SaaS Business Models

SaaS isn’t slowing down. It’s getting eaten alive ... cannibalized. Look at the Aventis Advisors growth chart. The trend isn’t subtle, it’s a cliff. We went from 36 percent growth to 12 percent. Almost a decade of down and to the right. With forecasts pointing to 11 percent … and falling, this feels like a pretty big structural shift. SaaS is starting to look like utilities and pipelines, durable and necessary, but no longer where the real upside lives. And the reason is pretty simple. AI is cannibalizing the very work SaaS used to monetize. Here’s what the chart doesn’t show, but every operator feels. 1) SaaS used to sell “workflows.” AI sells “outcomes” (OaaS). Agents do the work inside the tool, so the tool stops being the product. The labor becomes the product. 2) Budgets (and investors) are leaving SaaS and flowing to digital labor. CFOs aren’t buying more seats. They’re buying fewer humans. AI fits. SaaS doesn’t. 3) Feature parity killed differentiation. Entire categories are indistinguishable. CRM, CX, marketing automation … all the same. AI exposes how thin the moats always were. 4) Enterprises hit peak-SaaS years ago. Now they’re consolidating and cutting 20 to 40 percent of their stack. AI accelerates that purge. 5) AI startups are growing at speeds SaaS can’t touch. When companies hit nine figures in months, not years, investor expectations reset. SaaS looks slow, expensive, and operationally bloated. 6) Value is moving down the stack. The action is in compute, data, agents, and orchestration. SaaS is becoming a UI layer that AI sits on, not the engine driving the work. The growth-rate collapse isn’t a mystery, it’s more of a transfer of value. SaaS is maturing into a stable, cash-flow asset class. AI is becoming the new growth engine of the enterprise. That means founders have a choice, build SaaS and optimize it like infrastructure, or build AI agents that replace the workflows SaaS was built to capture. One path gives you stable multiples, the other gives you growth.

The question I hear most from founders during Sequoia Capital's Arc program is about #pricing. Pricing is one of the most underutilized levers for startups. Why does it matter so much? It has the most direct impact on revenue, and the moment you establish your pricing, you determine your TAM. Getting the pricing metric right is, by far, the most important one. The key is to imagine the future: when you are a large and successful company, how have you changed the world, and what metric correlates best with your success? Hitch your financial wagon to that metric! If you are Figma, success is all designers using the app; therefore, the pricing metrics is per designer seat. If you are VMware, success is all workloads run in virtual machines; therefore, the right pricing metric would have been a virtual machine. A pricing metric is like the genie in a bottle: once you get it out, it is tough to rein it back or change it. The pricing model is about when and how frequently you charge. Recurrent subscriptions are the predominant model for SaaS apps, and usage-based pricing is the model for infrastructure solutions. Usage-based pricing creates a beautiful alignment of incentives but is less predictable. Upfront credit purchases and commitments are efforts to make usage-based practice more aligned with the rigid corporate budgeting processes. You can be the premium solution or the affordable one. Both are legitimate approaches. But your pricing needs to be consistent with the rest of your strategy: with your product and distribution channels.  You can’t have an affordable solution distributed through an expensive enterprise sales force. In this case, you need to sell either online or through inside sales—the product better be simple and the sales cycle quick. Many technical founders are shy about asking for a lot of money for their product. Don’t be. If customers like the product and it delivers value, they will gladly pay for it. Unless you hear customer complaints that you are expensive, then for sure you are underpricing. Calculate the ROI of your product, and take 20% of that value as your price point. How much it costs you to build the solution should not guide your pricing. But you should do a sanity check that you have a decent gross margin. Most companies start by selling a single package. Over time, they realize that different customer segments have different maturity levels and willingness to pay. To price discriminate between these segments, you need to introduce multiple packages.  Start by creating a customer maturity curve to inform your decisions on how many packages you need. The trick is to have the smallest number of packages to cover the broadest range of customer needs. Your packages will change and evolve quickly as your product matures. 

