USD 0.0006 per AI scan: what GenAI really costs in production

The question “how much does generative AI cost” usually gets a vague answer. We have our own production number: USD 0.0006 per AI scan in mojApteczka. But that figure is only the tip — the real cost of a GenAI implementation sits elsewhere. Let’s break it down, on production data.

The number: USD 0.0006 per scan

That is the cost of one AI scan in production — what the system pays when the AI extracts structured data from a drug package. At that unit cost, ongoing inference is usually not the main cost barrier — the bigger risk sits in data, quality and operations.

Where the cost really is: before the model, not in it

The most honest lesson from building mojApteczka: the biggest work was before the model, not inside it — ETL, cleaning and versioning the knowledge. 302,516 records of drug interactions in the production knowledge base did not come from a prompt. This is the dominant, usually hidden cost of GenAI: data engineering, not tokens.

The three layers of GenAI cost

Inference (ongoing model calls) — the smallest line item; it sits inside the per-scan cost (USD 0.0006).
Data and knowledge (one-off and ongoing) — ETL, cleaning, embeddings, versioning. This is the bulk of the budget.
Operation — storage, retrieval (RAG), monitoring, quality evaluation. A fixed cost that grows with scale.

A budget built on line item 1 alone is incomplete and often too low — which hurts most after go-live.

A cheap scan that gets it wrong is expensive

A low unit cost only matters with controlled quality. Our packaging-data extraction reaches 100% accuracy on the validation set (n=200). That is why USD 0.0006 actually scales — we don’t pay a second time to fix the model’s mistakes.

Optimizing AI costs: how to lower GenAI cost without losing quality

Match the model to the task — a smaller model where it fully suffices.
Cache and batch for repeated calls.
RAG instead of long prompts — shorter context means fewer tokens per call.
Managed AWS services (e.g. Amazon Bedrock) instead of running models and infrastructure yourself.
Measure the unit cost from day one, not after the fact.

What an AI implementation costs for you

Your unit cost will be different — it depends on the task, the model and the state of your data. But the structure is usually similar: tokens are the minority, data is the majority. A readiness audit prices it for your case before you spend on the implementation itself.

What’s next

mojApteczka is our production proof: USD 0.0006 per scan, 302,516 records in the knowledge base, 100% AI extraction accuracy on packaging data (validation set, n=200). See how it works or read the case study. To learn the cost for your own process, get in touch.