[P] Benchmark: Using XGBoost vs. DistilBERT for detecting "Month 2 Tanking" in cold email infrastructure?

I have been experimenting with Heuristic-based Deliverability Intelligence to solve the "Month 2 Tanking" problem.

The Data Science Challenge: Most tools use simple regex for "Spam words." My hypothesis is that Uniqueness Variance and Header Alignment (specifically the vector difference between "From" and "Return-Path") are much stronger predictors of shadow-banning.

The Current Stack:

• Model: Currently using XGBoost with 14 custom features (Metadata + Content).
• Dataset: Labeled set of 5k emails from domains with verified reputation drops.

The Bottleneck: I'm hitting a performance ceiling. I'm considering a move to Lightweight Transformers (DistilBERT/TinyBERT) to capture "Tactical Aggression" markers that XGBoost ignores. However, I'm worried about inference latency during high-volume pre-send checks.

The Question: For those working in NLP/Classification: How are you balancing contextual nuance detection against low-latency requirements for real-time checks? I'd love to hear your thoughts on model pruning or specific feature engineering for this niche.