Optimization Tip Needed: Built a feature across a stack via Cline + OpenRouter. Cost hit $3.5. How to optimize multi-step agent workflows?
I’m a dev building an AI platform.
I recently built a full "Tokenomics & AI Usage Monitoring" feature consisting from 10 steps: DB models, Tracker Services, Admin/PI Routers, and Frontends) using OpenRouter and the Cline extension in VS Code. I primarily used DeepSeek V4 Pro for its amazing price-to-performance ratio.
The issue I noticed: The total cost to build this single feature reached around $3.50. I know it's cheap for the value, but I want to optimize my workflow for scaling.
My workflow:🧱
To avoid confusing the model with my massive codebase, I tried to be as organized as possible:
I provided concise .md files containing the implementation roadmap and phase summaries. ( which I had already done before using Cline and openrouter api key )
I used @file to inject specific context rather than scanning the whole @codebase.
The Dilemma: 💣
🚩If I stayed in the same chat task, the context window blew up (sending the whole chat history + complex DB schemas again), costing me ~$0.50 per message.
🚩If I clicked "Start New Task" for each step, I still had to re-inject the roadmap and core .py files to get the model "up to speed" before coding, which still cost around ~$0.40 just to initiate the step.
❔❔My Question to the pros here:
1.How do you guys handle massive, complex codebases without bleeding tokens on context loading?
2.Are you using Prompt Caching heavily with OpenRouter/Cline for this? If so, how do you set it up effectively?
3.Any specific hacks for multi-step agentic workflows so the AI remembers the "architecture rules" without paying for that context every single prompt?
Would love to hear your advanced workflows!
THANKS ❤🙏