Ask ten firms what an AI product costs to build and you get ten sales pitches. Published cost data for custom software is thin, self-serving, or five years stale — and it almost never separates the build from the hidden multipliers that actually break budgets. We compiled the numbers ourselves.
Why this report exists
Most cost content online is lead-gen fluff: wide ranges, no method, no line items. Founders and product leaders end up budgeting from vendor quotes — the least neutral data source available. This benchmark is our attempt at a straight answer, drawn from real engagements at Binari Digital and cross-checked against verified market data. Where we don't know, we say so.
What's inside
- Cost benchmark tables by project type — MVP, agentic AI system, data platform, and tokenization rail — with the drivers that move each range up or down.
- Hidden-cost multipliers — integration debt, evaluation and QA for non-deterministic AI systems, compliance review, and the rework tax on underspecified scope.
- A dev-phase cloud-spend model — including what teams save by hosting development and staging on managed on-premise infrastructure before promoting to AWS/GCP/Azure at launch, the way we run every build.
- US vs EU cost deltas — engineering rates plus the compliance overhead layer: GDPR, the EU AI Act, MiCA, and DORA on one side; the GENIUS Act on the other.
- Budget worksheets — fill-in templates to pressure-test a vendor quote or an internal estimate before you commit.
Three preview findings
- The build is rarely the biggest line item. Across the projects we studied, integration, evaluation, and compliance work routinely rivaled — and sometimes exceeded — feature development. Budgets that only price the feature list miss the majority of the risk.
- Agentic AI costs cluster around evaluation, not models. Model API spend is a rounding error next to the harnesses, test data, and iteration loops needed to make agent behavior shippable. Teams that skip this line item pay it later, with interest.
- Dev-phase cloud burn is mostly avoidable. Teams paying production-cloud rates for pre-launch environments spend materially more than teams who containerize from day one and promote at launch. The savings are real; the report quantifies the model.
Method
Benchmarks are drawn from Binari engagements — typically $100K+, with MVPs from $60–150K — anonymized and normalized, then cross-referenced with verified public market data. No client-specific figures appear without abstraction. For how we scope and de-risk builds before pricing them, see our discovery sprint and the rest of our research.
The full report is free for qualified teams — request it via the form below.
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