Yes. The method is provider-agnostic. If you are on AWS, the assessment can often be funded through AWS partner programs. Ask us on the call.
We review your prompts, caching and model choice, then run your real workload across the options and show you which setup holds your quality bar for less. In two weeks, not a quarter.
Every feature defaults to the biggest model, because that is what worked in the demo.
There is no eval set, so “it feels worse” is the only test you have.
Prompts grew by copy-paste, context is resent on every call, and the invoice shows it.
Every claim we make comes out of this. Click through what you’ll actually be looking at in week two: your data, your workload, your numbers.
Most teams touch one and stop. The savings compound when you do them in order and measure each step.
Trim, restructure, and move static context out of the hot path.
Prompt caching, response caching, retrieval reuse. Usually the fastest win.
Route each task to the smallest model that passes your quality bar.
Build a golden dataset from your real inputs and outputs, so every change is measured, not felt.
We capture a sample of your real inputs and outputs, or use your existing golden data, and read your current prompts and pipeline.
We stand up an evaluator in your own cloud account and run your workload across model and prompt variants, side by side.
You get a ranked list of changes with cost and quality impact, plus the rewritten prompts and caching config to ship them.
Each line has a measured cost delta, a measured quality delta, and an effort estimate. You decide what ships.
Sample output. Your numbers come from your own workload.
Once the evaluator is set up, it doesn’t stop. When a new model ships, it runs your golden dataset against it and tells you if a cheaper model now matches your quality bar.
A newly released model matches your quality score on 1,200 golden cases at a materially lower cost. Review the diff.
Tested a cheaper alternative. Quality dropped 2.1 pts on classification cases. Not recommended.
AWS Advanced Tier Services Partner with the AI Services Competency.
50+ engineers who ship production AI systems on Bedrock and beyond.
Assessments are how most of our engagements start. We know how to make two weeks count.
If you’re on AWS, this is usually funded through AWS’s partner programs. Ask on the call.
Tell us more about yourself and what you're got in mind.
Prefer email? hello@apexlab.io
Or even a quick call?
We don’t care about fancy CVs or long interview processes. Provide some basic information, let’s have a quick chat, and then show us what you love doing the most: write some code and create a product.
