Cheap AI Coding Agents: Subscription vs API Costs in India
A freelancer in Pune opens her card statement and finds three AI charges: one dollar subscription, one API top-up, and a GPU she bought in March. All three were sold to her as the cheap option. She cannot say which one actually is.
That is the problem with the phrase "cheap AI coding agents." It points at three different kinds of bill, and they do not compare cleanly. A flat subscription charges you the same whether you code for two hours or twelve. An API key charges you for every token that moves in either direction. A local model charges you once for hardware, then quietly in electricity and lost capability.
This article explains how each bill works, then runs rupee math on hypothetical workloads. These are labelled sketches, not measured tests. I won't hand you a cheapest ranking, because the right answer depends on how many hours you code and how often the agent runs. Provider prices change often, so check every figure below against the provider's current page before you spend anything.
Open with the bill, not the tool: why 'cheap' means three different things
Most comparison posts start with the tool. Start with the bill instead, because the bill is what hits your account.
There are three shapes of bill. First, the flat subscription: a fixed monthly price in dollars or rupees, with some allowance of usage attached. Second, the pay-per-token API: you bring your own key, a provider meters every million tokens in and out, and your monthly cost is whatever your usage produces. Third, local hardware: you pay upfront for a machine that can run an open model, then pay for power and accept a weaker model.
None of these is cheap in the abstract. A flat plan is cheap if you use it heavily and wasteful if you open the editor twice a week. An API key is cheap if your agent runs are short and rare, and expensive if one run reads half your repository. Local hardware is cheap only if you already own the machine.
The mistake I see most often is comparing a subscription's sticker price to an API's per-token price. Those numbers measure different things. One is a price per month. The other is a price per unit of work, and you haven't counted the units yet.
So the working rule for the rest of this piece: convert everything into rupees per month at a stated date and a stated workload. If a comparison skips either, ignore it.
Subscriptions: flat monthly price, usage allowances, and the dollar-to-rupee gap
A subscription trades variance for predictability. You know the number before the month starts. That matters when you invoice clients in rupees and cannot absorb a surprise.
The catch for Indian developers is the currency. GitHub Copilot and Cursor both publish their prices in US dollars. A listed dollar price is not what you pay in rupees: your bank applies its own conversion rate and may add card or foreign transaction charges. The rupee amount on your statement can sit above the arithmetic you did from the headline price. Check the pricing pages for GitHub Copilot and Cursor for the current plan names, prices and usage terms. Both vendors have revised their tiers and included allowances more than once.
A useful habit: when you read a plan, find the line that says what happens at the limit. Some plans slow down, some pause premium model access, some bill overage. That line, not the headline price, decides whether the plan is cheap for you.
Here is the dollar-to-rupee gap as plain arithmetic. At a hypothetical rate of ₹85 per dollar (check the current RBI or FBIL reference rate for the real figure on the day you read this), a $20 monthly plan is ₹1,700 before any card charges. At ₹90 per dollar it is ₹1,800. The plan didn't change. The rupee moved, and so did your bill.
The argument for a rupee-priced subscription is simple. It removes one variable from the estimate. You see ₹899 on the page and ₹899 on the invoice line you plan around.
API keys: per-million-token pricing and a hypothetical workload worked out in rupees
With an API key, the provider bills per million tokens, with separate prices for input tokens (what you send) and output tokens (what the model writes back). Prices differ by model, and the large models cost several times more than the small ones. Read the current rates on the provider's own pricing page, such as Anthropic's or OpenAI's.
Now the worked example. This is a hypothetical workload, not a measurement. Treat every number as an assumption you should replace with your own.
Assume a solo developer runs an agent 20 times a day, 22 working days a month, so 440 runs. Assume each run sends 40,000 input tokens (the agent reads files and history) and receives 4,000 output tokens. Monthly input is 440 x 40,000 = 17.6 million tokens. Monthly output is 440 x 4,000 = 1.76 million tokens.
Now pick placeholder prices, clearly labelled as invented for the arithmetic: $3 per million input tokens and $15 per million output tokens. Input cost is 17.6 x 3 = $52.80. Output cost is 1.76 x 15 = $26.40. Total is $79.20.
Convert at a hypothetical ₹85 per dollar and that is about ₹6,732 for the month. Swap in the real RBI or FBIL reference rate for the date you are reading, and swap in the real per-token prices for the model you would use.