Today, B2B SaaS products perform impressively in isolation, providing functionality, efficiency and productivity gains. But they don’t play well with others. Vendors know they need to offer a wide set of native integrations, but that’s getting harder to achieve. As the B2B tech stack swells (the average business uses 371 SaaS apps), the number of integrations vendors need to build is skyrocketing. In the coming decade, this problem will increase even further as B2B software will operate across thousands of highly specialized applications. These systems won’t just coexist, they’ll need to interoperate in real time, across dynamic, evolving workflows. Current SaaS architectures struggle with integration complexity. Fragmented stacks, ad hoc APIs, and manual workarounds introduce bottlenecks at scale. To fully unlock the value of SaaS, vendors require infrastructure that abstracts the burden of bespoke integration development. Legacy solutions fall short: Embedded iPaaS enables point-to-point connectivity but lacks scalability and maintainability. Unified APIs offer abstraction, but constrain customization and depth of integration due to rigid schemas. What’s needed is a universal, API-agnostic integration layer, one that enables composable, reusable logic across heterogeneous systems at scale with hundreds of apps. At Integration App, we’re building exactly that. Our platform introduces a standardized integration framework that decouples integration logic from underlying APIs. Using AI, we generate adaptive, app- and tenant-specific implementations, allowing developers to build complex, multi-surface integrations with minimal overhead. This architecture dramatically reduces time-to-integration, supports scalable extensibility, and aligns with modern expectations for one-click deployments and dynamic orchestration. SaaS value is shifting from standalone features to ecosystem interoperability. The next generation of platforms will be defined by how well they connect.

We’re watching the rapid transformation - and possible end - of SaaS as we know it. Microsoft CEO Satya Nadella recently pointed out that traditional SaaS is disappearing, and I strongly agree. But I see the timeline accelerating even faster: Phase 1 (Right now): AI as Support AI enhancements like Copilot, Gamma, and Harvey are currently complementing existing SaaS platforms, making them seem more efficient and attractive. Providers feel secure, viewing AI as a feature rather than a threat. Phase 2 (Within 6-12 months): AI Takes Over Operations AI agents will quickly transition from assistants to autonomous operators. Instead of manually using tools like Tableau or Meta’s ad platform, we’ll simply instruct agents to perform analyses or optimize ads directly. The expertise traditionally embedded in SaaS interfaces becomes easily accessible through agents. Phase 3 (Within 1-2 years): Software Becomes Invisible AI agents begin interacting directly via APIs, eliminating the need for human-oriented interfaces like dashboards and menus entirely. This strips away the core value SaaS once provided—human usability. This isn’t standard disruption; it’s a fundamental shift away from human-operated software to agent-operated software. At the same time, the rise of AI-driven coding tools makes custom internal software development dramatically easier and cheaper. Companies no longer need to rely on costly SaaS subscriptions—they can quickly create tailored internal applications that perfectly fit their needs. The winners in this new era won’t simply be those who integrate AI the quickest. Instead, they’ll be companies providing open, agent-friendly APIs, becoming the trusted providers of actionable data and execution within their fields. The real question is whether giants of all industries will swiftly adapt or risk becoming obsolete, much like tech giants of the past. We’re entering an extraordinary period of opportunity for agile startups ready to embrace this change.

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.

Anchoring won’t fix your pricing page, but this will… Pricing is the second most visited page on any SaaS website, but most pricing advice misses the point. While everyone obsesses over psychology tricks like "end in 0.99" or "use anchoring," they miss something crucial: Those tricks only work if someone's already sold. In reality, most visitors aren’t ready to buy – they’re still figuring out your product. Therefore, pricing pages convert best when they demonstrate your value clearly, even if it seems repetitive. The best pricing pages do 3 things: 1. Lead with the outcome: Instead of a generic “Choose your plan” headline, show what customers actually get, e.g. “A/B test landing pages without coding” or “Generate fresh ad creatives in minutes based on your previous winners.”     2. Price for value, not access: Instead of charging per seat, price based on work delivered, (e.g. pages tested, ads generated). That way, every tier reminds them of the value they’ll get.     3. Answer objections directly: Add an FAQ addressing your prospects’ top 3-5 fears head-on. (Your sales team knows these by heart). Examples: “Will my team adopt a new tool?” or “Are we charged for unused credits?” Want a great example? Check out Leadsie's pricing page (Screenshots below) Helpful? Follow me to keep seeing my posts. Matt Lerner

Slack went down, and the internet panicked. But let’s be honest—this isn’t about Slack. It’s about how fragile our business operations have become when a single tool suddenly disappears. I’ve seen this play out before. A company I worked with relied heavily on a single SaaS vendor for all internal and external communication. When that platform went down—just for a few hours—it disrupted customer service, stalled sales deals, and even delayed compliance reporting. The aftermath? Scrambling to recover, frustrated clients, and a whole lot of “Why didn’t we have a backup plan?” First, redundancy is non-negotiable. Every business should have an alternative communication channel ready—whether it’s email, a second chat tool, or even (gasp) the phone. Second, product counsel should be in the room when these tools are selected. The legal team isn’t just there to review contracts—we should help assess risk, negotiate protections, and push for backup plans before disaster strikes. Third, train your teams. A business continuity plan only works if people know how to execute it. If your team’s response to an outage is “Now what?”—that’s a problem. If you’re rethinking your reliance on SaaS after this outage, good. It’s time for legal, IT, and product teams to work together to build a more resilient operation. For my take on the legal implications of SaaS downtime, check out the video—because trust me, contracts matter when things go south. -------- 💥 I’m Olga V. Mack 🔺 Expert in AI & transformative tech for product counseling 🔺 Upskilling human capital for digital transformation 🔺 Leading change management in legal innovation & operations 🔺 Keynote speaker on the intersection of business, law, & tech 🔝 Let’s connect 🔝 Subscribe to Notes to My (Legal) Self newsletter

