Leveraging AI in Subscription Models

Does AI have an unused growth lever hiding in plain sight? AI compute feels expensive. Users pay $20 to $200+ for access, and APIs charge by the token. But behind the scenes, inference costs are dropping, usage breakage is high, and scalable compute is becoming more abundant for many workloads. There’s a widening gap between what AI feels like it’s worth and what it actually costs to deliver. That’s an arbitrage opportunity. And while not exact, it looks a lot like what Dropbox pulled off in 2009. Remember: Dropbox gave away 250MB of storage for every referral. It felt huge, because hard drives were expensive. But it cost them almost nothing to incrementally add space on AWS. That’s the part everyone remembers. But what mattered more was that the product got better when more people used it. The storage unlocked shared folders, syncing, and collaboration. The referral loop wasn’t just about the incentive. It was about amplifying value through the network. Today, most AI platforms (ChatGPT, Claude, Gemini, Perplexity) give away compute too. They just do it through solo-player free tiers: – Limited speed – Smaller models – Capped usage – No shared context – No incentive to bring others in It’s a sampling strategy, not a viral engine. What if we built the Dropbox version for AI? A Better Model: Shared Compute, Shared Context, Shared Incentive Don’t just give away tokens. Pool them. Share them. Multiply them. Unlike disk space, people can't price a token, but they can price outcomes that matter. What if: You invite a teammate → your model remembers both your histories. Your group shares credits → your assistant becomes more capable. Your team grows → projects get done faster and better. Invites unlock more context, more memory, more speed—more wins for everyone in the workspace. Now you’re not just giving away compute. You’re giving away collaboration. Here's why this could work: Perceived Value → Tokens still feel scarce. Leverage that before the market catches up. Strategic Differentiation → Everyone else is playing the solo-player free tier. You’re building shared systems. Compounding Retention → Team-based tools stick longer. And grow faster. Product-Led Growth, Upgraded Sampling → Usage → Friction → Upgrade becomes Referral → Activation → Shared Value → Expansion Dropbox didn’t scale just because it gave away free megabytes. It scaled because it let people do something together they couldn’t do before—seamlessly. AI has the same opportunity. Maybe even bigger. How can we start thinking of compute not as a solo resource, but as a networked asset? The first company to build this loop could unlock something magical. Let me know if you're working on it.

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. If you are increasing the productivity of labor or usurping it, how should you price this? If every action your customer takes incurs a corresponding cost through an API call, how should you factor that in? How will buyers react to pricing models they’ve not seen before? There’s a lot to consider. However, AI-native companies are leaning into this shift. For instance, Decagon, an AI customer support platform whose AI agents autonomously resolve customer service tickets, offers per-conversation (usage-based) and per-resolution (outcome-based) pricing models to their customers. Both models scale with the amount of work completed (i.e. value delivered) vs. labor (software seats). Read more on Emerging AI Pricing Models in the a16z Enterprise Newsletter with Ivan Makarov and Equals 👇

We're moving away from charging for *access* to software and toward a model of charging for the *work delivered* by a combination of software and AI agents. Let’s dive into what’s happening and what it means for you ⤵️ 1. The rise of disruptive AI pricing models Tech companies are realizing they can't solely rely on seat-based subscriptions in an age of AI, automation and APIs where value is disconnected with how many people are logging in. Perhaps Salesforce going all-in on Agentforce (and charging $2 per conversation) was the push the industry needed. Each product category has its own flavor of disruptive pricing. - Legal AI products might charge for a demand package generated by AI or an AI-generated summary. - Creator AI products might charge for the content that gets produced such as a video generation or amount of video created. - GTM products might charge for specific tasks completed or workflows executed by the AI. 2. Selling work, not necessarily success As a customer, I wish I only had to pay for software when it delivered results. But the reality is that true success-based billing won’t work for the vast majority of today’s products. Most products should charge for work output instead. The issue is attribution. You want the customer to get a fantastic outcome — and you want them to recognize that your product powered that outcome. As soon as you start charging for success, the customer begins to rethink the results. 3. Goodbye ARR as we know it? Shifting to these newer value-based pricing models isn't a simple pricing change you can just announce in a press release. It's a business model evolution that looks a lot like the shift from on-prem to SaaS in the first place. These new AI pricing models might mean greater volatility in both usage and spend. Variable margin profiles across products and customers. Seasonal revenue fluctuations. The potential for project-based, non-recurring use cases. Put simply, annual recurring revenue (ARR) continues to get dethroned. — Full post in today’s Growth Unhinged newsletter: https://lnkd.in/ea5eTrVD Things are about to get interesting 🍿 #ai #pricing #saas

