Cutting LLM Costs Without Cutting Quality [Testμ 2026]

At Testμ Conf 2026, Databricks technical evangelist Viktoria Semaan argued that controlling LLM costs and proving model quality require the same foundation: a task-specific evaluation set that measures accuracy, latency, and cost across models. She recommended starting with the smallest suitable model, escalating only when evaluations show it fails, and using a model-agnostic gateway to avoid provider lock-in while centrally managing routing, budgets, access policies, and traffic experiments. Her demonstration of 16 models found that smaller open-weight models can match proprietary alternatives on straightforward classification tasks at far lower cost, although frontier models remain more effective for complex reasoning tasks such as writing human escalation briefs. Fine-tuning should be reserved for cases with recurring errors, several thousand labeled examples, and unsuccessful prompting or retrieval-augmented generation attempts; it improved classification performance but did not replace frontier reasoning in every scenario. Semaan also emphasized continuous evaluation using production traces, smart routing based on task complexity, and caching, prompting, and RAG as additional optimization methods, while identifying model choice as the largest potential source of savings.

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