The promise of AI is transformative, but the costs can be sobering. A production application processing millions of requests through a frontier model can easily cost tens of thousands of dollars per month. The good news is that with smart engineering, you can often reduce LLM costs by 80-95% without meaningful quality degradation. This guide covers the most effective strategies for optimizing LLM spending.

Understanding LLM Pricing

Before optimizing, you need to understand what drives costs. Most API-based LLMs charge per token, with separate rates for input (prompt) tokens and output (completion) tokens. Output tokens are typically 2-4x more expensive than input tokens because they require more computation.

Your total cost is: (input_tokens x input_price) + (output_tokens x output_price)

This means you have two primary levers: reduce the number of tokens processed and use cheaper models where possible. Let us explore specific strategies for each.

Model Selection and Routing

The single most impactful cost optimization is using the right model for each task. Not every request needs a frontier model. A smart routing system can classify incoming requests by complexity and route them to the most cost-effective model that can handle them.

Tiered Model Architecture

Design your system with multiple model tiers:

  • Tier 1 (cheapest): Small models or simple classifiers for straightforward tasks like intent detection, sentiment analysis, and keyword extraction.
  • Tier 2 (moderate): Mid-range models like GPT-4o-mini or Claude Haiku for standard conversational tasks, summarization, and routine code generation.
  • Tier 3 (premium): Frontier models for complex reasoning, creative tasks, and situations where quality is paramount.

In practice, 70-80% of requests can often be handled by Tier 1 or 2 models, dramatically reducing average cost per request.

"The most expensive token is the one you didn't need to generate. The second most expensive is the one generated by an unnecessarily powerful model."

Key Takeaway

Smart model routing is the highest-impact cost optimization. Route simple tasks to cheap models and reserve expensive models for complex requests. This alone can reduce costs by 60-80%.

Prompt Optimization

Prompt length directly affects cost. Every token in your system prompt, context, and conversation history costs money on every request. Here are strategies to minimize prompt tokens:

  • Compress system prompts: Review and trim your system prompt to remove redundant instructions. A well-crafted 200-token system prompt can be as effective as a 2000-token one.
  • Manage conversation history: Instead of sending the entire conversation history, summarize older exchanges or use a sliding window approach.
  • Optimize RAG context: Retrieve fewer, more relevant documents rather than stuffing the context with marginally related information.
  • Use concise output instructions: If you need a short answer, say so. "Respond in one sentence" prevents the model from generating unnecessary elaboration.

Caching Strategies

Caching can eliminate redundant LLM calls entirely, providing the biggest possible savings for cacheable requests.

Exact Match Caching

Store the results of previous LLM calls and return cached results for identical inputs. This works well for classification tasks, static content generation, and repeated queries. Even a modest cache hit rate of 20-30% translates directly to a 20-30% cost reduction.

Semantic Caching

Use embedding similarity to match semantically equivalent queries, even if they use different wording. "What is the capital of France?" and "Tell me France's capital city" should hit the same cache entry. Semantic caching requires a vector store but can dramatically improve cache hit rates.

Prompt Caching (Provider-Level)

Some providers offer built-in prompt caching that reduces the cost of repeated prompt prefixes. Anthropic's prompt caching, for example, allows you to cache your system prompt and retrieve it at a fraction of the normal input token cost. If your system prompt is long and used on every request, this feature alone can cut costs significantly.

Batching and Async Processing

Not every request needs a real-time response. For tasks that can tolerate some latency, batch processing offers significant savings:

  • Batch APIs: OpenAI and Anthropic offer batch APIs with 50% discounts compared to real-time pricing.
  • Off-peak processing: If using self-hosted models, schedule batch jobs during low-usage periods to maximize GPU utilization.
  • Aggregated requests: Combine multiple small requests into a single larger request to reduce overhead and improve throughput.

Self-Hosting and Fine-Tuning

For high-volume applications, self-hosting open-source models can reduce per-request costs by an order of magnitude. The economics are straightforward: GPU infrastructure has a high fixed cost but very low marginal cost per token.

Fine-tuned small models offer another path to cost reduction. A 7B parameter model fine-tuned on your specific task can often match the performance of a general-purpose 70B model, at a fraction of the serving cost. The investment in fine-tuning pays for itself quickly at scale.

Key Takeaway

LLM cost optimization is not a single technique but a combination of strategies: model routing, prompt optimization, caching, batching, and self-hosting. The most effective approach applies all of these in layers, targeting the biggest cost drivers first.

Measuring and Monitoring

You cannot optimize what you do not measure. Implement comprehensive cost monitoring that tracks spending by model, endpoint, user, and task type. Set up alerts for cost anomalies, and regularly review your cost breakdown to identify new optimization opportunities. The LLM cost landscape changes rapidly as providers adjust pricing, so what is optimal today may need adjustment tomorrow.