~/log / match-the-model-to-the-task
Match the model to the task: how my SEO agent went from $560 to $11 a year
#ai-ops #cost #seo
My SEO agent rewrites metadata, generates alt text, injects structured data, and writes three blog posts a week for two production sites. It originally ran on Claude, and the AI bill came to roughly $560/year.
Then I asked an uncomfortable question: how much of this work actually needs a frontier model?
The honest answer was almost none of it. The agent’s tasks are production work — high-volume, well-specified, checkable:
- Rewrite this meta description under 155 characters, targeting this keyword
- Generate alt text for this product photo
- Draft a blog post from this keyword brief and outline
None of that requires deep reasoning. It requires following a spec, and cheap models follow specs fine. I swapped the brain to DeepSeek and the annual AI cost went from ~$560 to ~$11. I watched rankings and click-through for regressions. There weren’t any.
The general principle
Agent work splits into two kinds:
- Judgment work — deciding strategy, evaluating trade-offs, anything where a wrong answer is expensive and hard to detect. Use the best model you can get.
- Production work — executing a defined task at volume, where output quality is easy to verify. Use the cheapest model that passes your quality bar.
Most agent systems I see get this backwards: they burn frontier-model tokens on production work because that’s the model the developer likes, and the cost makes the whole system feel unviable. Across my fleet the pattern holds — Claude does strategy and proposals, commodity models do the volume, and the marginal cost of an agent-run business stays near zero.
The uncomfortable question is worth asking of every agent you run: is this judgment, or is this production?