SaaS Business Models

We're moving away from charging for *access* to software and toward charging for the *work delivered* by software & AI agents. Don't freak out: this doesn't mean everything will become *pay-as-you-go* overnight. I can think of 7 flavors of charging for work: 1️⃣ Pay-as-you-go - No commitment, totally flexible - Enterprise procurement teams usually *hate* this! - Works best when your customers can bill-back the expense or bake it into an operating budget - Otherwise, there's a risk of customers policing their own usage (taximeter effect) 2️⃣ Subscription + pay-as-you-go - Small level of commitment helps 'lock customers in' and give them access to advanced features, support, etc. - Works well when the usage metric is getting commoditized (ex: SMS messages, compute, storage) -- you can advertise a low usage fee & make up for it with the subscription fee - Still not quite loved by enterprise procurement since their bill isn't predictable yet now includes multiple line items... 3️⃣ Three-part tariff (usage subscription + PAYG) - Similar to the above, but with a larger subscription fee that includes some level of usage "included" - Folks usually advertise the initial usage as a gift ("get your first 500 SMS messages for free!") - Including a minimum level of usage helps get the customer hooked & usually incentivizes more overall consumption 4️⃣ Usage-based subscription (high watermark) - Customers commit to a certain level of usage or tier (ex: up to 5,000 API calls per month); this is typically "use it or lose it" - Subscriptions are for a high watermark of usage -- if usage exceeds the plan in a given month, they immediate move into upgrade territory - Fear of overages + usage fluctuations encourages sales to over-sell & customers to over-buy 5️⃣ Usage-based subscription (annual drawdown) - Similar to the above, but the usage allocation can be consumed flexibly over the course of 12 months similar to a gift card - This gives the customer plenty of time to monitor adoption & plan for an early renewal/upgrade if usage is trending above their commit - Great for customers with seasonality or month-to-month usage fluctuations who still want a predictable bill 6️⃣ Roll-overs - If the customer doesn't consume their full allocation, they can "roll it over" to the next year -- typically only if they commit to a flat or increased renewal - More customer friendly, but also more painful to manage! 7️⃣ Adaptive flat rate - The customer commits to a usage-based subscription, but can use the product as much as they want with no overages/upgrades during that period - Their tier resets up/down at renewal based on their actual usage behavior - Much more predictable for customers while encouraging them to increase consumption (downside is that you could be stuck with the costs!) -- I suspect most folks will offer multiple options as they seek to balance lands, expands & tough procurement convos. The downside: complexity.

Lift and shift is the most expensive way to avoid real cloud transformation. Moving your mess to the cloud just gives you an expensive mess. At Mayfair IT, we have built cloud platforms using fundamentally different approaches. The difference in outcomes is dramatic. Lift and shift is seductive. Take existing servers, virtualise them, run them in Azure or AWS. Call it cloud migration. Declare victory. The infrastructure is now in the cloud. The problems are unchanged. Applications still assume they run on dedicated hardware. Scaling requires manual intervention. Failures cascade because nothing was designed for distributed failure. You pay cloud prices for on premises architecture. What cloud native actually means, We have built greenfield platforms on Azure designed from the beginning for cloud. Platform as a Service and Software as a Service components doing what they do best. Azure Data Factory orchestrating data pipelines instead of custom ETL running on virtual machines. Cosmos DB providing distributed databases instead of clustered SQL servers. Serverless functions handling event driven workloads instead of always on application servers. The difference is economic and operational. What changes with cloud native architecture: → Scaling happens automatically based on demand, not manual capacity planning → Failures in individual components do not bring down entire services → You pay only for resources actually used, not capacity provisioned for peak load → Updates deploy without downtime because architecture assumes continuous change We have also migrated legacy systems to cloud where complete refactoring was not feasible. The challenge is knowing which approach fits which situation. Greenfield builds should always be cloud native.  Legacy migrations require honest assessment of whether lift and shift provides enough value to justify the effort. Sometimes the answer is yes.  Moving a stable system with known workloads to cloud can reduce operational overhead even without refactoring. But presenting lift and shift as cloud transformation is dishonest.  You moved the location. You did not change the architecture. The organisations getting real cloud value are the ones willing to rebuild applications to use cloud capabilities properly. How much of your cloud spending is on virtualised servers that could be replaced by managed services? #CloudNative #Azure #DigitalTransformation

