Cut AI token costs and improve code via prompt caching & refactoring

Cut AI token costs and improve code via prompt caching & refactoring targets a $13.5B = 27M software developers x $500 annual spend on AI/code-assist tooling total addressable market with medium saturation and a year-over-year growth rate of 25%+ (developer tools + AI tooling adoption driven by LLM integration).

Key trends driving demand: LLM commoditization -- cheaper/experimental models and larger context windows change how inference is priced and where optimizations matter.; RAG & retrieval-first workflows -- storing and reusing snippets reduces repeated context and yields consistent outputs.; Prompt engineering professionalization -- teams invest in shared prompt libraries and tooling to scale best practices.; Edge and on-device inference -- local models enable fallbacks that cut cloud token spend and improve privacy..