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December 2024 Enterprise Newsletter: AI Is Driving A Shift Towards Outcome-Based Pricing

Ivan Makarov, James da Costa, and Equals

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AI is driving a shift towards outcome-based pricing
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Posted December 19, 2024

This content first appeared in the December 2024 Enterprise Newsletter. If you’d like more commentary and analysis about news and trends from the a16z Enterprise team, you can subscribe here.

AI is driving a shift towards outcome-based pricing

Ivan Makarov, James da Costa, Equals

Pricing shifts are not new to the software industry. When SaaS first came to prominence, we saw a seismic shift to seat-based pricing, something that was completely foreign in an on-prem world. AI is now driving the beginning of yet another and possibly more dramatic pricing shift.

Software companies of all sizes, and founders of new AI companies in particular, are currently thinking through three key shifts where AI is challenging them to think differently about pricing:

Software is becoming labor. AI is turning what used to be pure service businesses into scalable software plays. Traditional services that required human labor — like customer support, sales, marketing, or back-office finance administration — can now be automated and packaged as software products. This has blurred the line between software and service pricing models.
Per-seat is no longer the atomic unit of software. Consider customer support software Zendesk: companies currently pay per support agent ($115/month/seat), but when AI can handle ticket resolution, the natural pricing metric becomes successful outcomes. If AI can handle a sizable proportion of customer support, companies will need far fewer human support agents, and therefore fewer Zendesk software seats. This forces software companies to fundamentally rethink their pricing models to align with the outcome they deliver rather than the number of humans that access their software.
Variable costs are less predictable. Nearly every AI startup builds on foundation models (e.g., OpenAI, Anthropic, Mistral) which come with significant variable costs that scale with AI model usage. Every API call, every token processed, adds to their cost structure. This is a fundamental change in the underlying unit economics of pricing the AI service. The marginal cost of an additional user or usage is not zero and varies by user. And while inference costs are dropping dramatically, tasks requiring the newest models with advanced reasoning capabilities still incur relatively high costs. AI companies are leaning into usage-based pricing to account for this.

There is no one-size-fits-all solution for pricing nor for responding to these shifts. However, we are beginning to see a number of archetypes emerge in the AI space, with a notable difference between those who are AI-native companies (e.g., Decagon, Cursor, ElevenLabs) and those who have added AI on top of existing core products (e.g., Zendesk, Notion, Canva). AI-native companies have leaned towards newer pricing models: usage (pay for what you consume), outcome (pay for what was delivered), or hybrid models (a combination). Decagon, for instance, offers per-conversation (usage-based) and per-resolution (outcome-based) pricing, while Cursor is seat-based with usage-based charges for premium models. Existing companies, meanwhile, have mostly stuck with per-seat or bundled options.

Ultimately, this is a rapidly evolving space because of all the innovation and unit economics pressure. We will likely see even more new pricing and GTM approaches emerge. Experimenting with different approaches to pricing your AI product or add-on is something to consider as you seek pricing-market fit.

Ivan Makarov is a finance and business operations partner on the early stage venture operations team at Andreessen Horowitz.

James da Costa is a partner at Andreessen Horowitz, where he focuses on investing in B2B software and financial services.

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