NovaRunning AI Coding Agents on a $10/Month API Budget
# Running AI Coding Agents on a $10/Month API Budget
Most writing about AI coding agents is about capability. This is about the bill, because the bill is what decides whether you keep using one.
The good news: a focused individual can run serious agent workflows for less than the price of a streaming subscription. The bad news: the way most people use agents makes that impossible, and it is not because the models are expensive.
Where the money actually goes
Inference costs have two components — input tokens and output tokens — and one of them dominates in a way people do not expect.
On deepseek-flash (V4.1), the rates here are $0.55 per million input tokens and $1.65 per million output tokens. Output is 3x input. So the instinct is "output is the expensive part, keep answers short."
That instinct is wrong for agents, and here is why: an agent resends its entire context on every turn.
The quadratic trap
Say your agent has a 4,000-token system prompt and has been working for 20 turns, with roughly 800 new tokens per turn.
- Turn 1 sends ~4,800 tokens of input.
- Turn 20 sends ~4,000 + (20 × 800) = ~20,000 tokens of input.
You are not paying for 20 turns of 800 tokens. You are paying for the sum of a growing number: roughly 4,800 + 5,600 + … + 20,000. That is about 248,000 input tokens for what felt like 16,000 tokens of actual conversation.
Cost scales roughly quadratically with conversation length when you resend everything. A 40-turn session does not cost twice a 20-turn session. It costs closer to four times.
Four changes that cut the bill without touching quality
1. Stop re-sending what did not change. Prompt caching exists on several providers and typically charges around 10% of the input rate for the cached prefix. Where it is not available, restructure so the stable part of your context is not re-sent, or summarise the history rather than replaying it verbatim.
2. Route by difficulty, not by habit. Classification, extraction, formatting and file edits almost never need your best model. Reserve the expensive model for genuine reasoning and send the rest to the cheap one. This single change is usually bigger than any provider switch.
3. Cap the agent, not the output. A per-run token budget turns a runaway loop into a failed run with a clear error. Without a cap, a retry bug becomes a billing incident — and the failure is silent until the invoice.
4. Measure with p95, not the average. Averages hide the long tail, and the long tail is where agents spend money. One retry storm in a week can outweigh a month of normal use.
A realistic budget
Take a developer using an agent for a few hours a day: say 3 million input tokens and 600,000 output tokens a month, which is a normal shape for that usage (input dominates heavily).
| Item | Cost |
|---|---|
| 3.0M input @ $0.55/M | $1.65 |
| 0.6M output @ $1.65/M | $0.99 |
| Token total | $2.64 |
| Top-up: $10 via USDT TRC20 | +$1.00 fee |
| What you actually pay this month | $10.00, with $7.36 left over |
That is the thing worth internalising: at this usage level the payment fee is larger than the inference cost. A flat ~$1 TRC20 transfer on a $10 top-up is 10% — which means topping up $50 once beats $10 five times, for no other reason than the flat fee.
Where the same usage gets expensive
- Resending full history every turn — the quadratic trap above, worth multiples.
- Using the frontier model for everything — 3x the rate for work that did not need it.
- No per-run cap — one loop can eat a month's budget in an afternoon.
- Frequent small top-ups — flat fees quietly become a meaningful percentage.
The honest part
We sell API access, so discount this accordingly — but the numbers above are the real rates we charge, and the arithmetic does not depend on who you buy from.
What we would genuinely suggest regardless of provider: put a hard token budget on every agent run, keep the base URL in config so switching is a one-line change, and measure your own p95 before optimising anything. The people who run agents cheaply are not using cheaper models. They are not resending what did not change.
Rates shown are NovaAPI's, which include a reseller margin over direct access. We are not the cheapest option if you can pay the model vendor directly — we exist for people who cannot. Single Hong Kong node, best-effort uptime, keep a fallback for production.