AI agents should never receive unrestricted access just because they can complete a task. The more tools, systems, and data an agent can reach, the more carefully its permissions must be designed. These five access control models provide different ways to keep agent actions scoped, secure, and auditable: → 𝗥𝗼𝗹𝗲-𝗕𝗮𝘀𝗲𝗱 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 Permissions are assigned through predefined roles. It works well when responsibilities are stable and agents can be mapped to roles such as support agent, finance agent, or administrator. → 𝗔𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗲-𝗕𝗮𝘀𝗲𝗱 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 Access decisions use attributes such as agent identity, resource type, requested action, location, time, risk, and business context. This enables more precise and dynamic policies. → 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗟𝗶𝘀𝘁𝘀 Each resource maintains a list of agents or groups allowed to access it and the actions they may perform. This provides direct resource-level control but can become difficult to manage at scale. → 𝗠𝗮𝗻𝗱𝗮𝘁𝗼𝗿𝘆 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 Central authorities assign security labels to agents and resources. Strict policies determine access, and individual users or agents cannot override them. → 𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆-𝗕𝗮𝘀𝗲𝗱 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 Agents receive scoped tokens that authorize a specific action, resource, limit, or time period. This avoids granting broad standing permissions and works well for temporary, task-specific execution. No single access control model fits every agent workflow. Role-based control provides simplicity. Attribute-based control adds context. ACLs offer direct resource permissions. Mandatory control enforces strict policy. Capability-based control provides narrow, temporary authority. Which access control model best fits the AI agents operating inside your enterprise?

🔵 Stripe just paid $1 billion for something it could have built. That tells you everything about the complexity and urgency of usage-based billing in the AI era. The biggest shift in software monetization since SaaS is happening. Patrick Collison isn't mincing words: 𝐮𝐬𝐚𝐠𝐞-𝐛𝐚𝐬𝐞𝐝 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐢𝐬 "𝐭𝐡𝐞 𝐧𝐚𝐭𝐢𝐯𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐦𝐨𝐝𝐞𝐥 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐀𝐈 𝐞𝐫𝐚," potentially as big as (or bigger than) the advent of SaaS itself. UBB - Usage Based Billing Payment processing is one layer; monetization logic is another. Stripe is focused now on both. 🔷 𝐓𝐡𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 Metronome's valuation doubled in less than a year (from $470M in February to $1B now), with 8x growth in platform volume during 2024. Their client roster speaks volumes: OpenAI, Anthropic, Databricks, Nvidia—companies where consumption-based pricing isn't optional, it's essential. The shift makes sense. AI value correlates directly with consumption: API calls, compute time, tokens processed. Traditional seat-based subscriptions simply don't capture how customers actually derive value. 🔷 𝐁𝐞𝐲𝐨𝐧𝐝 𝐌𝐞𝐭𝐫𝐨𝐧𝐨𝐦𝐞: 𝐓𝐡𝐞 𝐔𝐬𝐚𝐠𝐞-𝐁𝐚𝐬𝐞𝐝 𝐁𝐢𝐥𝐥𝐢𝐧𝐠 𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦 This isn't a one-company phenomenon. This market has exploded with specialized players, each carving out territory. Here are four infrastructure players comparable to Metronome: ‣ Orb – Usage-based billing and pricing infra with strong adoption among modern SaaS and AI companies; Metronome itself positions Orb as its primary direct comparator. ‣ m3ter – Purpose-built usage metering and rating engine for complex B2B SaaS and hybrid models, often grouped with Metronome and Amberflo as the core UBB infra cohort. ‣ Amberflo.ai – Developer-first consumption billing that focuses on metering at scale and “AWS-style” usage pricing; regularly listed alongside Metronome and m3ter as leading UBB startups. ‣ Lago – Open‑source usage-based billing and metering, explicitly branded as a Metronome alternative and highlighted as the strongest choice when teams want control and self-hosting. Stripe chose to acquire rather than build. That signals how complex and critical this capability has become. 🔷 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 Usage-based pricing aligns revenue with value delivery in ways subscriptions never could. It's more transparent for customers, more scalable for providers, and infinitely more adaptable to hybrid models. For financial services and fintech, this is an infrastructure-level transformation. We're not just talking about billing—we're talking about how companies capture value in real-time, optimize pricing dynamically, and build monetization as a competitive advantage. The question isn't whether to consider usage-based models. It's how quickly you can implement them before your competitors do. #Fintech #AI #monetization

🚨 Big M&A! Stripe acquired Metronome to win at AI-usage-based billing A leader in usage-based billing. Customers include OpenAI& Nvidia. Here's why this matters: - AI products break traditional billing systems. - SaaS is predictable: $50/user/month. - AI is chaos: you're charging per token, per inference, per compute minute. Usage spikes 10x overnight when a customer's product goes viral. Most billing infrastructure collapses under that load. --- Metronome built theirs to handle it. That's why OpenAI uses them. They have sophisticated "budget" based billing (set an amount and use tokens within that budget). Most Metronome customers were / are Stripe customers, so there's a natural overlap here. --- The strategic rationale works two ways. For Metronome There's only so much upside in being pure-play billing. Owning the payment — the actual unit of value — unlocks every product extension over time. For Stripe They get that killer feature that had been hurting in the AI space and can massively expand their ARPU. --- Stripe gets proven AI billing infrastructure. Metronome gets distribution to millions of businesses. Smart deal on both sides.

