The problem
AI bills usually grow faster than usage. Prototypes reach production with duplicated calls, whole documents pasted into prompts and no ceiling on spend, and nobody knows what a single feature really costs.
Service
Reduce your LLM API costs and keep every
LLM cost optimisation means finding where your tokens go and cutting the waste: redundant model calls, oversized context, missing caches, no spend limits. On a consumer finance platform, rewriting the AI pipelines this way cut AI costs by about 80%. Every feature stayed, including the 13-slide AI financial reports.
Key numbers
~80%lower AI costs on a consumer finance platform, with the same features
What I do
1. Measure: log the cost of every AI call and every feature
2. Remove: redundant calls, context you don't need, repeated generations
3. Cache: reuse answers that don't need to be generated again
4. Cap: spend caps, rate limits, and generation only where it adds value (e.g. above a score threshold)
5. Then review the model choice, only where it still matters
Stack
OpenAI, Anthropic Claude, Gemini, Azure OpenAI; Python/FastAPI pipelines; background queues; AWS, Azure, GCP.
Results
- Consumer finance platform: AI costs down ~80%, same features
- Estyl: lower latency and token spend per request while usage grew
What it means for your company
If you can't say what one report, one chat or one image costs you, there is almost certainly a large saving to find. It usually comes from fixing the pipeline, more than from switching to a cheaper model.
Frequently asked
How much can LLM costs really come down?
It depends on how the pipelines were built. On a consumer finance platform, removing redundant calls, trimming context and adding caching, spend caps and rate limits cut AI costs by about 80% with the same features. Mature pipelines may have less to gain, and the audit tells you how much.
What pushes LLM API costs up the most?
Usually the architecture: the same model call made more than once, whole documents or histories sent when a fragment would do, content regenerated when it could be cached, and automated jobs with no caps. These patterns matter more than the model's price.
Will cutting costs hurt quality?
It shouldn't. Quality checks stay in place: on the finance platform, reports still verify source domains and check the model's output. What goes is the waste, and an evaluation set confirms quality before and after.
What is a spend cap for LLMs?
A hard limit, written in code, on how much a feature or job can spend in a given period. For example, daily and per-category limits on AI image generation. Together with rate limits and cost logging for each output, it stops bugs or traffic spikes from running up the bill.
Related
Next step
Want to know where your tokens go? We can look at your real traffic.
Tell me the workflow. We scope it on a free call, then a working version is live within 10 days.
Book a scoping call