Engineering @ Scale — 2026-05-19#

Signal of the Day#

The most critical insight this period comes from Snapchat’s billion-prediction-per-second ML platform: at massive scale, the “boring machinery” of network transport and data serialization dominates inference costs more than the ML model itself. By refactoring their data plane to transfer features as raw bytes and delaying deserialization until inside the inference engine, they achieved a 2x reduction in latency and a 10x drop in data plane costs.