LLM Cost Control: How Engineering Teams Keep AI Bills Predictable

From the archive. Originally published 2025-01-17. Tool details, examples and offers reflect that publication date. See our current guides for newer material.

The cost problem with LLM-powered features

LLM costs have a counterintuitive property: they scale with usage complexity, not just usage volume. A feature that handles 1,000 simple queries cheaply can cost 10× more per query when users start asking complex, multi-step questions - even at the same request volume.

Engineering teams that don't build cost awareness into their AI stack from the start routinely get surprised by bills that balloon as features get adopted.

Five cost control patterns that work

1. Model routing by task complexity

Not every query needs your most powerful (expensive) model. Use a lightweight classifier or rule-based router to send simple queries to Claude Haiku or GPT-4o-mini, and escalate to Sonnet or Opus only when complexity warrants it. Teams using this pattern typically cut LLM costs by 40-60% with no quality degradation on simple tasks.

2. Prompt caching

If your prompts contain large static context (system prompts, document chunks, tool definitions), cache them using Anthropic's prompt caching feature. Cached tokens cost 90% less. For applications with consistent context, this is often the single highest-leverage cost optimization available.

3. Output length control

LLM costs are proportional to output tokens. Explicitly constrain output length in your system prompt, and use structured output (JSON with defined schemas) to prevent models from padding responses. This alone can cut output token counts by 30-50% on many tasks.

4. Semantic caching

For applications where users ask similar questions repeatedly (support, search, FAQ), semantic caching (storing responses and retrieving by embedding similarity) can serve a significant fraction of requests from cache. GPTCache and similar tools make this straightforward to implement.

5. Observability before optimisation

You can't optimise what you can't measure. Instrument every LLM call with token counts, model used, and task type from day one. LangSmith, Langfuse, or a simple custom logger all work. The teams managing costs best are the ones who can see their cost per task type in a dashboard.

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