AI Cost Optimisation

AI cost optimisation is the practice of managing and reducing the expenses associated with deploying AI systems while maintaining acceptable quality. As organisations scale AI usage, costs can grow rapidly — making optimisation a critical operational concern.

Where AI costs come from

• API usage: Pay-per-token pricing for cloud AI services (the most common cost for most organisations)
• Compute infrastructure: GPU/TPU costs for training or self-hosting models
• Data preparation: Cleaning, labelling, and managing training data
• Development: Building and maintaining AI integrations and workflows
• Monitoring: Ongoing evaluation, testing, and quality assurance

API cost optimisation strategies

• Model selection: Use the cheapest model that meets your quality requirements. Simple tasks (classification, extraction) often work well with smaller, cheaper models. Reserve expensive models for complex reasoning.
• Prompt engineering: Shorter, more focused prompts use fewer input tokens and cost less. Remove unnecessary context and instructions.
• Caching: Store and reuse responses for identical or similar requests. If 100 users ask the same question, call the API once.
• Batching: Group multiple requests into batch jobs (often available at 50 percent discount).
• Output length control: Set maximum token limits on responses to prevent unnecessarily long outputs.
• Routing: Build systems that route simple queries to cheap models and complex queries to expensive ones.

Infrastructure cost optimisation

For organisations running their own models:

• Quantization: Reduce model precision to run on less expensive hardware
• Spot instances: Use discounted cloud computing capacity for non-urgent workloads
• Auto-scaling: Scale infrastructure up during peak usage and down during quiet periods
• Model distillation: Train smaller, faster models that mimic the behaviour of larger ones

Cost monitoring

Effective cost management requires visibility:

• Track costs per use case, team, and application
• Set budgets and alerts for unusual spending
• Monitor cost per outcome (cost per customer query resolved, cost per document processed)
• Compare the cost of AI against the cost of manual alternatives

Common cost traps

• Over-specifying models: Using GPT-4 for tasks that GPT-3.5 handles equally well
• Verbose prompts: Including extensive system instructions when shorter prompts would suffice
• No caching: Making redundant API calls for identical requests
• Unbounded generation: Not setting output length limits, leading to unnecessarily long responses
• No monitoring: Discovering excessive costs only at month-end billing

The ROI framework

Cost optimisation should be evaluated in the context of value delivered. An AI system costing £5,000 per month that saves 200 hours of manual work (worth £10,000+) has strong ROI even before optimisation. The goal is not minimum cost but maximum value per pound spent.