SaaS Business Models

I’ve been revisiting Microsoft’s end-to-end data platform reference architecture and it’s a great blueprint to think in terms of lifecycle: source → ingest → store → process → enrich → serve. 🌐 Data Sources Start with a diverse set of inputs. Structured databases, semi-structured files, unstructured sources and even streaming data feed into the platform. Whether data lives on-premises, in SaaS applications or in other clouds, the architecture supports bringing it all together. 📥 Ingest Data ingestion is flexible and powerful. Use mirroring to replicate relational databases in near real time. Use Data Factory pipelines, event streams or connectors to pull data from many sources into OneLake. Real-time event data can arrive via dedicated event streaming. 💾 Store OneLake serves as the unified data lake where all ingested data lives. Data can be stored in Delta Lake format and organized using a medallion architecture with bronze, silver and gold layers. High-volume event data can be placed into eventhouses optimized for analytics. 🛠 Process Processing happens where it makes sense. Datawarehouse and Spark notebooks let you transform, cleanse and aggregate data. Dataflow Gen2 helps shape and standardize datasets. Cross-engine queries let you combine mirrored databases, lakehouses and warehouses. 📊 Enrich This is where data is made more valuable. Use Generate AI-powered customer insights to enrich data and drive business value with Data Agents to have get insights from your data using Natural Language. 📈 Serve Finally, make the data available for consumption. Semantic models and SQL endpoints feed BI tools like Power BI for dashboards and reporting. Alerts and real-time insights can be configured so stakeholders always have current information. In practice this architecture brings raw data from everywhere into a governed, secure, highly usable platform that supports insights from batch and real-time workloads. If you’re thinking about building or modernizing your data estate this framework from Azure gives a solid foundation for every stage of the data lifecycle. Read more here: https://lnkd.in/eT4S-TFx #MicrosoftFabric #DataPlatform #DataEngineering #Analytics #ModernData #OneLake #Lakehouse #MedallionArchitecture #DataPipelines #BigData #AI #MachineLearning #PowerBI #CloudArchitecture

Notes from the field ✍ Agents are taking two very different paths into the enterprise: ▪️ Horizontal Agent Platforms: Sell the “What” These companies position as: “Build a fleet of agents” or “Your enterprise AI layer.” It’s a noun-first pitch. You are buying “agents” - powerful, general and, in the abstract, quite compelling. The problem? The buyer now has homework: define the use case, find budget, justify ROI. In other words, the hardest part of the sale gets outsourced to the customer. The usual response: - “Cool… what should we use it for?” - “Who owns this?” - “Which budget does this come from?” - “Can you help us design a use case?” Sales turn into co-creation and roadmaps resemble consulting. Most didn't set out to become systems integrators, but that is where gravity pulls them when the use case must be invented alongside the sale. These platforms are technically powerful but commercially blunt because they lead with capability (agents) instead of pain (a specific broken workflow). ▪️ Vertical Agents: Sell the “Why” They start with: “Reduce support cost per ticket” or “Resolve 60% of IT tickets autonomously.” Now the nouns are irrelevant. Call it an agent, a bot, or magic. What matters is that it attaches to an existing metric and budget. There is an incumbent to displace - no category creation required. Think Decagon in B2C support, Pylon in B2B support, Serval in ITSM. They’re selling outcomes, not AI. The vertical starting point may looks narrower. Increasingly, operators and CTOs are telling a different story: the fastest way to go broad is to start specific and earn your way out. Traditional vertical SaaS gets boxed in by its workflow. AI-native agents don’t, because the core asset is not the workflow but the layer that observes, orchestrates, and accumulates context across systems. Imagine: - A company launches a customer support agent - automating refunds, order changes, subscription issues. Soon they realize most issues are symptoms of pricing and billing friction. Embedded across CRM and billing, it starts triggering fixes, not just answering complaints. Support automation → control layer for customer experience and revenue leakage. - Another launches in IT - password resets, access requests, provisioning. Soon they realize most tickets stem from identity drift. Sitting across HR and IAM, it expands into security (privilege risk, audit) and finance (license optimization). IT automation → control layer for access entropy. Most enterprise workflows are artifacts of how software was purchased, not how work actually happens. You can have different tools across IT, Support, and Security all compensating for the same upstream limitation. Fix the root constraint and you’re not improving a workflow, you’re collapsing artificial boundaries between them. That’s the opportunity. Start vertical to get distribution, trust, and data. Expand horizontally by following the problem, not by declaring a platform.

