Latency, Cost, and Token Optimization for AI Apps

Performance Is Product Quality

AI applications can become slow and expensive if teams do not manage context size, retrieval scope, model choice, retries, streaming, caching, and tool latency. Optimization should be designed into the system, not added after costs spike.

Optimization Levers

• Route tasks to the smallest capable model.
• Reduce unnecessary context and retrieved chunks.
• Cache stable outputs, embeddings, retrieval results, and intermediate computations.
• Use streaming for better perceived latency.
• Batch offline jobs when real-time responses are not required.
• Track token usage, retry rate, tool latency, and cost per workflow.

Optimize by Workflow

Not every request needs the same model, context, or latency target. Classify tasks by risk, complexity, freshness, and business value before choosing the expensive path.