Hybrid Offer Models

💰 AI PRICING is going to be hybrid. 💰 Here's why. - Pure per-seat doesn't work for agents. A company with 100 users can run 100 agent tasks a day or 10 million. The headcount doesn't change. The workload does. - Pure consumption also doesn't work. CFOs hate it. "Your bill could be $50K or $5M depending on usage" is not a sentence that flies. So what wins? Think: your phone plan. Remember when it used to be per minute and megabytes? We moved away from it. Flat rate for the predictable stuff. Variable for the unpredictable stuff. You know exactly what your plan costs. International roaming is based on usage. AI will land in a similar place. Fixed fee covers the predictable stuff, including some agentic work: the AI agents and assistants that run on a reasonable cadence, bounded context, expected volume. That's budgetable. Variable kicks in when it's not bounded. Agent runs 50,000 times a day, at random. Context window blows up on a very large query. That's when consumption pricing makes more sense. The makers who figure out the right split win enterprise. The ones pushing pure consumption will lose deals to budget anxiety. The ones holding onto pure seats will watch their economics collapse as agents scale. Hybrid is the model that works for both sides. Every mature category got here eventually. AI is next. 😎

Everyone agrees usage-based pricing is the future of SaaS. But is it ACTUALLY delivering stronger performance? We dug into the 2025 Software Equity Group B2B SaaS report and layered in Monevate’s own pricing and segmentation data. Here’s what we found: 💡 Usage-based pricing is now more common than user-based (37 companies vs. 24 in the dataset). The shift is real. 📉 But the performance tells a more complicated story. Usage-based models show similar growth to user-based, but with significantly lower EBITDA margins. 🤔 However, valuation multiples for companies with each model are nearly identical. That disconnect suggests investor preference is inflating usage-based multiples beyond what fundamentals alone would justify. Does that mean usage-based pricing isn’t delivering? No—there’s a variant that is clearly a winner. 🔀 Hybrid pricing, combining elements of user-based AND usage-based models, is outperforming both. On ALL key performance metrics. ✔️ Higher growth. ✔️ Higher margins. ✔️ Higher valuation multiples. Often seen as “the best of both worlds,” hybrid pricing’s performance data suggests it might just be living up to that title. 👇  Seeing this in your own data or GTM shifts? Want the full findings? Drop a comment or DM me and I’ll send the full 10-slide deck + dataset. #SaaS #Pricing #HybridPricing #UsageBasedPricing #Valuation #SaaSGrowth #SaaSInvesting -- Hybrid pricing outperforms user- and usage-based models on growth, margin, AND valuation.

For years, it was a debate: Product-led or sales-led? Self-serve or high-touch? PLG or enterprise sales? Pick one. Can't do both. Turns out, that was wrong. The best SaaS companies are doing both. And it's working. What's happening: Companies starting PLG, adding sales for expansion. Companies starting sales-led, adding self-serve for smaller deals. The hybrid model is winning. Examples: Slack: Started pure PLG. Free teams sign up. Upgrade when ready. Then added enterprise sales team for Fortune 500. Now: Self-serve for SMB. Sales-led for enterprise. Notion: Free individual accounts. Teams form organically. Enterprise sales team closes 6-figure deals with strategic accounts. Figma: Designers sign up free. Teams adopt bottom-up. Sales team comes in when company wants enterprise features. The pattern: PLG gets you in the door fast. Sales takes you upmarket. Why this works: PLG creates demand. Sales captures value. Small teams self-serve at $50/month. Large companies need hand-holding at $50K/year. Same product. Different motion. Stop asking "PLG or sales?" Start asking "Which customer segments need which motion?" Small teams with simple needs → PLG Large teams with complex needs → Sales The infrastructure this requires: Self-serve onboarding (no human needed) Usage-based triggers (when to involve sales) Expansion playbooks (moving PLG users to paid plans) Sales team that can take warm PLG leads and expand them Companies getting this right: Calendly: Free for individuals. Sales for teams Airtable: Free for small teams. Enterprise sales for large. Loom: Self-serve everywhere. Sales for strategic accounts. The insight: You're not choosing between PLG and sales. You're choosing which customers get which experience. The question for your SaaS: Which customer segments should self-serve? Which need high-touch sales? And how do you move between them? That's the game now.

