TokenOps: Reducing Cost, Latency, and Carbon in LLM Workflows through Token-Aware Middleware

This preprint introduces TokenOps, a compiler-inspired middleware architecture designed to optimize token usage in large language model (LLM) API workflows. Developed through applied research at Chitrangana.com, TokenOps implements a dual-layer optimization system that wraps around LLM APIs — using preprocessing to compress input prompts and postprocessing to reduce verbose outputs.

Simulations across 5,000 enterprise prompt-response pairs demonstrated average token savings of 40–60%, with significant reductions in latency and computational overhead. The framework also models environmental impact, estimating carbon savings based on reduced token throughput.

The paper positions TokenOps as both a technical enhancement and a sustainability layer — with applications in cost optimization, infrastructure design, and equitable AI access. It proposes strategic integration into LangChain, LLM agent systems, and semantic orchestration stacks.

This work reflects Chitrangana’s consulting experience across over 1,850 digital transformation projects and contributes to emerging standards for efficient and environmentally responsible AI deployment.