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Cost Optimisation for ML and LLM Workloads

Practical ways to reduce the cost of training, serving and calling models without hurting quality.

Editorial team 1 min read

AI costs can grow quickly. Systematic optimisation keeps them sustainable.

Measure First

Track cost by model, feature, team and customer. Find the biggest contributors before optimising.

For LLM APIs

  • Use smaller models for simpler tasks, with routing.
  • Prompt caching for repeated prefixes.
  • Batch APIs for non-urgent work.
  • Shorter prompts and output limits.
  • Cache responses for repeated questions.

For Self-Hosted Models

  • Quantisation and efficient serving frameworks.
  • High GPU utilisation through batching.
  • Autoscaling to demand.
  • Spot capacity for flexible workloads.

For Training

  • Start from pretrained models.
  • Smaller experiments before large runs.
  • Stop unpromising runs early.

Quality Guardrails

Measure quality alongside cost; savings that degrade user experience are false economies.

Review Regularly

Model prices and capabilities change quickly. Re-evaluate options periodically.

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