AI API usage scales fast. What starts as a few hundred calls a day can become millions of tokens an hour the moment your product finds traction. The good news is you have a lot more levers than “use a cheaper model.” Below are seven practical strategies we see consistently reduce spend while keeping quality high. Each tip maps to a real engineering control you can implement today.
1) Use prompt caching strategically
Prompt caching is the easiest win when your requests have a large shared prefix. Think system prompts, tool schemas, or long retrieved context that stays mostly the same across calls. Cache the serialized prompt prefix and reuse it, so you only send and pay for the dynamic part. The idea is simple: if 70% of your prompt is identical, you should only be paying for it once.
Implement this by splitting your request into a stable prefix and a small suffix, then caching the tokenized prefix. When you can’t cache tokens directly, you can still cache the string and rebuild the prompt quickly. Either way, the savings are real, especially for long RAG contexts.
2) Batch requests wherever latency allows
Batching turns dozens of small requests into a single larger request. This reduces network overhead and can dramatically improve throughput. If your use case tolerates a few seconds of latency (e.g., nightly reports, batch processing, or background classification), this is a high-impact optimization.
A simple queue + batcher is enough: collect incoming requests for a short window, merge them into a single prompt, and split the response. You’ll also simplify rate limiting because the number of API calls drops.
3) Pick the right model tier for each task
Most teams overuse flagship models. A better pattern is a tiered model strategy:
- Use a lightweight model for fast triage, extraction, or formatting.
- Escalate to a larger model only when confidence is low or complexity is high.
This “router” approach can cut costs without harming accuracy. For example, a low-cost model can handle 70–80% of requests, while a premium model handles the tricky edge cases.
4) Optimize tokens with tight prompts and output caps
Tokens are your direct cost driver. Every extra sentence in the prompt and every unneeded paragraph in the output is money. Get your prompt down to the minimum that still performs well, and always set a reasonable max_output_tokens ceiling.
Practical token tactics include:
- Use clear, concise instructions instead of long explanations.
- Prefer structured output formats (short JSON fields) over verbose prose.
- Strip retrieved context to only the most relevant chunks.
If your output is deterministic (like a label or a summary length), enforce it. “Keep responses under 120 words” is not enough. Set the actual max output tokens.
5) Monitor costs at the feature and endpoint level
Global API spend is a lagging indicator. You need feature-level visibility to find and fix cost spikes fast. Instrument each endpoint with tags like feature=onboarding, endpoint=summary, or model=gpt-5-mini. This lets you trace costs to specific experiences.
Once you have this visibility, set thresholds. If a single endpoint starts consuming 2x its baseline budget, you should know the same day and roll back or adjust. Cost regressions are just like performance regressions, so treat them the same way.
6) Rate limit and shed non-critical load
Rate limits are not just for availability. They are also a cost safety net. When usage spikes, it is better to defer or drop non-critical requests than to burn budget. Define tiers such as:
- Critical: user-facing actions that must succeed
- Important: background tasks that can be delayed
- Optional: analytics or enrichment that can be skipped
When you hit a cap, shed load in that order. You protect UX and avoid surprise bills. This is especially important for teams that allow user-generated prompts, where a few power users can explode costs.
7) Fine-tune for smaller, cheaper inference
Fine-tuning is not always the answer, but in high-volume workloads it can be a cost superpower. A fine-tuned smaller model can match or beat a large general model for a narrow task. That means lower per-token prices and often fewer tokens needed overall.
If you fine-tune, focus on narrow tasks with consistent structure: categorization, extraction, or constrained generation. Validate quality with a clean evaluation set before switching production traffic.
Putting it all together
Cost optimization is less about a single “best model” and more about a system of controls. You can mix and match the tactics above to match your product’s needs. A typical stack looks like this:
- Start with a tiered model strategy.
- Add caching and batching for high-volume endpoints.
- Cap tokens and compress prompts.
- Monitor at the feature level and enforce rate limits.
Each of these levers has compounding effects. A 30% reduction from caching plus a 25% reduction from better model selection can quickly turn into a 50% lower bill. More importantly, you avoid the common trap of fighting costs only after they balloon.
If you want to see the impact instantly, use the cost calculator on the homepage. Enter your average input/output tokens and request volume, then try switching models. You’ll see how quickly the monthly spend changes with even small adjustments.