Marketing for AI trading bots usually quotes one number: the monthly subscription. That number is often the smallest cost of running the bot, and traders who budget for the subscription alone are surprised by the real monthly bill. The full cost stack includes exchange trading fees on every entry and exit, bid-ask spreads, funding rates on perpetual positions, slippage between signal and fill, market data or API costs, VPS hosting to keep the bot online, and the subscription itself. Each item is small alone; together they decide whether a strategy with a modest edge survives or slowly bleeds to zero.

Exchange fees are the most predictable layer. Many retail venues charge a taker fee around 0.04 percent per trade for market orders, with maker fees lower for limit orders that add liquidity. A round trip at taker rates therefore costs about 0.08 percent of position size, before spreads or slippage. On a 10,000-dollar position that is 8 dollars per round trip; at fifty round trips a month it is 400 dollars, regardless of whether the trades win or lose. High-frequency strategies multiply this brutally: a strategy that trades hundreds of times a month needs a far larger edge just to pay its own fees, which is why backtests that omit fees are meaningless for active bots.

Spreads, funding, and slippage are the quieter layers. The spread is paid on every round trip and widens in volatile or illiquid conditions, exactly when bots trade most. Funding on perpetual futures is charged on full position size at set intervals, and a crowded long position can pay a meaningful annualised rate for weeks. Slippage is the gap between the signal price and the fill price, driven by latency, order size relative to book depth, and market speed; a few basis points per trade sounds trivial until it is multiplied across a year of trading. Add VPS hosting, typically a modest monthly fee, and data feeds or premium API tiers where applicable, and the fixed monthly overhead can reach well into three figures before a single trade is placed.

Work out the break-even before subscribing. Add the subscription, VPS, and data costs as fixed monthly overhead, then estimate variable costs from your expected trade count, average position size, fee tier, and a realistic slippage assumption of a few basis points per trade. Divide total monthly costs by expected monthly trades to get the cost per trade, and ask what average profit per trade the strategy must earn just to break even. If the vendor's advertised returns do not survive that arithmetic, the strategy is not viable at your size, whatever its backtest claims. Smaller accounts suffer most here, because fixed costs are a larger percentage of a small balance.

Reduce costs where you can without fooling yourself. Use limit orders where the strategy allows, negotiate or earn a lower fee tier through volume where genuinely available, avoid overtrading by filtering for higher-quality signals, and run one bot well rather than five bots poorly. Review the real cost statement monthly, not the vendor dashboard, because exchanges and brokers report what you actually paid. A bot can be a useful tool, but it is a business with costs, and traders who treat it as one, with a profit-and-loss statement that includes every fee, make better decisions than traders who only remember the subscription price.

A last cost is easy to miss: taxes and record-keeping. Every bot trade can be a taxable event depending on your jurisdiction, and a bot that trades hundreds of times a month creates a reporting burden that a buy-and-hold investor never faces. Keep complete trade records from day one, export them monthly, and check with a tax professional before scaling up a high-frequency bot. A strategy that is profitable before tax and painful after it is not the strategy its dashboard suggested.