Hot take: people don’t actually want predictability… they want transparency. I keep hearing: “Usage-based pricing is unpredictable and buyers don’t like that” Predictability is: “Tell me my bill to the dollar a year in .” Transparency is: “Show me what’s driving it, and give me knobs.” Because we already live in a (mostly) usage world: Electricity / water: I don’t predict kWh. I just know my holiday guests took extra long extra hot showers. AWS: the whole point is you pay for what you use (compute/storage/queries), and you survive because they give you dashboards, alerts, budgets, and “hey… this spike looks suspicious.” or this S3 bucket hasn't been used in ages. Snowflake, Clay, Figma, OpenAI, Loveable, … and many more So the fix isn’t “go back to seats.” Also: seats are not coming back. Buyers ask for seats because it feels simpler—until layoffs hit, usage drops, and suddenly your “predictable” revenue is predictably… down.  Plus procurement will grind your price per seat every renewal with the classic: “We’ll open an RFP.” Congrats: you’re now funding rising LLM/inference costs by giving your innovation away for free. 🫠 The real answer is making usage feel fair: ✅ Visibility Tell customers exactly how they are using those credits. Contacts? Emails sent? Scanning inboxes? Calls? Doing research? ✅ Controls Let your customers set caps, alerts, limits - and let them know when they are about to hit them, who did it, and why! ✅ Forecasting “if you keep doing this…” and “here is a plan that will save you money …” ✅ Economizing that’s actually a win-win: “Hey, you’re trending high—upgrade to a bigger package and your unit cost drops.” Customer pays less per unit, vendor earns more, everyone stops doom-refreshing invoices. AI Agents are turning software into a usage-based economy - whether it’s outcomes or resource consumption, that economy only works if we put customers at the center: clarity, control, and trust—while staying profitable. Usage isn’t the problem. Opacity is.

Welcome to this edition of the Fintech Wrap Up Newsletter! In today’s deep dive—crafted with insights from Activant Capital—we explore why usage-based billing (UBB) is emerging as one of the most impactful pricing models in modern software and services. UBB isn’t exactly new—it’s how we’ve long paid for utilities, rides, and phone bills. But in the era of SaaS, AI, and automation, it's making a powerful comeback. As software eats the world and machine-to-machine consumption surges, the traditional subscription model is losing ground. A recent report shows that 3 out of 5 SaaS companies already use some form of UBB, and that figure is expected to rise to 80% in the next few years. With SaaS growing at 7.3% CAGR and AI accelerating usage complexity, pricing is no longer a static lever—it’s a data challenge demanding flexibility and customer-centricity. The advantages are compelling: better net revenue retention (up to 9% higher than subscription-only peers), reduced churn, expanded total addressable markets, and higher customer satisfaction. But UBB isn’t without pitfalls—monitoring usage metrics accurately, managing billing systems, and ensuring revenue predictability remain key hurdles. That’s why many companies are now embracing hybrid pricing models, combining UBB with subscriptions to align value with customer behavior throughout the user journey. Underpinning this shift is a growing ecosystem of pricing infrastructure—from legacy ERP integrations to nimble UBB platforms like Metronome, M3ter, and Amberflo. Industry giants like Stripe and Zuora have jumped in through acquisitions, signaling confidence in UBB’s strategic value. And with pricing increasingly seen as a tech and data play, companies that nail dynamic, transparent, and usage-aware models will be the ones that thrive. In short, usage-based billing is no longer optional—it’s a strategic imperative. And whether you’re a SaaS founder, pricing strategist, or investor, now is the time to pay attention to how usage is reshaping value. #fintech #payments #billing