One of the least-discussed challenges in AI adoption today is pricing. Everyone talks about model performance, benchmarks, or features. But for enterprises, the real sticking point often shows up when the bill discussion starts. The problem: current pricing models don’t align with how enterprises budget and buy. Usage-based pricing makes perfect sense for vendors, but it feels like a blank cheque for buyers. If adoption succeeds, the bill grows in unpredictable ways. No CFO wants to be surprised by a doubling in costs because usage spiked. Flat subscriptions feel safer for buyers, but they put vendors at risk. The underlying compute costs fluctuate, and a heavy customer can easily push margins underwater. Hybrid models try to balance the two, to put in predictability for buyers’ forecast, and vendors try to to defend and improve profitability. This mismatch slows progress. Solution: a new generation of pricing models. Simple enough to understand, predictable enough to budget for, but still sustainable for vendors. It could also mean having periodic reviews instead of fixed term pricing for multi year deals. That could mean outcome-based contracts, tiered usage bands with hard caps, or bundled services that absorb variability in spikes. Until AI economics are solved, adoption will remain slower than the technology itself.

TymeBank (South Africa) and Moniepoint (Nigeria) have achieved unicorn status with valuations of $1.5 billion and over $1 billion, respectively, by blending digital banking with physical touchpoints. This hybrid model caters to Africa’s 90% cash-based economy and unbanked populations, overcoming barriers like unreliable internet and low trust in online-only systems. Together, these fintechs now serve over 25 million users, redefining what scaling financial inclusion looks like in emerging markets. SO WHAT TymeBank's partnership with supermarkets like Pick n Pay has enabled the deployment of over 1,000 kiosks and 15,000 retail points across South Africa, allowing it to grow to 15 million users. Moniepoint’s 200,000 agents, acting as human ATMs, bridge the gap in Nigeria, where only 16 ATMs per 100,000 adults exist, supporting over 10 million users. Both companies are expanding into Asia and broader African markets, leveraging $360 million in recent funding rounds to replicate their models. A digital-only strategy, like that pursued by Kuda (valued at $500 million), may be more scalable in regions with higher internet penetration and digital trust. However, it risks limiting market reach in areas where 43% or fewer have reliable connectivity. Think about it this way: the hybrid model embraces complexity to unlock growth in underserved regions. Could a hybrid approach redefine banking for other industries or regions, or is this model uniquely suited to Africa’s fintech challenges? What’s your take on scaling such a model sustainably? #fintech

Relying solely on traditional ad revenue simply isn’t enough anymore—sustainable growth depends on diversifying income streams. Ad revenues are under pressure, with CPMs declining 18% year-on-year (Reuters Institute, 2024) and stricter privacy regulations limiting traditional advertising’s effectiveness. A case study from The Guardian demonstrates that a strategic shift to hybrid revenue models can significantly boost performance. The Guardian transformed its approach by introducing tiered memberships that offer premium analysis and live editor Q&A sessions. This strategy not only tripled revenue in 12 months but also achieved a 32% membership uptake. Similarly, Forbes tapped into NFTs, providing over 10,000 subscribers with exclusive event access and early article previews—clear evidence that audiences are ready to pay for exclusivity. Even more telling, The New York Times now derives 64% of its revenue from subscriptions, while publishers like The Information have further strengthened their community ties by launching subscriber-only apps that reduce third-party dependencies. These initiatives reflect a broader shift in audience expectations. Consumers are increasingly drawn to high-quality, exclusive content and personalised experiences rather than generic, ad-supported material. Moving beyond an ad-only strategy isn’t just about following trends—it’s a practical move to secure your business for the future by building deeper relationships and ensuring long-term financial stability. Here are the key insights: 1. Diversify Revenue Streams: Embrace innovative approaches such as tiered memberships and NFTs to reduce reliance on declining ad revenues. 2. Enhance Audience Engagement: Offer exclusive, value-driven content that fosters deeper connections and builds community trust. 3. Future-Proof Your Business: Transitioning to hybrid revenue models is essential for long-term sustainability and resilience in digital publishing. The shift towards diversified revenue models not only strengthens financial performance but also cultivates a more engaged and loyal audience. Would your audience pay for exclusive content? Why or why not? Share with me in the comment section. #DigitalPublishing #SEO #RevenueDiversification #MembershipModels #MediaInnovation

