GenAI vs Classical ML: When to Use Which in Production
LLMs are not always the right tool. A decision framework — with real cost-per-inference numbers — for choosing between generative and classical ML in enterprise workloads.
The 'LLM for everything' trap
Foundation models are extraordinary at unstructured tasks — summarisation, extraction from long documents, conversational interfaces. They are also 10–100× more expensive per prediction than a well-tuned gradient-boosted tree. Using GPT-class models for tabular fraud scoring or demand forecasting is almost always the wrong choice.
The T7 decision matrix
We map every use case on two axes: structure of the input (tabular → unstructured) and tolerance for latency and cost. Structured, high-volume, low-latency workloads route to classical ML with feature stores. Unstructured, low-volume, high-value workloads route to LLMs with retrieval augmentation. Hybrid workloads use LLMs as orchestrators calling classical models as tools.
Blended architectures win
The best production systems we ship are hybrid. A logistics client's dispatch engine uses XGBoost for ETA prediction, a fine-tuned small language model for exception summaries, and a large model only for the customer-facing chat surface — cutting inference cost by 87% versus an LLM-only design.
Key takeaways
- Route by input structure and latency budget
- Reserve large LLMs for unstructured, high-value tasks
- Blend classical + generative for cost and accuracy
T7 Research
Enterprise AI Research Group
T7 Research is the research arm of T7 Solution, focused on benchmarking LLMs, evaluating RAG patterns, and compiling implementation playbooks for enterprise technology leaders.