What does it cost to add AI features to your SaaS?

This is the first question almost every founder asks us, and the honest answer is that it splits into two very different numbers: what it costs to build the feature, and what it costs to run it once real users hit it. Teams plan for the first and get surprised by the second.

What actually drives the cost

The headline price of an AI feature is mostly senior engineering time, and that scales with a handful of things:

• Scope: a single well-defined feature (a copilot, a search box, a classifier) is a few weeks. A platform of features is a quarter or more.
• Data access: connecting the model to your private data safely, with permission-aware retrieval, is usually the real work, not the prompt.
• Quality bar: a demo is cheap. A feature that holds up across edge cases needs evals, and evals take time to build.
• Latency and cost targets: hitting a price-per-request and a response-time budget at scale is engineering, not configuration.

The running cost is the part people miss

Once a feature ships, every interaction calls a model, and those calls have a unit cost. At a few hundred users it is a rounding error. At scale it can become one of your largest line items if nobody is watching it. We have seen features that worked perfectly in a demo become uneconomic in production simply because no one measured cost per request before launch.

The good news is that this is controllable. Caching, model routing (using a cheaper model where it is good enough), prompt compression, and strict token budgets routinely cut running cost by large margins without hurting quality. But it only works if you instrument it from the start.

Where teams overspend

• Building a broad platform before proving that one feature changes user behaviour.
• Reaching for fine-tuning when retrieval would have been cheaper and easier to maintain.
• Shipping without cost instrumentation, then discovering the bill after launch.
• Paying for premium models on every call when a cheaper model handles most of the traffic.

How we scope it

We start with a short paid sprint: a technical spec, a cost model with a measured price per request, and a working prototype against your real data. From there you know the build number and the running number before committing to the full feature. Most first versions ship in under six weeks; the cost model is what keeps them shippable at scale.