Having founded two SaaS companies (Statista and ECDB), I've watched pricing models evolve from simple seat-based subscriptions to something far more nuanced. Today, I want to share my findings on what pricing model works for many SaaS companies, and what we’ve learned. Seat-based pricing means you buy x seats, use 70-80% actively, and everyone gets tool access. Simple. But the world has changed. Today, data flows through multiple channels, which means a seat does not reflect actual usage anymore. Data can now be accessed in various ways: 📈 Direct API integrations with BI tools 🤖 AI assistants answering ad-hoc questions 🖥️ Automated workflows pulling market data daily 🧑💻 MCPs (APIs for LLMs) enabling new use cases A single developer might automate queries for an entire organization. Ten analysts may share one dashboard but rarely log in. Why should they all pay the same? It doesn’t make sense. According to an OMR/hy study, usage-based pricing adoption in SaaS jumped from 31% to 67% in just two years. The reason? AI and automation are making per-seat models obsolete. When one employee can automate what previously required five, charging per seat doesn't reflect value delivered. The software’s true value comes from enhancing efficiency, output, or outcomes, not the headcount. That is why we at ECDB are moving to a hybrid model: platform access + consumption credits. 👇 Here's our approach: 1. Platform tiers remain - You still choose a plan based on team size and features needed. 2. Credits introduced - Each plan includes base credits for downloads and light API usage. Heavy automation requires add-on credit bundles with volume discounts. 3. Fair pricing across channels - Whether you access a data point via xls, API call, or AI query - same credit cost. No more arbitrary pricing based on how you access the data. We found that this model works best for us right now. I welcome feedback from our customers, other SaaS founders, and industry experts. Are you seeing similar shifts in your products?

The AI Paradox There are two big risks with AI in customer experience: Going all in on AI 👉 You get efficiency, speed, automation. 👉 But: you risk becoming boring, rational, predictable. Everyone uses the same tools, so standing out becomes almost impossible. If everyone succeeds in optimising, the result is a market of sameness — efficient, but average. Ignoring AI and focusing only on the human 👉 You may create warmth and emotional connection. 👉 But: you risk being too slow, inefficient, or impractical. Customers may walk away if you can’t match the convenience they expect in a digital-first world. This is the AI paradox: both extremes are dangerous. How to avoid both risks? The way forward is not choosing between AI or human, but building a hybrid model: Use AI for what it does best: automating the rational, removing friction, creating speed and convenience. Use humans for what only humans can do: adding emotional depth, creativity, storytelling, and empathy. The winners will be the brands that combine AI-driven efficiency with human-driven resonance. If you go all in on AI, you risk becoming average. If you ignore AI, you risk becoming irrelevant. The future belongs to those who master the mix: efficiently human.

Hybrid work is worth $10K to Boston finance candidates. And it's changing how offers work. Here's the reality: Senior Accountants take $8k-$12k less for hybrid roles. Hybrid postings get 3x more applicants. Full-time office roles take 50% longer to fill. 5-day office policies require 15-20% salary premiums. Last month, a Senior Accountant had two offers: $105k, fully on-site $95K, hybrid 3 days She took $95k. Started in 12 days. Her reason: "The $10k difference allows time back with my kids." This is the norm in Boston now. What hybrid means to candidates: -10+ hours/week saved commuting -$3k-$5k saved annually on transportation -Flexibility for family and appointments -Lower stress and burnout When you add it up, $10k less feels like break-even. Here's what your policy costs you: Fully remote: can pay 10-15% below market Hybrid (2-3 days): competitive at market rates 5 days on-site: need 10-20% above market + longer hiring time Common mistakes that drive me nuts: ❌ "Hybrid available after 90 days" (they want it day one) ❌ "Flexible schedule" (too vague) ❌ Not mentioning hybrid in posting (invisible to 60%) ❌ "With manager approval" (reads as "probably not") What I often see work: ✅ "Hybrid: In office Tuesday-Thursday" ✅ "Remote Mondays and Fridays" ✅ Put it in job title: "Senior Accountant (Hybrid)" Time-to-fill reality: Fully remote: 3-4 weeks Hybrid: 4-6 weeks Fully on-site: 8-12 weeks Same role. Same salary. Different policy. Huge difference in results. Hybrid isn't a perk in Boston anymore. It's standard. Candidates take pay cuts to get it. If you require full-time office, you'll pay more and wait longer. Factor hybrid value into your comp strategy. It's as real as the salary number.

In the Venture Capital executive education program at The Wharton School today, I focus on how to analyze #businessmodels of #startups. That used to be hard enough. Now #AI is making it much harder. Why? Because many AI companies do not fit the old boxes. They are not clean SaaS. They are not pure services. They sit somewhere between software, labor, and outcomes. And that creates real confusion for investors and operators: - Are you paying per seat? - Per token? - Per workflow completed? - Per outcome delivered? The big shift is from selling access to selling outcomes, while also dealing with real inference costs and much lower gross margins than classic SaaS. Hybrid models are emerging because the old pricing logic often breaks in AI. That is why evaluating AI business models is now one of the most important skills in venture investing. The winners will not just build better models. They will build business models that actually make economic sense. #AI #VentureCapital #BusinessModels #Pricing #Monetization