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

Ever wondered why despite immense potential, some SaaS companies struggle to scale and achieve profitability? I recently went deep into a compelling discussion that shed light on the vital role of business metrics in SaaS growth. One anecdote stood out: the story of Salsify, a company that enhanced its trajectory by relocating its European headquarters to Lisbon, symbolizing a strategic shift in optimizing operations. The central theme was crystal clear: "If you can't measure it, you cannot improve it." Accurate metrics are not just numbers; they shape strategies, align teams, and spark growth. But what's the secret formula? Key takeaways include: - The Rule of 40: A SaaS company's growth rate and profitability combined should exceed 40%. - Net New ARR: Monitor bookings via net new Annual Recurring Revenue (ARR), encompassing new customer ARR, expansion ARR from existing customers, and losses from churned customers. - Sales Funnel Efficiency: Deploy a holistic funnel that includes onboarding, retention, and expansion. - Sales Team Metrics: Productivity per salesperson and timely hiring are crucial to meet growth targets. - Customer Economics: Balance the Customer Acquisition Cost (CAC) against the Lifetime Value (LTV). Aim for an LTV to CAC ratio of 3:1 and recover CAC within 12-18 months. - Negative Churn: Expansion revenue should ideally outpace revenue losses from churned customers for sustainable growth. Metrics like these can transform a SaaS company from merely surviving to thriving. It's fascinating how strategic measurement and adjustment can turn potential into proven success. How do you leverage metrics to steer your SaaS business towards growth and profitability? Share your experiences and insights! #SaaSMetrics #GrowthStrategy #BusinessAnalytics #SaaS #CustomerRetention #StartupGrowth #ScaleYourBusiness

I've been thinking about vertical SaaS lately. From 2018–21, only 24 % of 80 software IPOs were vertical SaaS. Why? Smaller customer pools and limited value capture kept the upside capped. AI changes the math. Instead of putting clipboards in the cloud, AI does the work itself—and that rewrites three fundamentals: 1. Value | Outputs, not clicks - Pre-AI apps sped up human workflows. - AI-native apps ship the deliverable—draft the brief, reconcile the invoice, triage the patient. When software does the work, it earns a bigger share of the value created. 2. Pricing | Usage, not seats - Seat licenses mapped to headcount. - AI teammates meter documents, calls, or tasks. 3. TAM | Core industry spend, not IT budget - Old ceilings: field-service software ≈ $5.5 B, restaurant POS ≈ $12 B, construction management ≈ $10 B. - New horizon: legal services alone top $1 T. When software augments the lawyer’s, nurse’s, or analyst’s job, it taps the services budget—not just the software line item. Takeaway: Bigger value → usage-based pricing → 100× larger markets. Bonus for founders: Many of these opportunities are untapped.

When cloud bills spike, the first reaction is predictable. “Let’s bring in FinOps.” “Let’s optimise workloads.” “Let’s negotiate better credits.” Useful steps but often misplaced. Because sustained cloud cost escalation is rarely a tooling problem. It’s a governance gap. In many enterprises, cloud adoption scaled faster than decision rights. Teams spin up environments. Data pipelines duplicate. AI experiments multiply. Storage grows quietly in the background. No one is individually reckless and collectively, the system lacks discipline. That’s not a FinOps issue. It’s an operating model ambiguity. Because, cloud spend reflects three deeper questions: 1. Who owns architectural standards? 2. Who approves data duplication? 3. Who links infrastructure usage to business outcomes? If those answers are unclear, cloud becomes a variable expense without accountability. And markets don’t reward variable opacity. I’ve seen organisations try to “optimise” after the fact by shutting down idle clusters, resizing compute, archiving cold storage. But optimisation without structural clarity is temporary relief. The real shift happens when cloud consumption is tied to: Business unit P&L, defined data ownership, lifecycle governance and ROI-based prioritisation. When cloud cost conversations move from “How do we reduce this bill?” to “Why does this workload exist?” maturity begins. If your cloud spend keeps rising unpredictably, the question isn’t: “Do we need better FinOps tooling?” It’s: “Do we have clear ownership of digital capital?” Because in today’s environment, cloud cost isn’t just an infrastructure line item. It’s a reflection of leadership discipline. #CloudComputing #FinOps #CloudGovernance #DigitalTransformation #TechnologyLeadership

