Good practical framing - agent workflows are where cost optimization gets genuinely hard, because it's not one call you can tune, it's a tree of calls where a bad routing decision early multiplies downstream. The thing I'd emphasize most for agents specifically: the cost killer is usually re-sending fat context on every step of a long loop, plus running the top-tier model on steps that are pure tool-glue. Both compound over a multi-turn run.
I built Moonshift around exactly this problem - a multi-agent pipeline (prompt to a shipped SaaS on your own GitHub + Vercel) where the optimization is structural, not a tuning afterthought: each step is routed to the cheapest capable model and context is scoped per-agent rather than dragged along whole. That's what gets a full build to ~$3 flat. First run's free, no card. Solid guide - do you handle model selection statically per node, or dynamically based on task difficulty at runtime? The dynamic version is more powerful but I've found the routing classifier itself can get expensive if you're not careful.
glad you liked it. tbh I lean dynamic with a cheap classifier, like Haiku or a tiny fine-tune, and cache its decisions per task type. guide covers keeping that router's cost under 1% of total spend.
For further actions, you may consider blocking this person and/or reporting abuse
We're a place where coders share, stay up-to-date and grow their careers.
Good practical framing - agent workflows are where cost optimization gets genuinely hard, because it's not one call you can tune, it's a tree of calls where a bad routing decision early multiplies downstream. The thing I'd emphasize most for agents specifically: the cost killer is usually re-sending fat context on every step of a long loop, plus running the top-tier model on steps that are pure tool-glue. Both compound over a multi-turn run.
I built Moonshift around exactly this problem - a multi-agent pipeline (prompt to a shipped SaaS on your own GitHub + Vercel) where the optimization is structural, not a tuning afterthought: each step is routed to the cheapest capable model and context is scoped per-agent rather than dragged along whole. That's what gets a full build to ~$3 flat. First run's free, no card. Solid guide - do you handle model selection statically per node, or dynamically based on task difficulty at runtime? The dynamic version is more powerful but I've found the routing classifier itself can get expensive if you're not careful.
glad you liked it. tbh I lean dynamic with a cheap classifier, like Haiku or a tiny fine-tune, and cache its decisions per task type. guide covers keeping that router's cost under 1% of total spend.