approach, reducing token consumption by 8.72% while maintaining 88.40% semantic match accuracy, demonstrating that token optimization requires balancing multiple objectives rather than simple prompt shortening.

This research addresses a critical pain point in enterprise software modernization: the prohibitive cost and complexity of using large language models for database migration tasks. Oracle-to-PostgreSQL conversion represents a significant challenge because it requires understanding dialect-specific SQL/PL-SQL semantics, schema dependencies, and procedural logic—artifacts that typically consume enormous token budgets when fed directly into LLM contexts. The paper's systematic evaluation of twelve optimization strategies reflects the growing maturity of LLM-as-a-tool approaches in the enterprise space.

The findings reveal important nuances that challenge naive assumptions about token optimization. While aggressive strategies like schema distillation achieve 132% efficiency gains, they sacrifice semantic accuracy by 44 percentage points—an unacceptable trade-off for production migration work. Conversely, mild context pruning preserves 89.75% semantic match while reducing input load, suggesting diminishing returns exist across optimization techniques. This trade-off landscape demonstrates that database migration cannot be solved with prompt-engineering shortcuts.

For enterprises considering LLM-assisted modernization, the adaptive routing approach offers practical value. By reducing both input and output tokens while maintaining acceptable semantic preservation, it lowers operational costs without requiring developers to manually curate migration logic. The research implicitly validates the market for specialized migration tools and platforms that implement these optimization patterns systematically.

Looking forward, this work will likely spawn specialized LLM applications and fine-tuning approaches designed specifically for database migration, similar to how code-specific models emerged from general-purpose LLMs. Organizations maintaining legacy Oracle installations represent a substantial market opportunity for tools that automate semantic-preserving database conversion.

• →Adaptive routing reduces token consumption by 8.72% while maintaining 88.40% semantic accuracy, offering the best practical trade-off for Oracle-to-PostgreSQL migration
• →Aggressive optimization strategies sacrifice semantic fidelity; schema distillation improves token efficiency by 132% but reduces semantic match by 44 percentage points
• →Token optimization requires multi-objective evaluation balancing cost, syntax validity, semantic preservation, and structural fidelity rather than simple prompt shortening
• →Mild context pruning preserves near-baseline semantic quality (89.75%) while reducing model input requirements
• →LLM-based database migration remains a viable use case when optimization strategies are applied strategically across the transformation pipeline