Why “Complexity” Is the Single Biggest Lever in Your AI Build Cost Estimate

"It's just an MVP with login, a dashboard, and Stripe" can describe a project that costs $1,500 in AI tooling to build, or one that costs $8,000. The feature list is nearly the same. What changes the number is complexity — how much non-trivial reasoning, custom logic, and integration work sits behind those three words.

What "complexity" actually measures

• Simple. Mostly CRUD screens, templated UI, standard auth, minimal custom business logic. AI agents handle this with very little back-and-forth — high first-pass success rate, low token usage per feature.
• Medium. A standard SaaS app with some custom business logic, background jobs, and a handful of third-party integrations. Most features still go smoothly, but a few require real design decisions and iteration.
• Complex.Non-trivial integrations (payments edge cases, multi-tenant data isolation, custom algorithms), multiple coordinated services, or domain logic that doesn't map cleanly to a tutorial. Agents need more context, more iteration, and more verification per feature.
• Highly agentic.The product itself orchestrates AI agents or multi-step AI workflows — which means the build process has to get that orchestration right, debug non-deterministic behavior, and design for failure modes that don't exist in conventional software. This is the highest tier for a reason: it's the hardest thing to get an AI agent to build correctly on the first few passes.

AI coding intensity compounds complexity

Complexity describes the project. AI coding intensity describes how your team uses AI to build it — from light autocomplete-style usage, through a daily AI assistant, to AI agents editing files directly, to fully agentic tasks, up to autonomous multi-agent workflows. Higher intensity generally means faster delivery, but it also means more tokens consumed per feature — more exploration, more generated code to review, more iteration loops. On a simple project, high intensity is mostly a convenience. On a complex or highly agentic project, high intensity is often necessary just to make the work tractable — and the AI tooling spend reflects that.

Detected modules carry their own multiplier

Not all features are equal even within the same project. When Forecost's AI discovery analyzes a project description, certain module categories — payments and AI runtimefeatures in particular — are flagged with high build impact by default, regardless of overall project complexity. That's because these areas tend to involve edge cases (failed payments, webhooks, retries, partial AI responses) that require more careful implementation and testing than their surface-level description suggests.

Two MVPs, same feature list, different budgets

Consider two projects, both described as "a SaaS MVP with auth, a dashboard, and Stripe billing, two developers, 1–3 months."

• Project A is genuinely simple: single-tenant, one Stripe plan, no usage-based billing, standard auth. Complexity: simple. Expected AI build cost lands toward the low end of the range.
• Project Bhas the same labels but is multi-tenant with per-seat and usage-based billing, role-based permissions, and a custom onboarding flow that adapts based on plan. Complexity: complex. Even with the same team size and timeline, expected AI build cost lands well above Project A's — often by several times.

The lesson: when estimating, spend less time arguing about the feature list and more time being honest about which of these two projects you're actually building. That one judgment moves the estimate more than almost anything else.

Not sure where your project falls? Walk through the manual wizard and see how each answer shifts the estimate.