Cost of Running Real-Time Moderation
The expensive part is not inference itself; it is maintaining low-latency safeguards while traffic spikes and threat patterns shift.
Inference compute is only one line item. Queue orchestration, feature extraction, and review tooling dominate operating complexity during peak windows.
We budget for burst tolerance first, average traffic second. Under-provisioning for bursts creates the exact blind spots coordinated bot waves exploit.
Human review is intentionally overrepresented in spend. Automated confidence is strong, but ambiguous edge cases are where policy credibility is decided.
The business tradeoff is clear: we spend more upfront to avoid trust debt. Cheap moderation pipelines often push hidden costs onto users through false positives or delayed response.
Real-time safety is an operations problem with product consequences. Treating it as optional infrastructure is usually where quality erodes.