Teams running AI agents in production commonly underestimate their first-year costs by 2-4x. Here are the four most common budget blind spots.
Blind Spot 1: Prototype Costs ≠ Production Costs
Your prototype handles 10 queries a day with carefully crafted prompts. Production handles 10,000 queries with messy real-world input. Users ask edge-case questions. Agents retry on failures. Context windows fill up. The cost per query in production is typically 2-3x what you saw in testing.
Blind Spot 2: Token Inflation
System prompts grow over time. Every new feature, guardrail, and edge case handling adds tokens to your system prompt. A system prompt that started at 200 tokens bloats to 2,000 tokens within months. That's charged on every single API call.
Blind Spot 3: Multi-Turn Conversations
LLM APIs charge for the full conversation history on each turn. A 10-turn conversation doesn't cost 10x a single turn — it costs 55x (1+2+3+...+10 turns of accumulated context). Long conversations are disproportionately expensive.
Blind Spot 4: Error Multiplication
When an agent hallucinates a tool call, it fails, retries, potentially hallucinates again, and eventually escalates. A single bad response can trigger a chain of 5-10 additional LLM calls. At scale, error handling can account for 15-25% of your total LLM spend.
Building Accurate Projections
Run AgentBurn for two weeks in production with real traffic. Look at the P95 cost per task, not the average. Multiply by projected volume. Add 30% for growth and prompt changes. That's your real budget.
Teams that track from day one avoid the surprise $50K bill in month three.