AI API Cost Optimization implementation checklist

Deploy a proxy like LiteLLM or OpenRouter to programmatically switch between providers without refactoring code.

Verify if tasks like classification, summarization, or extraction can be handled by GPT-4o mini, Gemini Flash, or Claude Haiku instead of flagship models.

Implement a lightweight classifier to route high-complexity queries to flagship models and low-complexity queries to cheaper alternatives.

Set up logic to attempt requests on a cheaper model first, falling back to a more expensive model only if the output fails validation or safety checks.

Configure Ollama or vLLM for local development and CI/CD testing to eliminate API costs during the build phase.

Implement a Redis-based cache to store and return identical prompt-response pairs for 0 tokens.

Use a vector database to identify and serve cached responses for prompts that are semantically similar but not identical.

Ensure cache keys include user identifiers to prevent data leakage between different user sessions while maintaining cost savings.

Set time-to-live values on cached LLM responses to ensure data freshness and prevent stale content delivery.

Integrate tools like Helicone or Portkey to track the financial impact of your caching layer in real-time.

Set explicit output token limits on every API call to prevent runaway generation and predictable cost ceilings.

Audit system messages to remove redundant instructions, filler text, and overly verbose formatting requirements.

Retrieve only the most relevant few-shot examples for the current context rather than sending a static, large block of examples.

Define specific stop sequences to terminate generation as soon as the relevant data is produced, avoiding trailing filler tokens.

Implement a sliding window or summarization strategy for conversation history to keep context windows small and manageable.

Attach user_id, feature_id, and environment tags to every API call for granular cost attribution.

Configure provider-level and proxy-level alerts at 50%, 80%, and 100% of the monthly budget.

Code automated triggers that disable high-cost features if a specific user or feature exceeds a daily spend threshold.

Establish a metric for cost-per-successful-interaction to identify features that are economically unviable.

Monitor the efficiency of your tokenizer; identify if specific languages or formats are inflating token counts unexpectedly.

Use OpenAI or Anthropic Batch APIs for non-real-time tasks to receive a 50% discount on token pricing.

Use Pydantic or Zod to validate inputs before sending to the LLM, preventing costs on malformed requests.

Force JSON mode or tool calling to reduce the need for retries caused by parsing errors.

Ensure embeddings for static knowledge bases are calculated once during ingestion, never during the query runtime.

Test if a fine-tuned smaller model can match the performance of a large model with a long complex prompt.