AI Feature Development Cost: The Monthly Bill Breaks the Budget, Not the Build

The median of the 9 API integration and backend quotes Freesi issued in 2026 was 1,500,000 KRW (approx. USD 1,100). Most work that adds AI features falls into that category.

The problem is that it does not end there. Ordinary software costs you a server bill once it is built; AI features charge you every time they run. More users means a bigger bill, and a poor design lets the running cost overtake the build cost within months.

So an AI project quote should come in two lines: what it costs to build (once) and what it costs to run (monthly). Start with only the first line and month three is an unpleasant surprise.

This is why the same request to "add AI" produces quotes that differ by an order of magnitude.

Tier 1. Prompt calls — send a request to an external AI model and use the answer. Summarizing documents, routing inquiries, drafting replies, classifying review sentiment. Most "AI features" are really this tier, and stopping here is also the cheapest.

Tier 2. Internal document search (RAG) — find your own company's documents and answer based on their contents. Internal policy Q&A and product manual chatbots are typical. Splitting and storing documents plus tuning retrieval quality adds substantially to the build.

Tier 3. Fine-tuning — teach the model your tone of voice or a fixed output format. Building the training data often takes longer than the development. This is a step you reach after tiers 1 and 2 have demonstrably failed, not a card to play from the start.

Tier 4. Self-hosted models — run the model on your own servers because the data cannot leave your network. Here hardware and server costs exceed the development fee.

Most companies want tier 1, and tier 2 covers the rest. If a vendor proposes tier 3 or 4 up front, ask them to justify why.

Excluding VAT. Monthly running cost varies enormously with usage assumptions, so it is given as a band only.

One thing attaches to all four tiers. Because AI states wrong answers confidently, you need a way for a person to check or reverse the output. The quote that omits this is the cheapest and the most dangerous.

Model pricing changes often, so do not memorize rates — remember the structure of the calculation.

Monthly cost = (input volume per request × input rate + output volume × output rate) × monthly request count × retry factor

Volume is counted in tokens. As a rule of thumb, Korean text runs about 1–2 tokens per character; English runs closer to 4 characters per token. A single page of text is roughly 1,500–3,000 tokens.

Three things are easy to miss when you actually run the numbers.

Input is usually much larger than output. Feed in a whole document and the answer may be three lines, but the bill comes from the document side

Retries double the bill. Include calls repeated because the output did not match the required format in your retry factor

Testing costs money too. Budget for the call volume during the development period itself

When you receive a quote, ask what monthly request volume the running cost assumes. A vendor who cannot produce that number has not calculated running cost at all.

The places where budgets break are fairly predictable.

Sending an entire long document on every request. Even with a short question, attaching the full reference document to every call turns document length directly into cost. Restructuring so only the relevant passages are sent (tier 2) drops the bill by an order of magnitude. It is a trade: more build cost, less running cost.

Appending conversation history without limit. Common in chatbots. The longer a conversation runs, the larger every request's input becomes, so the same user gets more expensive the longer they stay. Older turns need to be summarized before being passed along.

No per-user limits. Open a feature for free and one person sending thousands of requests a day is billed straight to you. Per-account and per-hour caps are not a feature; they are a safety mechanism.

All three are expensive to fix after the fact because the structure has to be redesigned. Asking about them at the quoting stage is the cheapest moment.

The judgment AI will make: in one sentence, what it looks at and what it decides

The reference material: whether internal documents are needed, and if so how many and in what format

Usage: how many times a day, by how many people

When it is wrong: does a person review it, or does the output go straight out? (This decides the architecture.)

Data restrictions: is there material that cannot be sent to an external model?

The fourth and fifth are what set the tier. If answers go straight to customers without review, or if data cannot leave your network, the whole cost band moves up.

For adopting AI tools internally rather than shipping them, see evaluating Codex for company use; for ordinary backend integration rates, see API integration cost.