Guides ยท Technology
ML Inference Cost Optimization
Cut serving costs without quality loss
Reducing ML inference cost involves choosing efficient hardware, batching, quantization/distillation where acceptable, caching results, and autoscaling on SLO-relevant metrics while monitoring latency/quality tradeoffs.
- inference cost
- batching
- quantization
- autoscaling
- distillation
Tune Models
Quantize/distill when quality allows; cache common results.
Right Size
Pick efficient hardware; batch requests; set concurrency and autoscale.
Watch Tradeoffs
Monitor latency and quality metrics; rollback if regressions.
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