How I Track My AI Spending as a Solo Dev (Without Going Broke)
I ship solo. No team, no finance department, no one reviewing expenses but me. When I started using LLMs heavily in my workflow — Claude for code review, GPT for drafts, a bit of Gemini here and there — I told myself I'd keep a close eye on costs. I had a vague sense of what I was spending. Turns out "a vague sense" doesn't cut it when you're getting invoiced. So I built a system. Or rather, I cobbled one together after getting burned. The Moment That Changed How I Think About This I was three weeks into a heavy coding sprint. I had Claude open basically all day — asking it to review diffs, explain errors, help me write tests. Normal stuff. Then my monthly statement hit. Not catastrophic, but more than I'd mentally budgeted. The frustrating part wasn't the money. It was that I had zero vis
I ship solo. No team, no finance department, no one reviewing expenses but me.
When I started using LLMs heavily in my workflow — Claude for code review, GPT for drafts, a bit of Gemini here and there — I told myself I'd keep a close eye on costs. I had a vague sense of what I was spending. Turns out "a vague sense" doesn't cut it when you're getting invoiced.
So I built a system. Or rather, I cobbled one together after getting burned.
The Moment That Changed How I Think About This
I was three weeks into a heavy coding sprint. I had Claude open basically all day — asking it to review diffs, explain errors, help me write tests. Normal stuff.
Then my monthly statement hit.
Not catastrophic, but more than I'd mentally budgeted. The frustrating part wasn't the money. It was that I had zero visibility while it was happening. I couldn't have told you whether I'd spent $5 or $50 that day. There was no real-time signal. Just vibes, then an invoice.
That's when I started treating AI spending like CPU usage — something you need to monitor while it's happening, not after.
What I Do Now
I run TokenBar in my menu bar. It's a little macOS app I actually built myself after getting frustrated with the lack of real-time token visibility. It shows me live token counts and cost estimates as I work.
Here's what that actually looks like in practice:
In the morning, I glance at the menu bar before I start. See my running daily total from yesterday. Gives me a baseline.
While working, if I'm about to paste a huge chunk of context into Claude, I can see what that's going to cost before I hit send. Usually the answer is "not much, carry on." But occasionally I'll realize I'm about to dump 80k tokens when 20k would do.
End of day, I have a number. Real, not estimated. I know exactly what that coding session cost me.
The Mental Model Shift
The thing that helped most wasn't any particular tool — it was changing how I thought about it.
Old mindset: tokens are an abstraction, worry about the bill at end of month.
New mindset: tokens are like compute. I'd never let a process eat 100% CPU for hours without noticing. Why would I let token consumption run unchecked?
Once I started thinking that way, I naturally started being more intentional. Not stingy — I still use AI constantly. But deliberate. I'll batch questions instead of firing off ten single-sentence prompts. I'll summarize long threads before feeding them back in. Small habits, real savings.
What I Wish Existed Earlier
Honest answer: I wish there had been something sitting in my menu bar from day one showing me a number.
Not a dashboard you have to log into. Not a weekly email digest. Just a persistent, ambient readout. The kind of thing that makes spending visible without requiring active effort to check.
That's why I built TokenBar the way I did — it's meant to be glanceable, not something you manage. You install it, connect your APIs, and it just sits there keeping score.
For Other Solo Devs
If you're running lean and using multiple AI APIs, please don't do what I did and assume your mental accounting is accurate. It isn't. Not because you're careless — because the feedback loop is broken by design. You're billed monthly for something you're consuming continuously.
Get some kind of real-time signal in place. Whether that's TokenBar or something else you've built yourself, just make the spending visible. It'll change how you work.
The goal isn't to spend less. It's to spend knowingly.
DEV Community
https://dev.to/godnick/how-i-track-my-ai-spending-as-a-solo-dev-without-going-broke-5ec1Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
claudegeminimodel
The AI Gaslight
Why Tech Billionaires Are Selling a Utopia to Build an Empire From “vibe coding” tech debt to digital sweatshops — how the AI industry is sacrificing the working class to summon a machine we cannot control. A few weeks ago, I made a very public, very painful admission about building my startup, Nexa. Caught up in the deafening hype of the AI bubble, I stopped writing deep architectural code and started relying entirely on Large Language Models (LLMs) to “vibe code” my MVP. The AI acted like a sycophant. It flattered me. It told me my ideas were brilliant. It made me feel like a 10x engineer. But when real users touched the product, the system choked. Beneath the beautiful UI was a terrifying ocean of unscalable spaghetti code and suppressed errors. I realized the hard way that AI doesn’t m
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models

AI That Improves AI: What Happens When Agents Start Rewriting Themselves?
From Darwin Gödel Machine to HyperAgents-understanding how AI is evolving from static models to systems that continuously improve themselves What happens when an AI system is no longer just solving problems but also begins to change itself in order to solve them better? Not retrained. Not fine-tuned.But actively rewriting it’s own code, it’s own workflow and eventually improving the way it improves itself ! At first, it sounds like science fiction. However, the idea of a machine that can modify itself has been discussed for decades, earlier framed as a theoretical construct-something powerful yet out of reach . One of the earliest formulations imagined a system that could rewrite its own code but only after proving that the modification would lead to better performance. Agreed, it was a be

Bounded Autonomy: Controlling LLM Characters in Live Multiplayer Games
arXiv:2604.04703v1 Announce Type: new Abstract: Large language models (LLMs) are bringing richer dialogue and social behavior into games, but they also expose a control problem that existing game interfaces do not directly address: how should LLM characters participate in live multiplayer interaction while remaining executable in the shared game world, socially coherent with other active characters, and steerable by players when needed? We frame this problem as bounded autonomy, a control architecture for live multiplayer games that organizes LLM character control around three interfaces: agent-agent interaction, agent-world action execution, and player-agent steering. We instantiate bounded autonomy with probabilistic reply-chain decay, an embedding-based action grounding pipeline with fa




Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!