4. Application-Oriented Cost and Efficiency Optimization

Cottonia provides a set of optimization strategies tailored for different applications, enabling the platform to reduce overall compute costs while significantly saving on Token/inference expenses in high-frequency scenarios:

• Context Semanticization and Cache Reuse: Repeated conversations or code snippets are abstracted into semantic units, cached, and directly reused in similar requests, reducing redundant Token input.

• Prompt Flow Management: Prompts are managed in a pipelined manner during execution (e.g., templating, paragraph separation, partial completion), minimizing the transmission of lengthy context.

• Compiled Inference Decomposition (Coding-aware Inference): For code generation tasks, Cottonia can split large tasks into a three-stage process—“semantic analysis → code snippet generation → merging and testing”—executing each stage at the most suitable node and caching intermediate results.

• Tiered Pricing and Priority Economy: Through a dual-token or credit point system, real-time priority tasks, batch tasks, and cache refresh tasks are differentiated, allowing users to purchase “approximately equivalent” compute or cache services at lower cost.

Impact on AI Coding Scenarios:

• Context Reuse and Caching: Reduces 20%–50% of redundant Token input.

• Inference Decomposition and Parallelization: Lowers average response latency by 20%–40%.

• Resource Market Mechanism: By leveraging compute closer to the edge and hybrid deployment, costs are further compressed and stability improved.