Here’s a story I keep hearing. Founder builds a product using OpenAI’s API on the free tier or the cheapest plan. Works great. Demo looks slick. Investors are impressed. Product-market fit starts happening. Users grow from 100 to 1,000 to 5,000.

Then the invoice hits.

I talked to a founder last month who went from spending $40/month on API calls to $3,200/month in the space of twelve weeks. That wasn’t a surprise — that was the plan. What surprised him was the $3,200 became $8,500 the month after, because usage patterns at scale are nothing like usage patterns in a prototype.

This is the AI API pricing trap, and it catches founders who don’t model their costs past the first few hundred users.

The free tier illusion

Most AI API providers structure their pricing the same way drug dealers structure theirs: the first taste is free or cheap.

OpenAI’s pricing looks reasonable on paper. GPT-4o at $2.50 per million input tokens and $10 per million output tokens. Fine. At 100 users making 5 requests a day with modest token usage, you’re looking at maybe $50-80/month. Coffee money.

But here’s what changes at scale:

Token usage per request goes up, not down. In your prototype, you sent short prompts with minimal context. In production, you’re stuffing system prompts with user history, conversation context, RAG retrieval results, and safety instructions. Your average request just went from 500 tokens to 3,000 tokens. Your cost just 6x’d.

Users don’t behave like your test scenarios. Your stress test assumed users would send 5 requests a day. Your power users send 50. And they write long messages. And they follow up with “elaborate on that” which doubles the output tokens. One chatty user can cost you as much as twenty average ones.

Retry logic is expensive. API calls fail. Rate limits trigger. Timeouts happen. So you build retry logic. Now you’re paying for failed calls plus retry calls. At scale, retry costs alone can add 15-25% to your bill.

You need multiple models. Your prototype used one model for everything. In production, you need a cheap model for classification, a mid-tier model for routine tasks, and an expensive model for complex reasoning. Managing this model routing adds engineering complexity and doesn’t reduce costs as much as you’d expect.

The cost curve nobody shows you

Here’s a rough model based on what I’ve seen across startups I advise and my own experience:

• 0-500 users: $50-200/month. Barely registers.
• 500-2,000 users: $500-2,000/month. Noticeable but manageable.
• 2,000-10,000 users: $2,000-15,000/month. This is where it gets scary. At this stage you’re typically pre-profit, maybe post-seed, and suddenly your AI costs are a top-three expense.
• 10,000+ users: $10,000-50,000+/month. You’d better have a business model that supports this or you’re burning runway on API calls.

These numbers assume a product where AI is core to the experience — not a sidebar feature. If you’re running a SaaS where AI handles customer queries, generates content, or processes documents as a primary function, this is your reality.

The costs nobody budgets for

API pricing is just the visible part. Here are the costs that sneak up on you:

Monitoring and observability. At scale, you need to track API performance, response quality, cost per user, cost per feature, latency percentiles, and error rates. Tools like LangSmith or Helicone cost money. Building your own costs engineering time. Either way, it’s a real expense.

Prompt engineering maintenance. Your prompts aren’t set-and-forget. Model updates change behaviour. User feedback reveals edge cases. New features require new prompts. Someone on your team is going to spend meaningful hours maintaining and optimising prompts. That’s salary cost directly attributable to your AI dependency.

Caching infrastructure. Smart caching can reduce API calls by 30-50% for many use cases. But building a semantic cache isn’t trivial. You need embedding models, vector storage, and cache invalidation logic. This is a real engineering project.

Fallback systems. What happens when the API goes down? If your product is 100% dependent on a third-party API, you need a fallback strategy. That might mean maintaining integrations with two providers, or building a degraded-experience mode, or caching common responses. All cost time and money.

Quality assurance. At 100 users, you can manually review AI outputs. At 10,000 users, you need automated quality checks, user feedback loops, and systematic evaluation frameworks. This is often an entire workstream that didn’t exist in your prototype.

What to do about it

I’m not saying don’t use AI APIs. I’m saying model the costs honestly before you’re locked in.

Run realistic cost projections. Don’t use your prototype usage patterns. Talk to other startups at your target scale. Multiply your optimistic estimate by 3x for your planning scenario. If the business still works at 3x, you’re probably fine.

Build model routing from day one. Use the cheapest model that produces acceptable results for each task. Classification? Use a small model. Summarisation? Mid-tier. Complex reasoning? Premium. The engineering overhead of model routing is worth it — I’ve seen it reduce costs by 40-60%.

Implement usage controls. Set per-user limits. Throttle heavy users. Charge for premium tiers. Don’t let one power user’s API usage subsidise everyone else. This is product design, not just cost management.

Negotiate volume pricing early. If you’re spending $5,000+/month, talk to your provider. OpenAI, Anthropic, and Google all have volume commitments that reduce per-token costs significantly. Don’t wait until you’re at $20,000/month to start this conversation.

Consider open-source models. For many tasks, running a fine-tuned open model on your own infrastructure (or a managed platform like Together.ai or Replicate) is cheaper at scale than API calls. The breakeven point varies, but it’s often around $5,000-10,000/month in API spend.

Price your product accordingly. This seems obvious, but an alarming number of startups set pricing before they understand their AI costs at scale. If your per-user AI cost is $5/month and your subscription price is $10/month, you’d better have incredible margins on everything else.

The uncomfortable truth

Free AI APIs are a growth hack for getting started. They’re not a business model. If AI is central to your product, your unit economics must account for AI costs at 10x your current scale. If they don’t work at that level, you don’t have a sustainable business — you have a demo that’s temporarily cheap to run.

I’ve been burned by this personally. I’ve watched friends get burned by it. The founders who avoid the trap are the ones who build a cost model in month one and update it every month. It’s not glamorous work. But it’s the kind of thing that determines whether you’re still operating in twelve months.

Do the maths. Do it early. Do it honestly. Your bank account will thank you.