$2,000 a month. That's what a typical multi-project AI API bill looks like once you're running production workloads across OpenAI, Anthropic, and AWS Bedrock. And if you haven't audited your usage recently, there's a good chance you're overspending by half.
The good news: a handful of straightforward optimization techniques can dramatically reduce what you're paying, often without any noticeable drop in output quality.
Model Routing: The Biggest Win
Not every API call needs your most expensive model. Model routing means automatically sending simple requests to cheaper, faster models (like GPT-4o mini or Claude Haiku) and reserving the heavy hitters for tasks that actually need them. Practitioners report cost reductions of around 55% from routing alone, with no degradation in final output quality.
The key is building a classifier that evaluates request complexity before choosing which model handles it. A straightforward rule-based system works fine to start - you don't need an ML model to route your ML models.
Prompt Compression
Most API pricing is based on tokens (the chunks of text the model processes, where roughly 750 words equals 1,000 tokens). Longer prompts cost more. Prompt compression strips unnecessary context, redundant instructions, and verbose formatting from your prompts before they hit the API.
On high-volume endpoints, this alone can save 70% on your most-called routes. Tools like LLMLingua and Microsoft's prompt compression research offer practical starting points, though even manual prompt trimming pays off quickly.
Caching and Deduplication
Two techniques that compound well together:
- Semantic caching stores responses to previous queries and serves cached results when a new query is similar enough. This isn't exact-match caching - it uses embedding similarity to catch paraphrased versions of the same question. Teams see 20-30% cost reduction from this alone.
- Request deduplication catches duplicate calls from retries, race conditions, or redundant logic paths. It sounds minor, but eliminating wasted retry calls typically removes about 15% of total API spend.
The Compounding Effect
These techniques stack. Model routing saves 55%, prompt compression trims another 70% off your highest-volume endpoint, deduplication removes 15% of wasted calls, and caching handles another 20-30% of redundant queries. The exact savings depend on your workload, but going from $2,000 to under $800 a month is realistic without touching your application logic.
The practical starting point: run a one-week audit of your API calls. Sort by cost, not volume. You'll almost certainly find a handful of endpoints responsible for most of your spend, and those are where routing and compression deliver the fastest ROI.