🚀AI Agent Pricing Models The rise of agentic AI is changing how enterprises think about AI adoption. As I have called out in my previous posts, this technology promises efficiency, flexibility, and improved outcomes, a key question remains: how will AI agents be priced? Major companies are already starting to leverage different pricing models, each catering to different use cases and business needs. 👉 Here’s a breakdown of the key models: 1️⃣ Per-Conversation Pricing: Salesforce has introduced a model where businesses pay $2 per conversation, with "conversation" defined as a 24-hour interaction window. This approach works well for companies that have predictable, low-to-moderate interaction volumes. However, for organizations with high usage, this could quickly become expensive. 2️⃣ Outcome-Based Pricing: This model ties costs directly to successful task completions or outcomes. It’s intuitive and aligns pricing with value delivered, but defining and agreeing on outcomes can be challenging, especially in complex scenarios. 3️⃣Cost-Plus Pricing: Here, pricing is based on the underlying compute and operational costs with a small markup. It’s transparent and predictable but as any cost plus model, doesn't always capture the full value the AI delivers. 4️⃣ Subscription or Per-Seat Pricing: A flat-fee subscription model or per-seat pricing offers unlimited use within a fixed cost structure. This is ideal for organizations seeking predictability in budgets but may undervalue AI agents in low-usage scenarios. 5️⃣Consumption-Based Pricing: In this model, businesses pay based on the number of tokens processed or generated. While it’s precise, the unpredictability of costs—especially during spikes in usage—makes it risky for organizations with fixed budgets. For leaders, the choice of pricing model depends on specific use cases, desired outcomes, and usage patterns. Subscription models offer predictability, while outcome-based models provide ROI alignment. It’s crucial to balance cost, transparency, and flexibility to avoid lock-in and unexpected expenses. 🎯As agentic AI adoption grows, I think pricing models will continue to evolve. Hybrid approaches combining cost-based transparency with performance-driven incentives may emerge as the standard. But for now, considering how early we are in AI agents adoption, CIOs must assess their organization's needs and build forecasts to identify the best-fit model. I write about #artificialintelligence | #Technology | #Startup | #mentoring | #Leadership Vignesh Kumar

Orb 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 “𝟮𝟬𝟮𝟱 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗣𝗿𝗶𝗰𝗶𝗻𝗴” 𝗿𝗲𝗽𝗼𝗿𝘁 — 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗼𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝘆𝗲𝘁 𝗼𝗻 𝗛𝗢𝗪 𝘁𝗼 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗳𝗼𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀. ⬇️ The report analyzes the pricing strategies of 66 companies offering AI agent products. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗴𝘂𝗶𝗱𝗲 𝗰𝗼𝘃𝗲𝗿𝘀: ⬇️ 1. Orb identified 8 foundational pricing components → This are the pricing core models currently emerging in the market: • Subscription – Flat recurring fee for access, usually monthly or annually. • Per user or seat – Charged based on the number of individual users. • Usage-based – Scales with consumption (e.g. tokens, API calls, generations). • Outcome-based – Pricing tied to results (e.g. leads closed, tickets resolved). • Freemium or free trial – Free limited access to drive adoption and conversion. • Tiered – Pricing packages with increasing features or usage limits. • Add-ons – Paid upgrades for advanced features or premium support. • Hybrid – A mix of models to balance predictability, flexibility, and value capture. 2. Hybrid pricing is the default  → 92.4% of companies now combine multiple pricing components — most commonly subscription + usage + freemium + tiered access. Understanding these levers is now table stakes for anyone pricing agents. 3. SaaS-only pricing will kill your margins  → Flat rates break under AI’s compute load. 85% of SaaS-based offerings now layer in usage pricing to avoid margin collapse. 4. Outcome-based pricing is a wide open lane  → Only 4.5% of companies tie price to actual business results. But the strategic upside is enormous — especially for agents replacing human work. 5. Parallel pricing = segmentation superpower  → 12% of vendors now offer distinct models for different audiences (e.g., flat-rate for individuals, per-seat for teams). This flexibility fuels learning and market fit. 6. Billing infra is now a moat  → Hybrid pricing adds complexity fast. If your billing stack can’t handle dynamic usage, add-ons, or outcome tracking — you’re flying blind. Pricing isn’t a table in a Google Sheet. It’s your growth mechanic. It’s part of the product — and it’s one of your strongest levers for growth. Full report below. ⬇️ Enjoy. 𝗣.𝗦. 𝗜 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝗜 𝘄𝗿𝗶𝘁𝗲 𝗮𝗯𝗼𝘂𝘁 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘁𝗵𝗲𝘀𝗲 𝘀𝗵𝗶𝗳𝘁𝘀 𝗲𝘃𝗲𝗿𝘆 𝘄𝗲𝗲𝗸 — 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝘄𝗵𝗶𝗹𝗲 𝗼𝘁𝗵𝗲𝗿𝘀 𝘄𝗮𝘁𝗰𝗵 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀. 𝗜𝘁’𝘀 𝗳𝗿𝗲𝗲, 𝗮𝗻𝗱 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/dbf74Y9E