How Usage-Based Pricing is Changing 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?

The recent interest in hybrid, usage-based and outcome-based pricing is on 🔥. Here's the thing: successfully going usage-based is way more than a pricing change. It's a hard pivot, and you might not be ready for it. What to look out for: 1. Pure usage-based or pay-as-you-go pricing really isn't for every product. The challenge is finding a metric that buyers accept (the feeling of predictability is key) & that makes more $$. Look for metrics that grow quickly within an account, aren't susceptible to huge swings, and that are *outputs* of getting value rather than *inputs*. Or consider a workaround like putting a usage limit on a subscription plan or adding a fair usage policy to protect against heavy users. 2. In a usage-based business, there's no room for shelfware. The hard work *starts* at contract close. Everyday the customer is making a purchase decision about whether to adopt your product. This means everyone plays a role in customer success. 3. Sales incentives need to evolve to embrace land-and-expand. It's better to close deals quickly, then let usage grow over time. Commission structures can't over-index on the initial commitment. 4. Overage isn't a bad thing to penalize. Your customer grew their business and wants to consume more of your product. That's fantastic, celebrate it! 5. There are ways to make usage models more palatable to the enterprise. This usually means getting into the weeds with contract structures like:  - Annual draw-down: Customers flexibly draw down their usage over 12 months like a gift card. If they use the product faster than expected, they have time to plan & budget before renewal.  - Roll-over: Give customers the option of rolling over unused usage credits *if the next commit is larger than the last*. This helps reduce hoarding.  - Grace periods: After the grace period, customers can either re-up their contract at a higher commit or pay for the one-time flex spend. 6. Usage-based revenue isn't necessarily ARR. But that doesn't necessarily mean it's de-valued by investors. Folks want to see that usage revenue *acts* like ARR -- that it's highly re-occurring, grows over time in the average account, isn't project-based, and has high gross margin. 7. Forecasting your business is about to get way more complicated. It becomes a data science exercise more than a pipeline exercise. Finance teams are building models looking at individual customers & cohorts, factoring in criteria like the use case, ramp time, commitment, etc. What could go wrong 🙃 --- Adopting usage-based models isn't easy. But there aren't many better alternatives for AI, automation, API and FinTech products.

Per-seat is no longer the atomic unit of software. Consider customer support software Zendesk: companies currently pay per support agent ($115/month/seat), but when AI can handle ticket resolution, the natural pricing metric becomes successful outcomes. If AI can handle a sizable proportion of customer support, companies will need far fewer human support agents, and therefore fewer Zendesk software seats. This forces software companies to fundamentally rethink their pricing models to align with the outcome they deliver rather than the number of humans that access their software. If you are increasing the productivity of labor or usurping it, how should you price this? If every action your customer takes incurs a corresponding cost through an API call, how should you factor that in? How will buyers react to pricing models they’ve not seen before? There’s a lot to consider. However, AI-native companies are leaning into this shift. For instance, Decagon, an AI customer support platform whose AI agents autonomously resolve customer service tickets, offers per-conversation (usage-based) and per-resolution (outcome-based) pricing models to their customers. Both models scale with the amount of work completed (i.e. value delivered) vs. labor (software seats). Read more on Emerging AI Pricing Models in the a16z Enterprise Newsletter with Ivan Makarov and Equals 👇

SaaS pricing is shifting from selling seats to selling outcomes. I've been talking about this since March, and the noise is only getting louder. Last month, Sarah Tavel from Benchmark pointed out in her post “Sell Work, Not Software”, that we’re moving from selling software to selling work. This creates blue ocean opportunities by focusing on real value and outcomes, not arbitrary metrics like headcount. Jamin Ball from Altimeter said the same thing in his post, “Is Seat-based Pricing Dead?”. It's about reflecting the value we deliver, not just the number of seats sold. If our solution works as intended, you should achieve your goals without needing to hire more people, right? So, why is this happening now? Two reasons: Post-ZIRP Era: Layoffs are becoming the norm. Fewer seats are sold, and the ecosystem feeds on itself. SaaS companies, early adopters of tech, are feeling the pinch as there are fewer people to sell to, leading to reduced revenue. AI: Companies are leveraging AI to become more efficient, reducing the need for additional headcount. Instead of hiring more, they're implementing AI to optimize operations. The old way of pricing fails to align value with cost. Each seat no longer correlates to the value a company derives. Many companies end up cannibalizing their business model as AI promises to do more with fewer resources. As we rethink our pricing models to reflect the value we deliver, the focus should be on outcomes and results, not just the number of seats we fill.

