The Problem: Categories That Never Fit
I struggled to track my expenses for months. Every budget app I tried made me pick a category, set a budget, and log each purchase — and it always broke down the same way: I’d spend the same amount on the same type of item, but at a different store, and suddenly my neat categories didn’t fit anymore.
A simple example: I used to buy milk and curd from my local kirana store, so I made a dedicated category for it. Recently I switched to a higher-quality (and higher-priced) dairy for the same items. Now my old category is technically wrong, and I have to decide whether to edit it, split it, or just let it drift into inaccuracy.
The second problem was worse: sometimes I genuinely didn’t know what category a purchase belonged in, so I’d dump it into “Others” or “Miscellaneous” — which, after a few months, becomes a graveyard of untraceable spending.
Why an LLM Seemed Like the Fix
When I started using an LLM instead of an app, the categorization problem mostly disappeared. I just speak the expense using voice mode — “spent 200 on curd today” — and tell it to remember it. No dropdown menus, no rebuilding categories, no “Miscellaneous” bucket.
The bigger upside showed up at the summary stage. When I ask for a monthly report, the LLM doesn’t just spit out a static chart like an app would — it generates the file in whatever format I ask for (Excel, plain text, whatever), ranks spends from highest to lowest, highlights unusual spikes, and gives budgeting advice in plain language. Apps give you a chart. This gives you a conversation about your money.
Where Budget Apps Actually Fall Short
| Pain point | Typical budget app | LLM-based tracking |
|---|---|---|
| Manual entry | Required every time, via forms | Voice or text, natural language |
| Category drift | Manual editing when spending habits shift | Auto-categorized, flexible |
| Export/reporting | Often paywalled (PDF/Excel export) | Free-form, any format on request |
| Report structure | Fixed to app’s built-in template | Customized to what you ask for |
| Multi-device sync | Requires re-login; data loss if local-only | Tied to your account/chat history |
| Ongoing cost | Often a paid subscription for cloud sync | Free until you exceed usage limits |
This is the case for using an LLM. But it comes with real, invisible costs that only show up once you understand how these models actually process information.
The Hidden Token Tax
Say your daily entry is “add ₹200 for curd.” If that’s part of an ongoing chat with memory, the model doesn’t just process those five words — it re-reads your entire conversation history first, because that’s how it “remembers” earlier entries. Day 1’s log costs a handful of tokens. By day 30, you’re re-processing an entire month of accumulated history just to append one line — token cost compounds roughly quadratically over the month, not linearly.
A batch app that appends a row to a database pays zero re-processing cost for old data. The LLM’s natural-language convenience quietly trades away that efficiency.
Energy Cost Nobody Talks About
Every voice-logged entry triggers a full inference cycle — compute spin-up, attention calculation, generation — for a payload that’s often just a handful of words. Research on LLM inference shows energy draw is dominated by fixed per-request overhead, and that overhead is amortized (spread across more useful work, hence more efficient) when requests are batched, not when fired off dozens of times a day in tiny pieces. Logging 30 separate times a day is measurably less energy-efficient per token than logging once with a full day’s expenses pasted in a single message.
The “Lost in the Middle” Risk
This one matters most for the feature I liked best — the monthly summary. LLMs are demonstrably weaker at attending to information buried in the middle of a long context compared to the start or end. When I ask for a full monthly summary drawn from 100+ scattered memory entries, there’s a real chance the model glosses over transactions from the middle of the month while nailing the first and last few days. The “highlights high spends” feature might be silently skipping data I assume it read.
Why It’s Still (Mostly) Free — For Now
Two provider-side mechanics quietly save money here, whether you realize it or not:
- Prompt caching: if your instruction phrasing (“log this expense, categorize it, remember it”) stays nearly identical each time, providers like Anthropic and OpenAI cache that repeated prefix — you’re not paying full price to re-send the same instructions on every voice note.
- Unused batch discounts: providers offer up to ~50% off for asynchronous batch processing, which is the exact opposite of how most people log expenses in real time. Logging one entry at a time never qualifies for that discount tier — you’re paying the premium rate for something that doesn’t need to happen instantly.
The Fix I Actually Use
Once I understood the token-replay and lost-in-the-middle problems, I stopped logging directly into the LLM chat. Here’s my actual workflow:
- Throughout the day, I log raw expenses as quick notes in Google Keep — just the amount, item, and store, no categorization, no formatting.
- Once a week, I copy all those raw entries and paste them as one message into my existing Claude chat.
- I say something like: “Here are this week’s expenses — categorize each one, add them to memory, and flag anything unusually high.”
- Claude parses the batch, assigns categories, and appends everything to memory in one pass — instead of seven separate inference calls re-reading history each time.
- Once a month backup, I backup the monthly budget excel to the google drive using Claude AI connectors.
This keeps the parts of LLM tracking I actually wanted — auto-categorization, natural language input, flexible reporting — while cutting out the daily token replay and the many-tiny-inference-calls energy cost. It also sidesteps the lost-in-the-middle risk at summary time, since I’m feeding the model manageable weekly chunks instead of an entire month of scattered entries at once.
What I’d Try Next
I haven’t yet measured the actual token cost difference between my old daily-chat approach and this weekly-batch method — that’s the obvious next step, since right now I’m going on the general research, not my own numbers. I’d also like to test whether a cheaper, smaller model is “good enough” for the categorization step, saving the more capable model calls for the monthly summary and budgeting advice, where reasoning quality actually matters.
The bigger lesson generalizes past personal finance: real-time interaction with an LLM feels better, but batching with caching is almost always cheaper, more energy-efficient, and — counterintuitively — often more accurate too.
So do you use an LLM for budgeting, how efficient and time saving is it for you? Feel free add your experience in the comment.
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