Spam and phishing emails remain a major cybersecurity challenge, even after decades of research into reliable detection methods. Modern ML-based spam filters typically rely on text embeddings to represent email content, yet the choice of embedding model and size is often treated as secondary. This work empirically compares a diverse set of sentence embedders to assess how model type and embedding dimensionality influence downstream email spam classification. Using both classical and ML-based classifiers, we evaluate performance across multiple embedding configurations. Our results show that embedder choice-especially embedding size-substantially affects classification performance and generalisation. We observe performance differences of up to 13% overall, alongside variations of 25% in misclassified spam and 10% in misclassified ham across embedders. These findings highlight that embedding models are not interchangeable; rather, their deliberate selection is just as critical as choosing the right classifier when designing AI-based spam detection pipelines.