On a $2M ARR, Dresma’s largest customer pays $500,000 annually, representing 25% of their total revenue. This validates that a pure usage-based pricing model, without seat-based or feature-gated upsells, can successfully land and expand large enterprise accounts, demonstrating a clear path to significant customer lifetime value.
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Offering a transparent look into the costs of building on a foundational LLM, Dresma's CEO reveals that 25-30% of total revenue is spent on Gemini credits for content generation. This metric provides a crucial benchmark for founders and investors evaluating the gross margins and defensibility of AI-powered SaaS businesses.
Dresma debunks the myth that large US enterprise deals require US-based sales teams. Their two Account Executives are based in Gurgaon, India, with an average base salary of $20,000 USD. This capital-efficient model is supplemented with periodic travel to the US and Europe for crucial face-time with major customers.
Clay deliberately chose usage-based over seat-based pricing because their ideal customer is a technical builder (GTM Ops, Growth Marketer), not an individual salesperson. This model aligns value with the systems these builders create for the entire team, rather than charging for every end-user who benefits from the output.
In categories like customer support, where AI can handle the vast majority of queries, charging per human agent ('per seat') no longer makes sense. The business model is shifting to be outcome-based, where customers pay for the value delivered, such as per ticket resolved or per successful interaction.
Standard SaaS pricing fails for agentic products because high usage becomes a cost center. Avoid the trap of profiting from non-use. Instead, implement a hybrid model with a fixed base and usage-based overages, or, ideally, tie pricing directly to measurable outcomes generated by the AI.
The dominant per-user-per-month SaaS business model is becoming obsolete for AI-native companies. The new standard is consumption or outcome-based pricing. Customers will pay for the specific task an AI completes or the value it generates, not for a seat license, fundamentally changing how software is sold.
Beyond upfront pricing, sophisticated enterprise customers now demand cost certainty for consumption-based AI. They require vendors to provide transparent cost structures and protections for when usage inevitably scales, asking, 'What does the world look like when the flywheel actually spins?'
For tools requiring a new workflow, like Factory's AI agents, seat-based pricing creates friction. A usage-based model lowers the initial adoption barrier, allowing developers to try it once. This 'first try' is critical, as data shows an 85% retention rate after just one use.
In the age of AI, software is shifting from a tool that assists humans to an agent that completes tasks. The pricing model should reflect this. Instead of a subscription for access (a license), charge for the value created when the AI successfully achieves a business outcome.
AI startups often use traditional per-seat pricing to simplify purchasing for enterprise buyers. The CEO of Legora admits this is suboptimal for the vendor, as high LLM costs from power users can destroy margins. The shift to a more logical consumption-based model is currently blocked by the buyer's operational readiness, not the vendor's preference.
