How to add AI features to your product without blowing the budget

Almost every product roadmap now has an AI line item. A smart search box, an assistant that drafts replies, automatic tagging, document summaries. The prototypes are quick to build and impressive in a demo. The surprise usually arrives a month after launch, when the first full invoice from the model provider lands and nobody can say which feature caused it.

This guide is about avoiding that surprise. It covers where AI genuinely earns its keep, how AI pricing works, and the practical levers that keep costs predictable without making the feature worse.

Why AI costs catch teams off guard

Traditional software has mostly fixed costs. A server costs roughly the same whether a page is viewed ten times or ten thousand times, up to a point. Large language model APIs are different: you pay for every request, measured in tokens. A token is a chunk of text, roughly three quarters of an English word on average. You pay for the tokens you send in (the input) and the tokens the model writes back (the output), and output tokens are usually priced several times higher than input tokens.

Three things multiply that per-request cost:

• Context size. If every request includes a long system prompt, the full conversation history and several pages of reference documents, you pay for all of it on every single call.
• Frequency. A feature that runs automatically on every page view or every keystroke costs far more than one the user triggers on purpose.
• Growth. Unlike a server, usage-based costs scale linearly with your users. Success makes the bill bigger, not smaller per user.

None of this means AI is too expensive. It means AI costs need the same design attention as database queries or image sizes.

Where AI features genuinely earn their keep

Before optimising cost, make sure the feature is worth paying for at all. Language models are strongest where the input is messy, written in natural language, or needs judgement that would be painful to encode as rules:

• Search that understands meaning. Customers search for “quiet flat near a park” rather than exact keywords. Embedding-based semantic search handles this well, and embeddings are cheap compared with text generation.
• Summarising long content. Turning a 40-message support thread or a long contract into a short summary saves real staff time.
• Extracting structure from documents. Pulling names, dates, amounts and line items out of invoices, CVs or forms.
• Classification and routing. Tagging incoming tickets, sorting leads by intent, flagging content for review.
• Drafting. First drafts of replies, product descriptions or reports that a person then edits and approves.

AI is a poor fit for exact calculations, anything governed by clear business rules, and decisions that must be right every time without review. Tax calculations, pricing rules and permissions belong in ordinary code.

The cost levers that matter most

1. Use the smallest model that passes your tests

Model providers offer families of models at very different price points. The largest model is rarely needed for classification, extraction or short summaries. Build a test set of 50 to 100 real examples with the answers you expect, run it against two or three model sizes, and choose the cheapest one that meets your quality bar. Re-run the test whenever you change prompts or models.

2. Precompute instead of computing on every view

If a product summary or a set of tags does not change between visitors, generate it once when the content is created or updated and store it in your database. Reading a stored summary costs nothing. Generating it on every page view can cost thousands of times more for the same result.

3. Send only what the model needs

Retrieval-augmented generation (RAG) means searching your own data first and sending only the most relevant passages to the model, instead of whole documents. It usually improves answer quality as well as cost, because the model is not distracted by irrelevant text. Trim conversation history too: keep the last few turns plus a short running summary rather than the entire chat.

4. Use caching and batch discounts

Several major providers offer prompt caching, where a repeated prefix such as a long system prompt is billed at a reduced rate on later calls. Many also offer batch APIs for work that does not need an instant answer, such as nightly tagging or report generation, at a significant discount compared with real-time calls. On top of that, cache complete responses in your own system when the same question is asked with the same inputs.

5. Cap output and set limits

Always set a maximum output length. Ask for structured output, such as JSON with specific fields, rather than open-ended prose when your code only needs a few values. Add per-user and per-account daily limits so that one heavy user, a bug in a loop or a scripted abuse attempt cannot run up an unbounded bill.

Matching the approach to the feature

Measure cost like any other product metric

Log the model, input tokens, output tokens and feature name for every call. From those logs you can calculate the number that actually matters: AI cost per feature per monthly active user. Put it on the same dashboard as your hosting costs and review it every month. When a number moves, you will know which feature moved it.

Pair cost with quality. Keep your evaluation test set in the repository, run it in your build pipeline when prompts change, and track how often users accept, edit or reject AI output. A cheaper model that users constantly correct is not actually cheaper.

Pricing AI features for your own customers

If AI is part of a paid product, decide early how it maps to your pricing. Common patterns are a fair-use allowance included in each plan, monthly credits that reset, or heavier AI features reserved for higher tiers. Whatever you choose, make limits visible to users rather than silently degrading quality when they hit them.

A sensible rollout plan

1. Pick one feature with a clear job and a measurable outcome, such as time saved per support ticket.
2. Build a small evaluation set from real, anonymised examples.
3. Prototype with a mid-sized model, then test whether a smaller one passes.
4. Add logging, output limits and per-user quotas before any customer sees it.
5. Release to internal users, then a beta group, behind a feature flag.
6. Review cost per active user and quality metrics after two to four weeks, then decide whether to expand.

Done this way, AI features stay affordable as you grow, and you can explain every line of the bill. If you would like a second opinion on an AI feature you are planning, we are happy to review the design and estimate its running cost before you build it.