AI coding blows up your credit card
once your project gets serious.
I found the fix.

I’d been using AI tools seriously for about eight months when the invoice arrived that made me seriously consider stopping.

I was building a commercial SaaS product — real work, not a tutorial. Multi-tenant architecture, billing system, role-based access, the whole thing. I was using Claude Opus because nothing else came close in terms of code quality. And for the first few weeks, it was extraordinary. Features that would have taken me days were done in hours. I was shipping faster than I ever had.

Then the API bill landed. $780 in a single month. For one developer. On one project.

I dug into the usage logs. The problem was obvious once I saw it. Every time I asked the AI to implement something, I was feeding it my entire codebase as context. The project had grown to nearly 80,000 lines of code. I was passing 60–80K tokens of context just so the model could find the 200 lines it actually needed to touch. I was paying for a 400-page document when I needed one paragraph.

It was getting worse, not better. The bigger the project grew, the more context I needed, the more it cost, and the worse the model performed. At 80K tokens, even Opus was starting to lose the thread.

I tried every workaround. Manual file selection. Smaller queries. Splitting the codebase into modules. None of it felt right. I was spending more time managing the AI than I would have spent just writing the code.

Then the idea hit me at 2am (as these things do).

What if instead of asking the AI to figure out what to do, I first asked a smart model to plan the work — but only show it the relevant files? Then take each atomic step and give a cheaper model everything it needs to execute: the exact file, the exact function to modify, the exact replacement. No discovery. No hunting through the codebase. Just precise execution.

I built a prototype over a weekend. The results were almost funny. A task that had been costing me $2–3 in Opus tokens was now costing $0.04 for the planning call and nothing for the implementation — because I was running Llama 3 locally on my own machine. Free. The code quality was indistinguishable. The local model doesn’t need to be brilliant when it’s given a precise recipe.

My monthly bill went from $780 to under $40. On the same project. Shipping faster.

I mentioned it to a few developers I knew. Within a week I had six people asking if they could use it. One was spending $1,200 a month. Another had stopped using AI altogether because his enterprise codebase was too large for any model to handle. A third was a freelancer who simply couldn’t afford frontier model rates on a tight margin.

I spent the next three months turning the prototype into something production-ready. Proper workspace management. Visual autopilot pipeline. Build gate on every step. Git commit after each success. Retry logic when something fails. Sub-task decomposition for complex steps. And full support for local LLMs — because free is the best price.

That’s EasyAgents. Built because I couldn’t find anything else that solved this problem properly. And now other people are using it to build commercial software without worrying whether the API bill is going to swallow their margin.

Here’s what I discovered after burning through thousands of dollars in API costs: the complexity that kills your budget isn’t the codebase — it’s the context you force the model to carry.

When you say “add a billing system to my app” and dump your entire codebase into a frontier model’s context window, three things happen:

1. You pay for a context window that’s 80% irrelevant noise.
2. The model’s attention degrades — quality tanks.
3. Large codebases exceed the window entirely. The model refuses or hallucinates.

The fix: stop giving the AI a mountain. Give it a molehill.

EasyAgents’s Task Auto-Planner takes your feature request, analyses only the files that matter, and decomposes the work into the smallest possible atomic steps — each with laser-focused context containing only the code it actually needs. Then it routes each step to the most cost-efficient model capable of handling it: