SaaS Business Models

The shift from seats to agents pressures SaaS margins. At the same time, the longstanding practice of getting enterprise customers to pre-commit and also prepay for functionality they may never deploy will get harder as CIOs look to free budget for their own LLM costs. To weather the storm, some SaaS companies have increased prices. This boosts revenue and margins in the short-term but can't be done repeatedly and creates even greater scrutiny over shelfware as procurement teams right-size and shift contracts to "pay as you go." To achieve sustainable growth, SaaS companies need to become hyperefficient at sales and marketing. Here are common ways to do so and who's doing it well: 1. PLG. Shopify and Atlassian exemplify efficient go-to-market based on product-led growth with free trials, low-friction upgrades and upsells. Their sales teams only need to get involved in the biggest opportunities at the largest accounts; every other step in acquisition, commercial transaction, activation, onboarding, and growth is self-service and automated. 2. Vertical SaaS. Guidewire Software and Veeva Systems are laser-focused on insurance and life sciences, respectively. Rather than casting a wide net, they spear-fish with deep domain knowledge and purpose-built solutions for that industry's specific workflows and regulatory requirements. Guidewire doesn't need to buy Super Bowl ads– their annual customer conference is the Super Bowl for property & casualty insurance executives. Nearly zero GTM effort is wasted– unsurprisingly they're the two most efficient on the list. We modeled Hearsay Systems after both these companies, and this focus allowed us to win incredible market share among Fortune 500 banks & insurers despite only raising $60M in totality. 3. Relocate operations to lower-cost regions and AI. This is private equity's favorite playbook to take costs out of companies they buy. Field sales continues to shift more to Zoom, which means you can hire AEs anywhere. Inside sales contributes a greater % of revenue as PLG motions are established. AI handles top-of-funnel leads qualification and generating marketing content and campaigns. 4. Focus on gross revenue retention. Because of high customer acquisition costs in #SaaS, leaky buckets are margin killers. Use LLMs to help customer success teams analyze product usage, segment cohorts, and identify opportunities to increase value realization. Put in guardrails to prevent sales reps from overselling an account, as doing so only creates churn in the next renewal cycle. 5. Introduce another product line. This only works if your new product has the same buyer as your existing products. Many SaaS acquisition pro formas fail to actualize for this reason, as it's not actually feasible to have the same AE sell both old and new products. Every SaaS company right now needs to double down on one or more of these levers in the AI era.

Sorry to break an uncomfortable truth to SaaS founders Your fancy new AI features? They're eating your margins for breakfast. And lunch. And dinner. The math is brutal but simple: In the good old days of traditional SaaS, for every $100 your customer paid, you kept $80. Your costs were predictable - some servers here, a support team there. Life was good! Enter 2025 and the AI revolution. Now for every $100, you're lucky to keep $65. Why? Because every time your customer uses your shiny new AI feature, ka-ching! The meter's running. And those GPT-4 tokens? They add up faster than a Tesla's battery drains in winter. "But wait!" you say, "We'll just pass the costs to our customers!" Sorry, friend. Your customers aren't stupid. They can do math too. Here's what's actually happening: - Your competitors are absorbing the AI costs to stay competitive - Your customers are expecting AI features as standard - Your margins are getting squeezed from both ends - And your investors are still expecting that magical 80% number 2025 is the year SaaS companies face a choice: Accept that the 80% margin party is over, or completely reinvent how they charge for value. Want to know who's going to win? It won't be the companies with the fanciest AI. It won't be the ones with the lowest prices. It'll be the ones who figure out how to charge for outcomes instead of subscriptions. The future belongs to those who can answer one simple question: What are your customers actually paying for - your software, or the results it delivers? Time to get creative, folks. The margin party's over and the innovation game is just beginning. 🎯 Who's ready for this conversation?

"Service reliability math that every engineer should know" I think it's useful for engineers to understand what uptime and reliability mean in practice. These numbers paint a good picture of what's involved :) Now while service reliability is often reduced to a simple percentage, the reality is far more nuanced than those decimal points suggest. First, not all downtime is created equal. A single 8-hour outage has dramatically different business implications than 480 one-minute outages, even though both sum to the same annual downtime. This distinction is particularly relevant when considering service level agreements (SLAs) and how they’re measured. The impact of downtime also varies significantly based on when it occurs. Five minutes of downtime during peak business hours might cost more than an hour of downtime during off-hours. This temporal aspect of reliability is often overlooked in simple percentage calculations. Each additional nine of reliability typically requires an order of magnitude more engineering effort and operational complexity. Moving from 99.9% to 99.99% isn’t just a matter of being "10 times more reliable" – it often requires fundamental architectural changes: At 99.9% (8h 45m downtime/year), you might get away with single-region deployment and basic failover At 99.99% (52m 35s), you’re typically looking at multi-region deployment, sophisticated health checking, and automated failover At 99.999% (5m 15s), you need redundancy at every layer, real-time monitoring, and likely some form of active-active deployment At 99.9999% (31s), you’re dealing with advanced techniques like chaos engineering, automated canary deployments, and sophisticated traffic management While understanding the basic math of service reliability is crucial, the real engineering challenge lies in understanding the context, trade-offs, and business implications of reliability decisions. The next time you see a reliability requirement, don’t just think about the percentage – think about the entire socio-technical system required to achieve and maintain that level of service. The numbers are simple. The engineering reality behind them is anything but. #softwareengineering #programming

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

Salesforce just fired the starting gun on a seismic shift in how we pay for software. At Salesforce #Agentforce, they announced they’re moving away from the traditional per-seat SaaS model to a consumption-based pricing for their AI agents. This is huge. Why? Because it signals the end of paying just to have access to technology. Instead, we’re moving toward paying for outcomes—the actual value delivered. Think about it. In a world where AI agents can perform the job functions of entire departments, does it make sense to charge per seat? Probably not. Here’s what’s changing: - From access to outcomes: Companies will pay for what the AI actually accomplishes. - From subscriptions to value: Pricing adjusts based on usage and results. - From Software-as-a-Service to Agent-as-a-Service: Technology that collaborates with you as a partner This isn’t just a tweak in pricing—it’s a radical upending of commercial models for large SaaS companies. What does this mean for businesses? - Budgeting will evolve: Costs align directly with value received. - ROI becomes clearer: Easier to measure the direct impact of technology investments. - Greater flexibility: Scale usage up or down based on needs without worrying about seat counts. It’s an exciting time, but also a challenging one. Is every SaaS company ready to embrace a model where companies pay directly for the value they receive? At Uniti AI, we’ve been thinking along these lines. We price our AI agents based on the amount of work they do, not on how many seats a company has. I believe this is the future. What do you think? Is the per-seat model on its way out?

HR is hitting a structural wall. The traditional model was built for a different era with different constraints. Today, HR teams are under significant pressure to optimize, enable enterprise-wide agility, and drive AI integration. Add in fragmented legacy systems, rigid hierarchies, and the old model simply cannot scale. But, we don't just need another layer of HR tech. We need a completely new operating system. The infographic presents Wowledge's blueprint, along with other leading voices we have seen publishing comprehensive models. There is something we all agree on: the future of HR is fluid, deeply integrated with AI, and built around human-machine teaming. While shifting away from static service centers toward agile, outcome-focused teams, how we propose structuring that shift varies significantly. Here is a look at how these four frameworks propose to reinvent the function, with links to each in the comments: The Human Readiness Operating System [Wowledge] Embraces a radically lean internal footprint via an "access vs. own" approach. It relies heavily on a curated external ecosystem for specialized expertise, enabling robust execution accountability directly by business leaders, aided through AI tooling. A New Operating Model for People Management [McKinsey] Fundamentally elevates IT and data capabilities directly within HR. It introduces "People Technologists" as a core structural pillar, aiming to replace traditional shared services with hyper-personalized "digital twins" or personal agents for employees. HR Reimagined [Deloitte] Treats AI as a literal component of the workforce. By emphasizing "Agentic AI" to execute complex, multi-step workflows, it advocates for HR to formally govern digital agents as part of the workforce strategy, freeing up to 25% of human capacity. Operating by Design [Mercer] Abandons process-based departments in favor of a structural fusion of HR, IT, and Finance by pulling budgets down to cross-functional "Outcome Delivery Teams," ensuring financial accountability is tied directly to operational demand. The model you choose depends heavily on your organization's scale, maturity, and specific friction points. But one thing is clear: as HR leaders, we must lead this transformation before the business begins driving it for us. ~ Click Carlos Larracilla and follow me [+🔔] for daily resources from Wowledge.

Consulting firms are no longer just competing with each other. They are competing with SaaS. Clients aren’t paying for advice anymore. They expect something tangible left behind: a tool, a platform, an algorithm they can use the day after Consultants leave the building. Big-ticket, multi-year strategy programs are becoming less frequent. The growth is in lower ticket, higher volume work that can be packaged, priced, and delivered at high margin. The shift is clear. PwC has embedded managed services across all eight advisory divisions, effectively creating subscription-style Consulting. Deloitte now mandates AI in every significant engagement, ensuring each project carries a productized layer. Even boutiques like Lovelytics are winning share with Databricks-enabled “AI in a box” offerings. In every case, delivery is being turned into data, data into IP, and IP into recurring revenue. The economics are obvious. Consulting has always been episodic and people heavy, while SaaS thrives on recurring, predictable revenue. Firms are rushing to close that gap by productizing insight, packaging knowledge into assets that scale. Clients want results they can see, measure, and keep using. They want speed, repeatability, and ROI baked into every engagement. The question is whether this makes Consulting better, or simply more commoditized. The firms that will win are not the ones that look most like SaaS. They will be the ones that find the right blend: lean teams, embedded industry specific AI, tangible tools, and the judgment to solve problems that no platform can. Consulting is being reshaped into a product business. The challenge is to avoid losing its value in the process.

$7,225 for one day of coding. And Cursor isn't even the worst example. Replit's margins went negative. Anthropic throttles its best users. I mapped pricing across 50 AI startups. Six distinct patterns emerged. The core tension: traditional SaaS has near-zero marginal cost per user. AI products pay for compute on every interaction. A casual Claude user costs pennies. A developer running Claude Code all day costs tens of thousands per month. Your best users are your most expensive users. That tension is breaking every pricing model in the market. Cursor charged a flat 500 requests/month. Worked fine until users leaned into multi-step agent workflows. They switched to credit pools. One developer burned 500 requests in a single day. The plan description changed from "Unlimited" to "Extended" twelve days after launch. Replit grew 15x in ten months ($16M to $252M ARR). But they were buying revenue with compute. When they launched a more autonomous agent, margins crashed to negative 14%. They had to invent "effort-based pricing" mid-flight. Anthropic played it differently. Their $17/$100/$200 tiers map to genuinely different user personas, not volume bands. A casual user and a Claude Code developer are different products with different willingness to pay. The lesson across all 50 companies: before you set any price, pull the cost distribution. What does your P10 user cost? P50? P90? If the ratio exceeds 10x, flat pricing will break. In AI products, it almost always exceeds 10x. Full guide with all 6 models, 4 case studies, and a decision tree: https://lnkd.in/gdKaQSMk

Building a product strategy that bridges vision with reality is one of the hardest parts of leading a product team. You’re balancing immediate priorities while keeping everyone aligned with long-term goals. That’s where the 'Product Strategy Layers' framework comes in, it provides a step-by-step guide to making strategic decisions that move your product forward. Here’s how it breaks down: 1. Company Vision This is your 5-10 year north star. It’s more than just a lofty idea—it sets the direction for everything your organization does, from product investments to team priorities. If the vision isn’t clear, it’s easy for teams to lose focus and drift. 2. Strategic Intents These are your 1-3 year business challenges that push the organization closer to that vision. They focus your team on specific areas and bridge the gap between high-level goals and everyday execution, making sure efforts are aligned and deliberate. 3. Product Portfolio Strategy This layer is all about identifying the key problems you need to solve across your product lines to meet those strategic intents. It’s where you decide where to invest resources for the greatest impact and how to align multiple products around common goals. 4. Product Strategy & Initiatives Now we zoom in on individual products. At this level, you’re planning for the next 6-12 months, defining specific initiatives that drive your product forward. The goal is to make sure every action contributes directly to your product portfolio strategy. 5. Options The most granular layer. Here, you explore different solutions, run experiments, and iterate. It’s highly flexible but remains rooted in the broader strategy, allowing your team to adapt quickly without losing sight of the overall vision. Using this layered framework ensures every decision you make is connected to the BIGGER picture. The 'Product Strategy Layers' structure helps you align your team, streamline decision-making, and consistently drive toward long-term success. #productoperations #productmanagement #productstrategy #strategicplanning

Sam Altman: “ending the fast fashion era of SaaS” Translation: You'll build your expense tracker. You won't build the next Stripe. With GPT-5, you'll build a wedding planning app in 2 hours. But you won't replace Salesforce in a weekend. Yes, you can build mini apps fast: - Internal tools? Done in hours - Simple calculators? Before lunch - Basic CRMs? Same day - Wedding registry? Weekend project But complex SaaS? Different story: My friend tried rebuilding QuickBooks internally with AI tools. Failed. Not because of coding—because of one data connectors. The real blockers aren't code: → Data access & API connectors → Understanding product requirements → User feedback loops → Compliance & security → Scaling infrastructure → Customer support systems The truth about AI-powered app creation: Mini app for your wedding guest list? ✅ Mid-size B2B SaaS with 50+ integrations? ❌ Internal dashboard for sales metrics? ✅ Full accounting platform? ❌ What changes with GPT-5: Prototype to MVP: 10x faster Simple apps: Everyone can build Complex systems: Still need architects What doesn't change: - Business logic complexity - Integration nightmares - Data governance requirements - Enterprise sales cycles The real opportunity? Get better at growth, content and selling! Because whether you do small apps or complex software, competition is coming for you!