Content Moderation at Platform Scale

Content moderation systems continuously classify text, images, video, and user activity to detect spam, abuse, policy violations, and unsafe content. These systems operate under strict latency constraints and enormous throughput requirements, often requiring billions of classifications per day.

• Problem: Moderation pipelines are expensive to operate because every piece of content is sent through full inference, even when many decisions are obvious or low-risk.
• With moco: moco automatically routes straightforward moderation decisions through cheaper decision paths while reserving full model inference for ambiguous or high-risk cases.
• Impact: 20–50% reduction in moderation inference cost and GPU utilization while maintaining moderation quality and platform responsiveness.