AI / ML platform
AI platform infrastructure review
Series B AI startup running inference and batch workloads across AWS with rising GPU spend and on-call fatigue.
Key findings
- Oversized GPU nodes with idle capacity outside peak inference windows
- Autoscaling policies tuned for CPU, not queue depth or token latency
- Missing SLO signals for inference paths and embedding pipelines
- Runbooks and ownership unclear during queue backlogs
- Backup and DR paths untested for vector and model artifact stores
Outcomes delivered
- Right-sized GPU pools with queue-aware scaling signals
- Reduced monthly compute spend without sacrificing SLA headroom
- Clearer on-call playbooks for inference and batch failures
- Executive view of cost drivers tied to product usage