Enterprise Log Cost Optimization
- problem
- Log volume and spend growing faster than the value teams got from it.
- approach
- Designed a logging middleware architecture adopted enterprise-wide.
- Introduced log tiering: high-value logs stay hot, the rest move to cheaper storage.
- Rolled out as multiple optimization waves across teams.
- ai_role
- Most of the analysis, design iteration, and implementation ran through AI agents on top of my architecture. I owned the design decisions and review gates.
- outcome
- Substantial reduction in logging spend.

