If your OpenAI bill (or any AI API bill) grows faster than your usage, you are almost certainly overpaying in specific, fixable places. These are the seven levers that most often move the needle, ranked from the biggest to the smallest typical saving. Most of them do not touch the quality of the answers; they simply stop wasting tokens.
The 7 levers at a glance
| Lever | Typical saving | Effort |
|---|---|---|
| Pick the model per task | 50–90% | Medium |
| Prompt caching | 50–90% (on cached tokens) | Low |
| Batch processing | ~50% | Low |
| Trim the context | 20–60% | Medium |
| Cap output tokens | 10–40% | Low |
| RAG instead of long context | Variable, high at volume | High |
| Monitor spend | Enables the rest | Low |
1. Pick the model per task
By far the biggest lever. Using the most powerful model for everything is the most expensive mistake. Most tasks —classifying, extracting, summarizing— are solved by a small model at a fraction of the price. Reserve the large model for what genuinely needs it and route the rest to the small one.
2. Turn on prompt caching
If your requests repeat the same block of context —instructions, examples, a base document—, caching charges for it once instead of on every request. On workloads with a lot of repeated context, the saving on those tokens reaches 90%. It is one of the cheapest things to implement.
3. Batch what can wait
If a task does not need an immediate answer —overnight reports, enriching a database, mass classification—, the batch API usually costs half. It is trading minutes for money on everything that is not interactive.
4. Trim the context you drag along
Every request pays for all the context you feed it, whether the model uses it or not. Send only what is relevant: instead of pasting the whole manual, pass the sections that are needed. Less context is less cost per task, on each and every request.
5. Cap the output tokens
Output costs several times more than input. Set a maximum number of tokens and ask for concise answers when you do not need a long text. A sensible max_tokens and a prompt that asks for brevity trim the expensive part of the bill.
6. Move to RAG when the context explodes
When you drag huge documents into every request, a retrieval system (RAG) that sends only the relevant fragments lowers the context per request and, at volume, the saving is large. It takes more setup, but at scale it pays for itself. We develop this in RAG, fine-tuning or long context.
7. Monitor your spend
Not a direct saving, but it enables all the others: without measuring cost per task, per user and per endpoint, you do not know where you are overpaying. What you do not measure, you cannot optimize.
Put numbers on your levers
Before touching anything, measure where your spend comes from. Put your tokens and your volume into the token cost calculator and try switching model or trimming the context: you will see the monthly saving of each lever before implementing it.
Frequently asked questions
How can I reduce the cost of the OpenAI API? The three biggest levers are picking a smaller model for each task, turning on prompt caching for the context that repeats, and batching what does not need an immediate answer. All three apply in hours and usually do not touch quality.
How much does prompt caching save? On the context tokens that repeat between requests, the saving reaches 50–90%. The larger and more stable the block of context you reuse, the more it pays off.
Is the batch API cheaper? Yes, it usually costs about half in exchange for not being immediate. It pays off on everything that is not interactive: reports, data enrichment, mass classification.
This piece is part of the cluster around cost per task of AI, the guide that anchors everything else.
At e-ficient we measure the cost per task of companies that already run AI in production and tell you how much you would save with each lever at your real volume. The first audit is free if you start a plan with a three-month commitment and returns a diagnosis within 72 hours.