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Both OpenAI and Anthropic offer batch API modes that process requests asynchronously (within 24 hours) at roughly half the standard price. If your workload isn't latency-sensitive (e.g., nightly data enrichment, bulk classification), batch mode is a straightforward 50% cost reduction.

Anthropic and Google now support prompt caching, where large repeated system prompts or document contexts are cached server-side. Subsequent calls with the same cached prefix cost as little as 10% of normal input price. For RAG pipelines reusing the same document chunks, this is transformative.

Route simple queries to cheap models (GPT-5.4 Mini, Gemini Flash, Haiku) and complex ones to premium models (GPT-5.4, Claude Opus). Using a lightweight classifier or a rule-based router, you can serve 80%+ of queries with cheap models and reserve expensive ones for the hard 20%.

Formatting tokens add up fast. A code block with triple backticks and a language specifier adds ~5 tokens. Markdown headers, bullet points, and bold formatting each add 1-3 tokens. For high-volume APIs, stripping unnecessary formatting in prompts can reduce token counts by 5-15%.

For repeated API calls, define an abbreviation guide in your system prompt (e.g., 'usr=user, msg=message, resp=response') then use abbreviations throughout. This can reduce prompt token counts by 15-25% on verbose instruction sets.

Tools like LangSmith, Braintrust, and Helicone provide trace-level observability for LLM applications. They let you inspect every prompt, response, latency, and cost in production, making debugging and optimization far easier than raw logging.

Break down complex tasks into smaller chunks to optimize token usage and improve response quality.

Monitor your token usage patterns to identify opportunities for prompt optimization.

Use model routing: send simple tasks to cheap/fast models and complex ones to premium models.

Different LLMs use different tokenizers. The same piece of text can result in a varying number of tokens depending on the model, which impacts cost and context window usage. Tools like TokenCalculator help you see these differences.

Don't expect the perfect response on your first try. Prompt engineering is an iterative process. Refine your prompts based on the LLM's output to improve results. Small changes can make a big difference.

Set up monitoring for your production LLM applications to track token usage patterns. This helps identify optimization opportunities and avoid unexpected costs when usage scales up.

Set up monitoring for your production LLM applications to track token usage patterns. This helps identify optimization opportunities and avoid unexpected costs when usage scales up.

Don't always use the largest, most expensive model. Smaller, faster models can be sufficient and more cost-effective for simpler tasks. Evaluate tradeoffs between capability, speed, and cost.

In a production environment, A/B test different prompt variations to empirically determine which ones yield the best results for your key metrics.

Provide a working piece of code to an LLM and ask for suggestions on how to optimize it for performance or readability.

Provide slow database queries to an LLM and ask for optimization suggestions, including index recommendations and query restructuring.

Ask LLMs to generate benchmarking code to measure the performance of different algorithms or implementations.

If initial constraints are too restrictive and yield poor results, gradually relax them: 'If the previous constraints are too limiting, suggest the closest possible alternative.'

Challenge: Create the most effective prompt for a complex task using the fewest possible tokens. Test different approaches and measure both output quality and token efficiency.