Building Zaon's Pricing Model: Clear Value, Predictable AI Costs

Introduction

During his Zaon Labs AI Product Manager internship, Marcelo developed a pricing model grounded in market research and competitive analysis to help inform revenue strategy. The intent was to create a structure that communicates value simply, supports both solo adoption and team rollout, and reduces surprise costs that can stall adoption in AI products.

The model was designed around two self-serve tiers, Individual and Teams, and a clear choice between two model approaches: a Zaon-hosted option with token costs included, and a Bring Your Own Model option where customers use their own AI API keys at a lower price.

Opportunity identification

Zaon is positioned as a centralized business solution for AI, where employees work consistently under one platform. The pricing challenge is that AI costs do not behave like traditional SaaS. Usage varies across customers and workflows, and different buyers want different tradeoffs between predictable spend and control over model providers and keys. When pricing is hard to explain internally, procurement slows and early adoption becomes fragile, even when the product itself is strong.

Strategic product decisions

Marcelo anchored the structure on clarity first by separating packaging from cost mechanics. The core offer is two self-serve tiers that are easy to understand and map to how usage scales: Individual (monthly) and Teams (monthly, per-user). Within both tiers, the model supports two customer-driven options so buyers can choose the tradeoff that fits their organization: Zaon-hosted models for predictability with tokens included, or Bring Your Own Model for maximum control and a lower price point by supplying their own AI API keys.

Because pricing work often starts with incomplete requirements, he treated ambiguity as something to manage explicitly. His approach was to define what the model needed to accomplish, clarify dependencies, document what was known versus unknown, and use intermediate checkpoints to keep stakeholders aligned as the plan evolved.

Enterprise options exist separately, but this work focused on the self-serve plans where clarity, speed to adoption, and predictable spend are most critical.

Measurement framework

The pricing model was designed to be evaluated using a mix of growth signals and operational clarity signals. On the growth side, the core questions are whether individuals convert, whether Teams expands in seats over time, and what the adoption mix looks like between hosted and BYOM. On the operational side, the focus is on whether hosted plans maintain stable margins given token inclusion, and whether sales cycles show fewer pricing objections and less back-and-forth during quoting and procurement.

Key outcome

The result was a pricing framework leadership could use for upcoming launches, pairing a simple tiered story with real flexibility around model costs and control. It was designed to help Zaon sell a clear starting point while still supporting the real-world variability of AI usage, without pushing complexity onto the customer too early in the adoption journey.