How We Price AI Product Development Engagements

AI product pricing fails when treated like static website builds. We use phased engagements — discovery, MVP, production hardening — with eval and metric gates so founders and engineers share risk realistically. Fixed-price website thinking applied to AI guarantees either vendor margin padding or cut corners on eval — phased gates align risk for both parties. Transparent line items build trust — hidden inference markup destroys partnerships when clients learn true API costs.

Pricing principle

Phase gates with eval milestones — not one fixed bid on unknown data.

What drives cost

Data complexity, compliance, number of integrations, and eval rigor dominate — not model choice alone. Agent workflows cost more than single-shot RAG Q&A. Agent workflows with five or more tool calls cost more than single-shot Q&A — scope doc should count tool integrations explicitly. Healthcare BAA and HIPAA logging add review hours invisible in generic dev rates. Change requests after scope doc signed get written impact assessment — timeline and cost, not verbal OK.

Multilingual, multi-tenant, and healthcare paths add review and infra overhead. Real cost guide covers year-one economics in detail. Multilingual Indic support adds ASR testing and embedding benchmarks — not a config toggle. Real cost guide quantifies year-one ops clients should model internally. Equity-only engagements rare — we prioritize cash-plus-equity when aligned with long-term product success.

Engagement shapes

Discovery sprint: fixed fee, written scope and architecture. MVP build: phased fixed or milestone-based with golden-set acceptance. Retainer for ops, prompt iteration, and model updates post-launch. Discovery sprint deliverable is scope doc and architecture — pay for clarity before build commits. MVP milestones tie to golden-set pass rates and design partner task completion, not demo dates. Knowledge transfer hours included in handoff — client teams must operate eval CI independently.

We avoid unlimited scope for fixed price — it guarantees cut corners on eval. Align incentives: bonus tied to metric hits, not only ship date. Post-launch retainers cover model updates, prompt iteration, and index maintenance — budget monthly, not surprise invoices. Bonus structures tied to metric hits beat pure time-and-materials misalignment. Multi-phase discounts reward committed roadmaps — single-phase spikes cost more per week.

Questions to ask any vendor

What is included in handoff: code, docs, runbooks, eval suites? Who owns model bills and observability tooling post-launch? Handoff includes eval suites, runbooks, and observability dashboards — clarify ownership of Langfuse bills and cloud inference accounts. Vendors who bundle opaque inference markup hide true unit economics. Compare vendor proposals on eval ownership — who writes and maintains golden sets matters.

Transparent partners itemize inference, infra, and human review separately. Book a discovery call with scope doc as the first deliverable. Book discovery first — scope doc is the product of that engagement, whether or not build continues with us. Discovery fee credits toward build when continuing — lowers friction to start with audit.

What to do next

Start with one measurable outcome — latency, accuracy, adoption, or revenue — and design your stack around that constraint. GenOrbit ships production AI systems across healthcare, astrology, fintech, and SaaS; if you want a second pair of eyes on architecture or delivery, we are happy to talk.