The recent Salesloft Drift (a third party application on Salesforce) breach is a powerful reminder that even the most sophisticated, well-resourced organisations are vulnerable when their supply chain security is in question. Tech titans—leaders who invest heavily in cyber defense—have now joined a long list of victims in a campaign rooted not in advanced malware, but in simple exploitation of third-party SaaS integrations. What’s striking is the attack itself wasn’t particularly high-tech. The adversaries exploited stolen OAuth tokens via Salesloft Drift’s integration with Salesforce — something any organisation could miss when the number of connected apps is ever-increasing. This breach highlights just how our reliance on interconnected SaaS platforms and supply chain partners inherently amplifies risk. If you’re integrating, you’re inheriting exposure—sometimes in ways even robust internal controls cannot offset. While it’s true that no single tool can guarantee prevention, SSPM (SaaS Security Posture Management) platforms are now essential for modern SaaS-centric businesses. The right SSPM doesn’t just help you set policies—it monitors for abnormal access, flags risky apps, and enables rapid detection and response when something goes wrong. In this case, an SSPM solution may not have blocked the initial token misuse, but it absolutely could have empowered incident response teams to respond far more swiftly—limiting data exfiltration and shoring up defenses before cascade breaches occur. For those in the market, consider best-in-class SSPM solutions like Obsidian Security (highly regarded for supply chain visibility), AppOmni, Adaptive Shield (Crowdstrike), and others now leading this critical category. Having deep insight into SaaS app risk posture isn't yet part of the Essential 8 - the security of your business will depend on it. Cyber resilience isn’t just about securing your walls—it’s about keeping an eagle eye on your supply chain, practicing robust integration hygiene, and investing in modern SSPM capabilities. The organisations that thrive tomorrow are preparing today. #cybersecurity #SSPM #Salesloft #SaaSsecurity #SupplyChain #IncidentResponse

This is a full decision tree for how to price your SaaS product. We recently published an edition outlining some pricing considerations, and the demand for more pricing information was overwhelmingly positive. We wrote this edition to answer that call and build out a true pricing model decision tree, along with a full guide to help you determine which pricing structure actually fits your product, buyer, and market. One of the most common mistakes is starting with: “what should we charge?” The right question is: "how should customers pay us, and why?" At a very high level (full details in the comments), here's the decision tree: Step 1: Does value scale with users? → Seat-based. Simple, forecastable. Use this when each user gets clear, individual value and the product becomes more valuable as more people join (e.g. collaboration or workflow tools like Slack, Notion, Salesforce). It’s simple, familiar, and creates a clean expansion path as teams add users. . Watch out as AI reduces seats needed. Step 2: Does value scale with usage? → Usage-based. Use this when value scales directly with consumption, customers naturally use more over time, and the metric is simple and intuitive (e.g. API calls, messages, compute). It’s common in infrastructure, data, and AI products where cost and value rise in lockstep (e.g. Twilio, Snowflake). Step 3: Is value tied to a business outcome? → Outcome-based. Use this when you can directly tie your product to measurable outcomes (revenue, cost savings, risk reduction) and prove the impact with clear data. Intercom priced Fin at $0.99/resolved ticket and went $1M to $100M ARR. Step 4: Is the product a system of record? → Platform/tiered. Use this when your product becomes core infrastructure used across multiple teams, with growing workflows, data, and integrations that increase switching costs over time. It typically combines a base platform fee, tiered plans, and add-ons that unlock as customer needs mature (e.g. HubSpot’s free CRM → Starter → Pro → Enterprise). Their NRR improved from 101.8% to 105% after they overhauled structure. Most companies end up with a hybrid of the above. Hybrid pricing jumped from 27% → 41% adoption in just 12 months. Important: There is no “best” pricing model, only the one that best reflects how customers experience value. The right model depends on how value expands within an account (users, usage, outcomes, or platform adoption) and whether it matches your actual GTM motion and how deals close. From all the pricing workshops we've run, content we've curated, and conversations we've facilitated, the biggest piece of pricing advice is this: Treat pricing like a product: test, iterate, and refine it continuously. It’s a core part of your go-to-market, and what once had wiggle room is now a critical lever to get right. The full pricing model decision tree guide can be found in the comments. Hope it's helpful! How are you evolving your pricing model in 2026?

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.