A Practical Guide to LLM Cost Control

LLM costs grow faster than you expect

Building with large language models is exciting until the first invoice arrives. A prototype that costs pennies per day can balloon to thousands per month when real users start making real requests. The good news: cost control is an architecture decision, not an afterthought.

Measure before you optimize

You cannot control costs you cannot see. Before making any changes, instrument your LLM calls:

• Per-endpoint costs: Track token usage and cost for each API endpoint or feature. Know which features are expensive.
• Per-user costs: Identify power users and abuse patterns. A single heavy user can dominate your bill.
• Model distribution: What percentage of calls use GPT-4o vs GPT-4o-mini? Are you using the expensive model when a cheaper one would work?

Route to the right model

Not every request needs the most capable (and expensive) model. A tiered routing strategy can cut costs by 60-80%:

• Simple tasks: Classification, extraction, formatting. Route to GPT-4o-mini or Haiku. Fast, cheap, sufficient.
• Medium tasks: Summarization, rewriting, basic reasoning. Route to GPT-4o or Sonnet. Good balance of quality and cost.
• Complex tasks: Multi-step reasoning, creative writing, detailed analysis. Route to Opus or o1. Expensive but necessary.

Automate this routing with a lightweight classifier that evaluates request complexity before selecting the model.

Cache deterministic calls

If the same input always produces an acceptable output, cache it. This sounds obvious but many teams skip it.

Effective caching strategies:

• Semantic caching: Cache responses based on embedding similarity, not exact string match. “What is Laravel?” and “Explain Laravel” should hit the same cache entry.
• Response caching: For stable outputs (product descriptions, FAQ answers), cache the full response with a TTL.
• Result caching: For processing tasks (extraction, classification), cache results keyed by input hash.

Optimize prompts and context

Every token costs money. Long, verbose prompts are expensive. Optimized prompts are cheaper without sacrificing quality.

• Trim context: Only include relevant chunks in RAG. Ten well-chosen chunks beat fifty partially relevant ones.
• Compress system prompts: Remove redundant instructions. Test that compressed prompts still produce acceptable outputs.
• Set max tokens: Always set a reasonable max_tokens limit. Open-ended generation can be expensive.

Measure quality alongside cost

The danger of cost optimization is silent quality degradation. Every cost-cutting change should be validated against quality metrics.

Our approach:

1. Build an evaluation set of representative inputs and expected outputs.
2. Run the evaluation set before and after each optimization.
3. Only ship changes where cost decreases and quality stays within acceptable bounds.

This evaluation-first approach means we never discover cost cuts broke quality from user complaints.

Need help controlling LLM costs?

We design cost-efficient AI architectures for production systems. If your LLM bill is growing faster than your revenue, we can help.

Book a free strategy call with our engineering team.