AI is not for Indie Dev

HTMLCoder — Sun, 19 Jul 2026 19:31:42 +0000

How LLMs Got Expensive Before They Reached Common Developers

I started building with LLMs thinking:

“This is it. AI is finally accessible to everyone.”

A few weeks later, I was checking my API usage like it was my bank balance.

And that’s when it hit me 👇

LLMs didn’t become expensive later.
They were already expensive — we just didn’t notice early.

The First Impression: “This is cheap!”

When you look at APIs from OpenAI or Anthropic, it feels affordable:

“Pay per token”
“Free credits to start”
“Just a few cents per request”

As a dev, you think:

“I can totally build something with this.”

And you can…
until real users show up.

Reality: Costs don’t grow linearly

My mistake was simple:

I thought:

1 user → 1 request → small cost

But in real apps:

1 user action = multiple LLM calls
Each call = more tokens than expected
Each feature = more hidden usage

Suddenly:

1 request becomes 5–10 calls
Each call sends history + context

Now multiply that by users.

That “cheap API” becomes your biggest expense.

Context is powerful… and expensive

This one hurt the most.

To make LLMs actually useful, you:

Send chat history
Add system prompts
Include documents (RAG)

Which means:

More tokens in every request
You pay for input + output

So the better your app becomes…

the more it costs to run it.

“I’ll just use open-source models”

I thought the same.

Models like LLaMA or Mistral sound like a solution.

But then you realize:

You need GPUs
You need infra
You need time to optimize

So instead of API bills, you get:

Cloud bills
DevOps problems
Latency issues

It’s not free.
It’s just a different type of expensive.

The hidden stack nobody mentions

Even if LLM APIs were free, your app still needs:

Vector DBs like Pinecone
Backend servers
Storage
Monitoring

LLMs don’t replace your stack.

They sit on top of it and increase cost.

Why big companies don’t complain

This part is important.

Big companies:

Get better pricing deals
Optimize token usage deeply
Have infra teams

Indie devs?

We:

Pay standard pricing
Overuse tokens
Learn by burning money
⚠️ The real problem

It’s not that LLMs are bad.

It’s that:

Cost optimization came after capability.

We got:

Powerful models
Easy APIs

But we didn’t get:

Clear cost patterns
Good defaults
Efficient architectures

What I’m doing differently now

After burning some credits, I changed how I build:

I cache responses aggressively
I reduce token size wherever possible
I avoid unnecessary LLM calls
I use smaller models when I can

Big mindset shift:

LLM is not your logic layer.
It’s your most expensive dependency.

What happens next?

I think this will happen:

Costs will go down over time
Bad AI products will disappear
Efficient builders will win

Until then:

Building with LLMs is less about prompts
And more about cost control

Final thought

LLMs feel magical when you’re building alone.

They feel expensive when users arrive.

And that gap?

That’s where most AI side projects die.

If you’re building something with LLMs right now:

Track your cost early. Not later.

Because that’s the part nobody talks about… until it’s too late.

Why does Large company still use Legacy Apps

HTMLCoder — Mon, 13 Jul 2026 16:15:28 +0000

Our Project has an application based on which the whole process is dependent on. If it breaks then nothing will work and we have to close shops. The Application is a broker app which forwards csv files from one sftp to another and validated input file.

The whole code is based on ESQL, deploying it takes a whole hour and adding feature or debugging is a nightmare. No developer touches it willingly and most of our time is spent on just understanding and referring back to the architecture.

I though why are the company is not upgrading the project to something more advanced and up-to date. The discussion was brought upto business and they simply denied because of Money. The whole picture of running a project well and putting in some time requires lots of money, and until unless something is breaking, nobody is willing to put in any money into upgrading it. which then clicks me that everything in tech is driven by cost of fixing something. if its not causing us to lose money, then there is no need to fix it.

which also makes me realise how much efforts people put in personal projects.

DEV Community: HTMLCoder

AI is not for Indie Dev

Why does Large company still use Legacy Apps