Imagine paying a fortune for an AI Service that can only do what you already know how to do but in much more sub-standard or generic way. That’s the reality with most of today’s ‘AI’ promises. Today we’re diving into the real cost of AI, and why the hype is far from reality.
my 10+ experience as a tech entrepreneur has taught me 1 very important lesson that don’t make critical business decisions based on Hype or short term trends like AI.
I’m not saying entire AI is Hype but there is element of hype & false optimism in AI along with Real positive benefits. but separating Hype from reality is very important. that’s why I’m making this video.
If you are Tech business owner then this video is more valuable for you especially while making critical business decision related to AI & Automations.
so what is the real cost of using AI in your production pipeline ? weather that be automation or running LLMs or generative AI the fundamental reality remains same that AI requires very high computation power which means high cost of operations.
Business Basics
here is my simple business formula which i use in business:
less input + high output = Profit
anything else is just loss and not worth it. when you implement AI in business it is important to keep less input of energy & high output of value to keep your business profitable. now keep this formula in mind as it will be helpful in understanding rest of this video.
another important point is “think like a goldfish in the sea—don’t behave like a shark chasing every hype wave”.
framework of decision making is much more different for small-medium size business owners like you & me, as compare to big tech companies who are going all-in for AI hype train.
AI Deployment
Let’s say you want to deploy AI models for your business operations. you have only 2 options: on-premise deployment (also known as self hosting) and Cloud Deployment.
both of these options comes with it’s own advantages & disadvantages which affect the cost of operation.
For On-Premise Deployment
you will have to Own & manage infrastructure, hardware, software and everything of your server or machine.
For AI you will need Good GPUs or TPUs & large size of RAM. Faster GPU means Quick AI Response, faster processing and less time to complete a task. so your cost of operations get 10 times more as compare to standard website hosting. and don’t forget that GPU are energy hungry so cost of electricity might go up.
as a small or medium size business, scaling might be problem as you have to add more machines for more bigger automation pipelines or Tasks. but for larger enterprises it can be viable option depending on your use case.
the biggest advantage of using on-premise AI Deployment is you have flexibility, full control & ownership of AI.
the disadvantage is there is high cost of hardware & you have to maintain it all by yourself.
For Cloud Deployment
This is the most easy method to build AI Automation pipelines because you don’t have to own any physical hardware or anything. just simple signup for account & start writing code & use API.
example is Gemini, ChatGPT, Claude and for advance use case you can use Google’s AI Studio or GCP Vertex.
the advantage of using cloud is it is easy for any beginner to build AI automation or pipelines for production. the cost of cloud based AI is very affordable as compare to on-premise deployment. in fact, most of base plans are free like Google AI studio allows 1 million free tokens per month to all users.
the biggest disadvantage of Cloud AI is lack of ownership & Privacy. when you use Cloud based AI Models, you don’t have much control over it and we all know privacy is big issue in tech industry today.
always remember that when you don’t pay for product means you are the product. Cloud based AI can be cheap & low cost but the trade-offs are hidden.
Cloud based AI are like a Rental model where you pay for service but doesn’t own anything and doesn’t have ownership. if i as a business owner dosen’t have ownership of AI models then it’s a red flag for me.
my simple advice will be don’t build critical AI Pipelines or operation in cloud, instead choose on-premise AI deployment.
Conclusion
both on-premise & cloud based AI models have it’s own advantage & disadvantage. going back to our basic business formula.
on-premise AI deployment has high cost of input & cloud based AI models have low cost of input.
when it comes to output of AI models, the content generated by LLMs & Generative models is always going to be average quality because that’s how Generative AI Works, copying patterns from massive datasets & generating output.
it is wiser to pay high cost for talent & skills because humans are flexibility**,** creativity**,** and critical thinking that AI can’t replicate, where as AI models are good for generating mass content but lack creativity & critical thinking.
so the question is, Is it worth paying for the “cheap” generative output, or invest in skilled people ?
AI isn’t a silver bullet; it’s a tool that can either boost your quality or drain it if you’re not careful. and this is when you as a business owner has to make critical decision from your own perspective rather than what hype or market tells.
~ ~ THANK YOU FOR READING ~ ~
