The eternal debate in production ML systems—do you reach for an LLM or stick with traditional machine learning?—finally gets a concrete answer, and it's more nuanced than the hype merchants want you to believe. A developer going by shashank_ms published a hands-on comparison on DEV.to that implements both approaches side-by-side for a customer support triage agent, and the results cut through a lot of noise.

The Use Case That Makes This Worth Reading

The author chose a ticket classification system that needs to do three things: categorize incoming support requests, detect urgency levels, and draft initial responses. It's exactly the kind of hybrid workflow where teams waste weeks in architecture meetings debating philosophy instead of shipping something functional. By implementing both paradigms in one script, shashank_ms removes the abstraction and lets the code speak.

Traditional ML's Strengths Stay Relevant

For structured classification tasks with well-defined categories and clear training data, traditional approaches like logistic regression or gradient boosting still hold advantages that won't disappear with bigger models. Latency is predictable, costs are deterministic, and explainability remains tractable—when a classifier says a ticket is "urgent," you can trace exactly which features triggered that decision.

Where LLMs Change the Equation

The language model approach shines when the classification categories evolve, edge cases multiply, or you need natural language generation for those first-response drafts. The flexibility comes with trade-offs: inference costs scale differently, behavior becomes harder to pin down deterministically, and prompt engineering becomes a legitimate software engineering discipline rather than an afterthought.

Key Takeaways

  • Traditional ML excels when your categories are stable and training data is abundant and well-labeled
  • LLMs win on flexibility, especially for open-ended classification and text generation tasks
  • Side-by-side implementation reveals trade-offs that whiteboard architecture discussions miss
  • The "right" choice often depends on operational constraints as much as model capability

The Bottom Line

This kind of empirical comparison is exactly what the field needs—less vendor marketing, more code you can run yourself. If you're building triage systems or similar classification workflows, spend an afternoon with this implementation before committing to a paradigm. Your users will thank you for the pragmatism.