10 Tips to Reduce Your LLM API Costs
Why LLM Costs Add Up Fast
A single API call is cheap. But at scale, LLM costs can dominate your infrastructure budget. A chatbot handling 50,000 conversations per day at $0.01 per conversation is $500/day or $15,000/month. Multiply that across multiple features, and costs become a serious concern.
The good news is that most teams can reduce their LLM spending by 50-90% without sacrificing user experience. Here are ten battle-tested strategies.
1. Start with the Cheapest Model
The most common mistake is defaulting to the most powerful model. GPT-4o-mini costs roughly 16x less than GPT-4o and handles most tasks well. Test the cheapest model first and only upgrade if quality is measurably insufficient.
For many classification, extraction, and summarization tasks, a smaller model is not just adequate — it is preferred for speed and cost.
Use the tokencalc Model Comparison to see price and capability differences side-by-side.
2. Shorten Your System Prompts
System prompts are sent with every single API call. A 2,000-token system prompt across 100,000 daily requests is 200 million input tokens per day. At GPT-4o rates, that is $500/day just for the system prompt.
Audit your system prompts ruthlessly. Remove redundant instructions, compress examples, and eliminate “just in case” guidelines that the model already follows by default. A well-written 500-token system prompt often performs better than a verbose 2,000-token one.
3. Use Prompt Caching
OpenAI offers automatic prompt caching that reduces input costs by 50% for repeated prefixes. If your system prompt and initial context are the same across many requests, the cached portion is charged at half price.
Structure your prompts so the static portion (system prompt, fixed context) comes first, followed by the dynamic portion (user message, variable data). This maximizes cache hit rates.
4. Limit Output Tokens
Set the max_tokens parameter to prevent unexpectedly long responses. If you expect a 200-word answer, set max_tokens to 300. Without this limit, the model might generate a 1,000-token response that you truncate anyway, wasting output tokens.
This also prevents runaway costs from edge cases where the model generates extremely long responses.
5. Implement Response Caching
Many applications ask the same questions repeatedly. “What are your business hours?” “How do I reset my password?” Cache responses for identical or semantically similar queries.
Exact Match Caching
Store a hash of the full prompt and return the cached response for identical prompts. This works well for deterministic queries with temperature set to 0.
Semantic Caching
Use embeddings to find similar past queries. If a new query is semantically close enough to a cached query (above a cosine similarity threshold), return the cached response. This catches paraphrased versions of the same question.
6. Use a Routing Layer
Not every query needs the same model. Build a router that classifies incoming requests and sends them to the appropriate model:
- Simple factual questions go to GPT-4o-mini
- Complex reasoning tasks go to o3-mini
- Creative or nuanced tasks go to GPT-4o
A lightweight classifier (even a rule-based one) can reduce costs significantly by sending 80% of requests to cheaper models while maintaining quality where it matters.
7. Truncate and Summarize Context
As conversations grow longer, the context window fills with old messages that may no longer be relevant. Instead of sending the entire conversation history with every request:
- Summarize old messages — Periodically compress older parts of the conversation into a shorter summary.
- Use a sliding window — Keep only the most recent N messages and a summary of everything before them.
- Filter irrelevant context — Remove messages that are not relevant to the current question.
Reducing context from 10,000 tokens to 2,000 tokens per request cuts input costs by 80%.
8. Batch Requests Where Possible
If you need to process many items (product descriptions, support tickets, data entries), batch them into a single prompt rather than making individual API calls. Processing 10 items in one call is cheaper than 10 separate calls because:
- The system prompt is sent once instead of ten times
- Per-request overhead is eliminated
- Many models offer batch API discounts (50% off at OpenAI)
9. Fine-Tune for Repeated Tasks
If you are spending heavily on a specific task that always follows the same pattern (classification, extraction, formatting), consider fine-tuning a smaller model. A fine-tuned GPT-4o-mini can match or exceed GPT-4o quality on narrow tasks at a fraction of the cost.
Fine-tuning requires an upfront investment in training data and compute, but the ongoing cost savings are substantial for high-volume use cases.
10. Monitor and Measure Everything
You cannot optimize what you do not measure. Track token usage per feature, per model, and per user segment. Identify your most expensive API calls and optimize them first.
The tokencalc API Cost Estimator helps you project costs based on your usage patterns and compare scenarios. Use the Token Counter to measure exactly how many tokens your prompts consume before deploying them.
Quick Wins Summary
| Strategy | Potential Savings |
|---|---|
| Use cheaper model | 80-95% |
| Shorten system prompt | 20-50% |
| Prompt caching | 25-50% on input |
| Response caching | 50-90% on repeated queries |
| Limit max_tokens | 10-30% on output |
| Model routing | 40-70% overall |
| Context truncation | 50-80% on input |
| Batch requests | 30-50% |
Start with the highest-impact changes: model selection, prompt caching, and response caching. These three alone can cut costs by 70% or more. Then measure and iterate.
Calculate your current and projected costs with the tokencalc Pricing Calculator to see exactly where your money goes and how much you can save.