The Forecast Loop: Why Your Numbers Never Match Reality 🧪 Ever notice how your forecasts miss the mark? You're not alone. Often times when I'm building forecasts for a fast-growing SaaS company, we'll spend weeks building models, only to watch it become irrelevant and stale after just a few months. The solution? Stop treating forecasting as a one-time project and start seeing it as an ongoing cycle of testing and improvement. ➡️ EXPERIMENT This is where the cycle begins. This requires structured testing, not random assumptions: Take a financial assumption and isolate it Change one pricing strategy at a time Adjust a specific operational factor The key is controlling your variables. When testing a price increase, don't simultaneously change your sales commission structure. Keep it clean! ➡️ MEASURE Now comes measurement. This means thorough tracking, well beyond a quarterly P&L review. I'm talking about tracking BOTH financial AND operational results: Revenue impact? Obviously. Customer acquisition cost changes? Critical. Renewal rates affected? You bet. Most companies fall short here - they watch revenue but miss the operational indicators that explain WHY the numbers changed. ➡️ LEARN Learning is comparing what you thought would happen with what actually happened. Launching a new product line? Trying a new acquisition channel? Landing a new partnership? These all involve assumption that require validation. But don't just note the difference - understand why it happened. Was your conversion rate overstated? Did it take longer to ramp up that partner? ➡️ UPDATE FORECAST Finally, update your forecast based on what you've learned. Most companies get this backward - they tweak forecasts to match historical results without updating the underlying assumptions. Instead: Adjust the actual input variables Refine how your model weighs different factors Document what you've learned so forecasts get smarter each cycle === The forecast loop focuses on continuous improvement rather than immediate perfection. What's the biggest gap you've seen between forecast and reality? How did you learn from it? Comment below 👇

They left GCP for AWS. The result: 25% lower infra cost and 50% less time on ops. Our client runs AI/ML products. GPU cost grew faster than user growth. They had to act. They had already decided to move from GCP to AWS. We used that move to redesign the platform for the next stage: scale GPU workloads, prepare for LLMs, and keep cost in check. We focused on four parts. 1) Smooth migration - We did a mix of lift-and-shift and targeted changes. - Core apps moved first. - Risky parts got extra care. - No big-bang rewrite. - No long downtime. 2) AI/ML on Amazon EKS + GPU EC2 - We built an AI platform on EKS. - GPU-enabled EC2 nodes run models. - Autoscaling reacts to load. - GPU nodes spin up for peaks and sleep when idle. 3) Data layer on Aurora PostgreSQL + S3 - We moved key data to Aurora PostgreSQL. - Cold data lives on S3. - Query speed improved. - Storage cost stays under control. 4) Hybrid GPU strategy - We mixed Spot and On-Demand GPU instances. - Spot lowers cost. - On-Demand keeps reliability. - The system chooses the right mix in real time. The impact:    • 25% lower infrastructure costs   • 40% faster data retrieval   • 30% faster model start time   • 2× faster GPU scaling at peak   • 50% less time on infrastructure managemen Now the customer has a secure, scalable base ready for GenAI and LLM growth, instead of fighting their GPU bill every month. Scaling GenAI is hard, doing it cost-effectively is harder. If that’s your focus, let’s talk. #CloudMigration #AWSforAI #MLOps #EKS

In my previous roles at two high-growth startups—and through working with multiple early-stage teams as an advisor—I kept running into the same problem: As you scale, you’re constantly trying to do 3 things: 1️⃣ 𝐒𝐡𝐢𝐩 𝐟𝐚𝐬𝐭 2️⃣ 𝐆𝐫𝐨𝐰 𝐀𝐑𝐑 3️⃣ 𝐒𝐭𝐚𝐲 𝐜𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐭 Most teams can do 1 and 2. But 3? That’s where momentum breaks. Compliance slows down sales, burns engineering hours, and becomes a blocker instead of an enabler. That frustration led me to build Zerberus—and focus on automating 3 critical pillars: 🔐 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 To embed proactive security into your SDLC—before dependencies or packages turn into threats. ⚙️ 𝐀 𝐠𝐥𝐨𝐛𝐚𝐥 𝐜𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 (𝐩𝐚𝐭𝐞𝐧𝐭-𝐩𝐞𝐧𝐝𝐢𝐧𝐠) To map any control across ISO 27001, NIS2, DORA, GDPR, Cyber Essentials, or the EU AI Act—with zero duplication. ⚡ 𝐉𝐮𝐬𝐭-𝐢𝐧-𝐭𝐢𝐦𝐞 𝐫𝐞𝐦𝐞𝐝𝐢𝐚𝐭𝐢𝐨𝐧 (𝐩𝐚𝐭𝐞𝐧𝐭-𝐩𝐞𝐧𝐝𝐢𝐧𝐠) To fix non-conformities in hours, not days. No more audit fire drills. No more “where’s the evidence” chaos. The result? 🚀 Your team and systems can get audit-ready in 10–20 days 📉 MTTR drops from weeks to hours ✅ Compliance becomes continuous, repeatable, and revenue-enabling We just published a post outlining how startups and SMBs can scale security maturity the smart way—from Cyber Essentials+, to ISO 27001, all the way to NIS2, DORA, and the EU AI Act. 🧭 If you’re building in the #UK or #EU, this will save your team a lot of heartburn. 👉 https://lnkd.in/eUUqAzbp #Startups #SaaS #ISO27001 #NIS2 #CyberSecurity #Compliance #Zerberus #ARR #UKTech #EUTech Zerberus.ai Felix Aravintharaj G