LLM Cost Optimization

Best Practice: LLM Cost Optimization

LLM cost optimization is the practice of reducing the money spent running language model applications while keeping quality acceptable. Costs scale with tokens processed and the model chosen, so spend can grow quickly and unpredictably. Optimization combines several levers: routing easy requests to smaller models, caching repeated prompts and responses, trimming context, batching, and setting limits. It matters because unmanaged LLM costs erode the business case for AI features, and most teams can cut spend substantially with no real quality loss.

LLM spend is driven mostly by tokens processed and the price of the chosen model, both of which compound quickly at scale. The most effective single lever is usually model routing: send the many easy requests to a small, cheap model and reserve a large, expensive model for the few hard ones, escalating only when the cheap model fails a quality check. Prompt caching, which reuses the processing of a stable system prompt or repeated context, can cut both cost and latency. Context trimming, output length caps, batching, and per-tenant budgets round out a practical optimization program.

Step-by-Step Implementation Guidance

1. Measure baseline cost per request, per feature, and per user.
2. Match model size to task difficulty; route easy tasks to cheaper models.
3. Use prompt caching for stable system prompts and repeated context.
4. Trim prompts: remove redundant context and over-long few-shot examples.
5. Cap output length and use streaming where it helps.
6. Batch and deduplicate requests when latency allows.
7. Set per-tenant rate and budget limits to prevent runaway spend.
8. Continuously monitor cost against quality and adjust.

A useful pattern is the model cascade: route every request first to a small, inexpensive model, and escalate to a larger model only when the small one fails a confidence or quality check. Semantic caching extends ordinary caching by reusing answers for queries that are similar in meaning, not just identical in text. Per-tenant rate limits and budgets prevent a single user or runaway loop from generating a surprise invoice. Throughout, the goal is to track cost against a quality metric so savings never quietly degrade the user experience.

Common Mistakes Teams Make When Ignoring This Practice

• Using the largest model for every task by default.
• Re-sending identical context with no caching.
• Bloated prompts with unnecessary tokens.
• No output length limits, paying for verbose answers.
• No budgets or alerts until the invoice arrives.

Tools and Techniques That Support This Practice

• Techniques: prompt caching, model routing, context compression, batching.
• Tools: cost dashboards in Helicone, Langfuse, OpenAI usage reports.
• Patterns: cascade (cheap model first, escalate on failure), semantic caching.

How This Practice Applies to Different Migration Types

• Cloud Migration: Route routine log-analysis queries to cheaper models during migrations.
• Database Migration: Cache repeated schema-analysis prompts to cut spend.
• SaaS Migration: Cap output length on integration-generation calls.
• Codebase Migration: Use a cheap model to triage files and a strong one only for hard translations.

Checklist

• Baseline cost measured per feature
• Model size matched to task difficulty
• Prompt caching enabled for stable context
• Prompts trimmed of redundant tokens
• Output length capped
• Per-tenant budgets and limits set
• Cost monitored against quality