The engineers who manage AI API costs well are not the ones who use AI less. They are the ones who use it more precisely. Every technique on this list reduces cost without reducing capability — because capability is what you are paying for, and there is almost always a way to get the same capability for less.
1. Route requests by complexity
Not every task requires a frontier model. A request that asks a model to extract a date from a sentence, classify a support ticket into one of five categories, or check whether a user input contains a phone number does not need GPT-4o or Claude Opus. It needs a correct answer at minimal cost.
Build a routing layer that sends simple, well-defined tasks to smaller models and reserves large models for tasks that genuinely require their capabilities — complex reasoning, nuanced generation, multi-step problem solving. The cost difference between a large and small model for the same task type can be 10 to 20 times. A routing layer that correctly classifies 40 percent of your requests as simple tasks can reduce total API costs by 30 to 50 percent with no change in output quality for those tasks.
2. Cache deterministic outputs
If you are calling the AI API with the same input repeatedly, you are paying for the same computation repeatedly. This is common in features that use a fixed prompt with variable user content, where the same user content appears multiple times — a search query, a product description, a support ticket category.
Cache the output keyed on a hash of the input. Set a reasonable TTL based on how frequently the underlying content changes. For many use cases, 24 to 72 hours is appropriate. Cache hit rates of 20 to 40 percent are achievable in most production applications with moderate user overlap.
3. Compress your system prompts
System prompts accumulate. A prompt that started as 200 tokens grows to 800 tokens as developers add edge case handling, examples, and clarifications. Every 600 tokens of unnecessary system prompt adds to the cost of every single API call for that feature.
Audit your largest system prompts quarterly. Remove instructions that apply to rare edge cases — handle those with application logic instead. Remove examples that can be covered by a single more general instruction. Remove redundant phrasing. A well-compressed system prompt that achieves the same behavior as a verbose one saves money on every request, permanently.
4. Truncate conversation history deliberately
Conversational features that pass full history to the model accumulate input token costs with every turn. Set a maximum context budget — a specific token count — and when history exceeds it, summarize the oldest messages rather than passing them raw. Summarization can reduce history token count by 80 percent with minimal loss of conversational coherence for most use cases.
5. Use structured outputs to reduce output tokens
Unstructured text generation is token-expensive because models often generate explanation, reasoning, and framing around the answer even when you only need the answer. Structured output modes (JSON mode, function calling) constrain the model to emit only what you asked for, eliminating the conversational overhead that inflates output token counts.
For classification, extraction, and any task where the output has a defined schema, structured outputs typically reduce output token count by 40 to 60 percent compared to free-text generation of the same information.
6. Set max_tokens explicitly on every call
If you do not set a max_tokens limit, the model will generate until it decides it is done. For some use cases, that is appropriate. For many production use cases, there is a practical upper bound on how long a useful response can be, and anything beyond that bound is waste.
Audit your API calls for max_tokens settings. For any call without one, estimate what a reasonable upper bound would be and set it. This is a 30-minute engineering task that permanently caps runaway output costs on any feature where the model occasionally decides to be verbose.
7. Batch requests where latency allows
Most major AI providers offer batch APIs with meaningful discounts — typically 25 to 50 percent off standard pricing — for requests that can tolerate processing delays of up to 24 hours. Many enterprise use cases qualify: nightly data enrichment, content classification runs, offline document processing, eval pipelines.
Identify which of your AI workloads are batch-compatible and migrate them. The latency tradeoff is often invisible to end users for background workflows, and the cost savings are immediate.
8. Monitor and kill zombie API calls
Zombie calls are API calls that are still running in production but are not attached to any active use case — prototypes that were never shut down, features that were deprecated but whose API keys were not revoked, automated jobs whose initiating project has long since concluded.
A monthly audit of active API keys against your registered use case inventory will surface zombies. Killing them requires no engineering work beyond revoking the key or disabling the job. The savings are pure.
9. Use evals to catch over-engineering
The most expensive AI systems are often over-engineered — they use a large model for a task a smaller model can handle because nobody has run the comparison. Build a lightweight eval harness that periodically tests your production prompts against cheaper model alternatives. If a smaller model achieves the same score on your quality metrics, switch it. This is not a one-time optimization. Model capabilities change fast, and a task that required a large model last year may be well within range of a small model today.
Cost optimization is not a project. It is a practice. The organizations that reduce AI API costs most effectively build these techniques into their development workflow — not as a special initiative when a bill surprises them, but as the default way they build AI features.