Скачать или смотреть AI Applications Cost Optimization

The cost of a deployed AI Application can quickly grow beyond initial projections. Numerous cost factors and implementation techniques get overlooked that can save a boatload of money. Would love to hear what your biggest learnings were throughout your process.

I'll share some of mine, these came from years of building and consulting.

1. The BIGGEST cost savings that everyone overlooks when it comes to building scalable AI applications and it's simple. Understanding what is a problem solved with software engineering vs being handled by a non deterministic agent. Need actions done based on a condition? Instead of building one VERBOSE prompt to handle all scenarios, use a couple prompts and SMART ROUTING.

One model (a cheap one like Gemini) to understand and categorize the question. A conditional statements based on the category returned to route to the necessary model (think claude sonnet or if you're Mr./Mrs. money bags, Claude Opus or ChatGPT-5.

2. This next one is something excited builders or requirement generating folks/roles always struggle with, you know a few of these folks. Not every problem needs to be solved in real time. There are processes like generating the embeddings for your Vector DB, never in your life should these be done real time unless you truly hate prosperity. These are batch embedding jobs and should always be that way. This comes from, making sure to always understand what your is a process. Before a single API call is made, understand and ACTIVELY search for which parts can be a batch process. This will save you about 50% on the token cost depending on the vendor used.

3. This should be done at all times after you've first generated your prompt(s), prompt compression. It may feel like all the context is needed to get the job done right but if done right, you will quickly see how much this can save on applications that have high traffic.

4. Now this next one can take your application from losing more money than you care to spend to being sustainable. Let's say you have an application, it is built with a prompt that can't be compressed further due to necessary verbose criteria mentioned within it. After reading through you notice there are parts that are in fact dynamic but most, is static. This is when you should refactor and employ, prompt caching. If you haven't read into it, you should. Anthropic offers up to 90% savings on tokens while Open AI offers 50%.

There are more means to wrangle your unrealistic builds. Want to speak further? We are here to help more AI applications thrive in the wild and balance ROI! Leave a comment or reach out on any of our socials!