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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.

Using the `response_format: {type: 'json_schema'}` parameter (OpenAI) or XML tags with output parsers (Claude) ensures the model always returns valid structured data. This eliminates regex hacks, reduces prompt length, and makes your pipelines far more reliable.

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%.

Your system prompt is sent with every API request and billed as input tokens every time. A 2,000-token system prompt on 100,000 daily calls equals 200M tokens/day just in system prompts. Keep system prompts concise, or use prompt caching to amortize the cost.

Setting temperature=0 reduces randomness but doesn't guarantee identical outputs across API calls - models can still vary due to sampling hardware differences. For true reproducibility, use temperature=0 AND set a fixed seed parameter (available in OpenAI and some other APIs).

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.

Always set max_tokens in your API calls to prevent runaway generation. A missing token limit on a recursive agent loop can generate tens of thousands of tokens in seconds, leading to unexpected bills.

When retrying failed LLM API calls, use exponential backoff with random jitter rather than fixed delays. This prevents thundering herd problems when multiple clients retry simultaneously after a provider outage.

Before committing to expensive API-based models, benchmark open-source alternatives like Llama 4, Qwen3, or Mistral on your specific task. For many classification, extraction, and summarization workloads, fine-tuned open models match or beat frontier APIs at a fraction of the cost.

Enable streaming (stream: true) for any generation over 500 tokens. Without streaming, the user waits for the entire response to complete before seeing anything. With streaming, the first tokens appear in under a second, dramatically improving perceived performance.

The cost of LLM API calls has fallen dramatically since 2023. GPT-3.5-Turbo launched at $2/M input tokens; by 2026, comparable-quality models like Gemini Flash and GPT-4o-mini cost under $0.15/M input tokens — a reduction of over 90% in under three years.

Consider using shorter models for simple tasks to save on API costs while maintaining quality.

Test models with smaller context windows first to save on API costs during development.

Batch API calls when possible — most providers offer significant discounts for batch processing.

System prompts are often re-sent with every API call. Keep them concise to save tokens. Use abbreviations or keywords if the model is fine-tuned or understands them.

System prompts are often re-sent with every API call. Keep them concise to save tokens. Use abbreviations or keywords if the model is fine-tuned or understands them.

Many LLM APIs allow you to specify which fields you want in the response. Only request the data you need (e.g., just the generated text, not full logprobs if unused) to reduce data transfer and processing.

Do not send personally identifiable information (PII) or other sensitive data to public LLM APIs unless you understand their data usage policies and have appropriate safeguards.

When making API calls to LLMs, implement exponential backoff for retries. This helps manage rate limits and temporary server issues gracefully without overwhelming the API.

Implement caching strategies for repeated queries to reduce API calls and costs.

If you have many small, independent tasks for an LLM, batch them into a single API call if the model and API support it, rather than many separate calls. This can reduce overhead.

Be aware of API rate limits (requests per minute/day). Design your application to handle these gracefully, perhaps with retries and exponential backoff.

Never embed API keys directly in client-side code or public repositories. Use environment variables or secure secret management services.

LLMs are excellent at generating boilerplate code for common patterns (e.g., setting up a new class, a basic API endpoint, HTML structure).

Beyond exact-match caching, consider semantic caching. If a new prompt is semantically similar to a previously cached one, you might be able to reuse the old response, potentially saving an API call.

For applications like chatbots, use streaming API responses if available. This allows text to be displayed token by token as it's generated, improving perceived responsiveness.

When working with APIs, ask LLMs to generate example code for common integration patterns, including authentication, error handling, and data parsing.

Generate user manuals, API documentation, and technical guides. Ensure clarity for your target audience's technical level.

Different LLMs tokenize text differently. The same sentence can result in varying token counts across models, directly affecting API costs and context window usage.