Change one assumption and the answer moves a lot. Halve the input tokens per run, and the bill nearly halves. Double the runs, and it doubles. The API is cheap when you control the context size, and unpredictable when an agent decides how much of your repo to read. That variance is the real cost of this bill.
Hardware and local models: upfront cost, electricity, and what you give up
Running a model locally feels like escaping the meter. It isn't. The bill just moves.
The upfront cost is the machine. A laptop or desktop with enough GPU memory to run a capable open-weight coding model is a purchase measured in tens of thousands of rupees at the low end and far more for serious GPUs. If you already own the hardware for gaming or video work, the marginal cost is close to zero and local becomes genuinely attractive. If you would buy it only for this, you are spreading a large sum across however many months you keep it.
Hypothetical sketch, again labelled as an assumption: a ₹90,000 machine kept for 36 months is ₹2,500 a month in depreciation alone, before any electricity. A desktop drawing 300 watts under load for 4 hours a day uses about 36 kWh a month. At a hypothetical ₹8 per unit that is roughly ₹288. Check your own tariff, because Indian electricity prices vary a lot by state and consumer category.
Then count what you give up. Local open models are generally weaker at multi-file reasoning than the large hosted ones, and they run slower on consumer hardware. If a task takes your agent three tries locally and one hosted, the hours you lose are a cost too, and for a freelancer, hours are billable.
Local makes sense in two cases: you already own the machine, or your code cannot leave it. Outside those cases, don't buy hardware to save money on AI. The arithmetic rarely works.
Where OxCode fits: Free 15 runs/day, Starter and Pro at recurring prices, plus the introductory offer
OxCode is an AI software engineer that lives in your editor. It plans, writes, tests and debugs code, runs what it writes in a sandbox, and fixes its own bugs before calling a task done. It reads your codebase first and routes each prompt to the model it judges most efficient, so you don't choose one yourself.
The billing is a flat subscription in rupees, measured in runs per day. One agent run is one request, and allowances reset daily. The plans:
Free: ₹0, 15 runs per day.
Starter: ₹899 per month, 40 runs per day.
Pro: ₹3,999 per month, 150 runs per day.
Keep two prices separate in your head. ₹899 and ₹3,999 are the recurring monthly prices. ₹99 for Starter and ₹999 for Pro are the introductory prices for the first two months only. If you budget from the introductory number, month three will surprise you. Budget from the recurring one.
Compare this against the earlier sketch honestly. A fixed ₹899 doesn't vary with token counts, and the run allowance caps how far a month can drift. Whether that beats an API bill depends on your run count and context sizes, which only you know. I'm not claiming it is the cheapest option for every workload, and the earlier numbers don't support such a claim.
For performance context rather than price, OxCode reports 82% on SWE-bench Verified and 82% on Terminal-Bench 2.1, with run records on the benchmarks page. For a deeper rupee guide, see AI coding assistant pricing in rupees.
How to pick: a checklist for estimating your own monthly cost before you pay
Do this before you enter any card number. It takes twenty minutes and beats any comparison table.
Count your runs. For one normal week, tally how many times you would ask an agent to do a task. Multiply by working days. That is your monthly run count.
Find your heavy days. Averages hide deadlines. If one crunch week triples your usage, price that week, not the mean.
Estimate context per run. Small scripts read little. A large monorepo makes an agent read a lot. This number drives an API bill more than anything else.
Fix the exchange rate. Look up the RBI or FBIL reference rate for today, write the date next to it, and use it in every dollar conversion. Add a margin for card charges if you pay in dollars.
Read the limit behaviour. For every plan, find what happens when you hit the allowance: throttle, pause, or overage. A cheap plan with a surprise overage is not cheap.
Price the free tier first. A free allowance may cover a light month entirely. The free AI coding agent for VS Code walkthrough shows a first task on the Free plan.
Price your own hours. If a weaker or slower tool costs you two extra hours a month, put your hourly rate on that and add it to the bill.
Only after these seven steps should you compare totals. Then pick the lowest total for your workload, not the lowest sticker.
Conclusion
Stop asking which tool is cheapest and start asking which bill fits your month. Run the seven-step checklist, write your own run count and exchange rate on paper, and let the total decide.
If your estimate lands near a few dozen runs a day, test the arithmetic against a real task for free. Install OxCode in VS Code, Cursor or Antigravity, use the Free plan's 15 runs per day on a real bug in your own project, and see how many runs it actually takes the agent to plan, write, test and fix. Every other estimate in this article depends on that count. Then compare it to ₹899 recurring, not ₹99, before you decide.