Insights / Case Study 01
Cost Optimization by Moving to Local LLM Inference
A mid-size B2B product team was spending too much on cloud API inference for high-volume internal workflows. We redesigned the stack around local inference with model adapters, quantization, and optimized serving.
Baseline
Cloud inference bills rising month-over-month
Approach
Local GPU serving + targeted model adaptation
Outcome
Lower recurring inference cost with stable latency
What we changed
We selected a local model family that met quality thresholds for the target tasks, then used parameter-efficient tuning instead of full retraining. Adapter-based methods made iteration cheaper and faster.
We applied quantization and memory-efficient serving patterns to fit throughput targets on fewer GPUs. This removed most per-token external API cost and made spend predictable.
We added observability around prompt classes, token volume, latency, and fallback rates so cost and quality regressions could be caught early.