show your website visitors what they get with your product (and what they’re missing out by not having your product in their tech stack). i visit saas websites for fun and add the cool ones to my swipe file. saas websites have a certain formula, which means when i see something different and cool.. it stands out. one website that caught my attention last week: Motion their product page has a brilliant "without [product] vs with [product]" comparison that does something a lot of b2b companies miss entirely. here's why this approach works: 𝟭/ 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘃𝗶𝘀𝗰𝗲𝗿𝗮𝗹 instead of generic pain points, motion lists specific daily frustrations: "procrastinate drafting difficult emails," "spend 2 hrs/day thinking about which tasks to prioritise." every executive reading this thinks "that's literally my tuesday." 𝟮/ 𝗾𝘂𝗮𝗻𝘁𝗶𝗳𝗶𝗲𝘀 𝘁𝗵𝗲 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 𝗰𝗼𝘀𝘁 when prospects see "2 hours/day thinking about task prioritisation," they don't just think about the time. they think about what else they could accomplish with those 2 hours. that's powerful positioning. 𝟯/ 𝘀𝗵𝗶𝗳𝘁𝘀 𝗳𝗼𝗰𝘂𝘀 𝗳𝗿𝗼𝗺 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝘁𝗼 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 motion isn't selling AI email drafting or calendar optimisation. they're selling the transformation from chaos to control. from reactive to proactive. most b2b websites lead with "our product does X, Y, Z." motion leads with "here's your life without us vs your life with us." motion takes it further by making the "without" section (hopefully) genuinely painful to read. you don't want to be the person "scrambling to get context before a back-to-back meeting." 𝘁𝗵𝗲 𝗹𝗲𝘀𝘀𝗼𝗻 𝗳𝗼𝗿 𝗺𝗮𝗿𝗸𝗲𝘁𝗲𝗿𝘀: stop selling your product's capabilities. start selling the life your prospects want to live. if you made it down here, think about the one daily frustration your product eliminates that you could make more visceral on your website…

3 B2B SaaS marketing strategies that actually work in 2025. After 20+ years advising startups, scale-ups and global brands, I've seen marketing fads come and go. But these three approaches are delivering meaningful results right now: 1. Micro-influencer partnerships (not celebrity endorsements) The opportunity is partnering with niche creators who have 10k - 250k followers in your specific target market. Why it works: You're borrowing trust, not just attention. Most businesses mess this up by making it purely transactional. The "secret" is alignment – the creator must actually value your product. 50% of Hubspot's media is creator-led now. Clay nailed this by giving creators tools, templates and premium support, not just commission. Don't believe the "free marketing" myth. You're either paying with money or time. 2. Proper demand gen (not lead gen) Lead gen is an outdated assembly line approach. Modern demand gen is about producing high-value content that builds real relationships. The data backs this up: • 4x higher conversion rate • 26% higher win rate • 36% shorter sales cycle Cognism executed this brilliantly by: • Ungating their premium content (counterintuitive but works) • Targeting specific accounts with hyper-personalised messaging • Leveraging dark social (Slack, WhatsApp) despite measurement challenges • Integrating marketing and sales teams to target key accounts It's not about building a massive list of unqualified leads that inflates your ego. It's about fewer, more engaged prospects. 3. Founder-led personal brand This isn't about posting selfies. It's about the CEO/founder consistently sharing specialised, high-value content. Gal and his team from Aligned generate most of their pipeline through content on LinkedIn. Personal profiles get 5x more reach than company pages on LinkedIn. Company reach is progressively dropping. People buy from people –  creating connection in ways corporate accounts never will. The businesses that thrive in 2025 won't be the ones with the biggest budgets. But those who build meaningful relationships at scale.

For decades, vertical SaaS in construction has focused on one thing: Digitizing the Status Quo. We took paper schedules and put them on a screen. We took physical folders and turned them into PDFs. We called it "digital transformation," but really, we were just creating high-tech storage units. The workflow enhancement, the connection of the physical and digital world with complex automation over the last few years that pushed for insights beyond just data, fueled the real digital transformation in the AECO market. AI changes the game because it moves from systems of record to systems of agency. In vertical SaaS—specifically for the AECO industry—AI doesn’t just store your data; it commands it. Here is why AI wins the vertical war: Deep Domain Moats: Horizontal AI (like ChatGPT) is a mile wide and an inch deep. Vertical AI built on proprietary datasets understands the nuance of a "change order" vs. an "RFI" and the legal/financial weight each carries. AI can't replace the why and the certainty that domain brings to the equation. Vertical AI captures institutional knowledge and turns it into a repeatable engine and advanced decision making but in the AECO world. The winners won't just sell software "seats"; they will sell project outcomes. Those outcomes can only be built of the train tracks of deep domain expertise and understanding of complex systems of systems that drive safety and reduce risk and go beyond project management or estimates..... #ConTech #ConstructionAI #VerticalSaaS #DigitalTransformation #AgenticAI

Peter Thiel just bet $220M on cow collars. Yes, you heard that right, cow collars. A New Zealand startup called Halter puts solar collars on cattle. The collar creates a virtual fence. Farmers move herds from their phone. This is not a boring AI pitch. Not a  "platform for everyone." Just one industry, one problem and nine years deep. Valuation: $2 billion. Cattle on collars: 1 million. Farms using it: 2,000. Here's the lesson for vertical SaaS founders. The real money is not in software for "all SMBs." It's in software for one industry, one workflow, one painful problem. Generalists fight on features. Specialists own the niche. If Thiel writes a check this big for cattle software, your vertical is not too small. It's the moat. So stop trying to sell to everyone. Pick the industry you understand. Go all in deep. The niche is the moat. P.S. Are you in vertical SaaS or Horizontal SaaS?