Before the iPhone came out, carriers used to make money by selling minutes. After the iPhone? They started making money selling data. Here's the big problem with selling minutes: there's only 1440 minutes in a day. You're capped on how much you can make from a customer. But charging for data consumption? It's limitless. This is the big shift happening in SaaS today. Seat-based pricing is capped. With companies shrinking workforces, this makes for an even worse pricing model than carriers charging for minutes. So what's the big shift? Charging based on outcomes. This is what I learned about during my walk with Manny Medina now the Founder and CEO of Paid where they help SaaS companies set up billing systems for AI Agents. SaaS Companies are now looking at four distinct new pricing models as they build on Paid: 1) FTE Replacement Model. Price per Agent. 2) Consumption Model. Price per Action. 3) Process Automation Model. Price per Workflow. 4) Results-Based Model. Price per Outcome. If you're deploying Agents for your customers, you should definitely check Manny's new company Paid out so you're properly architected to bill and get out of the Seat-based pricing model. And no, this isn't a sponsored post. I just thoroughly enjoyed our walk and our conversation and I think he's got the right unique insight for this big shift that's happening. If you're not yet building AI-Agents, if you're still thinking about how to properly leverage AI inside of your aging SaaS platform, I dig more into the Death of the Seat-based Pricing Model on this week's edition of The GTM Lever. You can read it here 👇 https://lnkd.in/gKMNtn46

The gym membership model is dying - here's what's replacing it We looked at pricing data from 200+ SaaS companies. The shift is clear: usage-based pricing has grown 3x since in the last 3 years. But here's what everyone's missing: This isn't just about pricing - it's about trust. When Netflix raised prices, they reportedly lost 500k subscribers. When New Relic moved to usage-based, their net retention hit 170%. The key insight? Customers don't hate subscriptions. They hate feeling trapped by them. Three patterns I'm seeing: 1. Top performers expose usage metrics in real-time 2. They offer hybrid models with low base fees 3. They're making downgrades as easy as upgrades While unlimited access sounds great, but how many of us are paying for services we barely use? It's the gym membership all over again. That's why we're seeing a move toward usage-based pricing. Consumers increasingly prefer paying only for what they actually use—valuing fairness over flat rates. Think less "all-you-can-eat buffet," more "pay-per-plate." The companies that get this right aren't just reducing churn - they're turning pricing into a competitive advantage. What's your take on usage-based pricing? Has it worked in your industry?

I think the winner in the sales tech race will be the company that figures out how to break away from seat-based pricing and cracks usage-based pricing. Here's why: Today, there are about 2.5M reps (SDRs, AEs, CSMs/AMs). My prediction: this number will shrink over the next few years. But companies will still have to generate pipeline (SDRs) + close revenue (AEs) + retain/upsell customers (CSM/AMs). How will companies still hit their revenue targets with fewer reps? My thinking is simple: there will be new tooling/infrastructure that will help companies generate more revenue with less people. But these tools can't be seat-based, or else their NDR will organically shrink (not good). Instead, these tools need to figure out how to capture the value they're driving. Here's an example... Today: - ACME corp has 10 SDRs - They send a total of 33K emails a month - That generates a total of 150 meetings a month (15 meetings per rep per month) In 2025: - ACME corp has 2 SDRs - They send a total of 66K emails a month - That generates a total of 450 meetings a month With the current pricing model of most sales technologies, the contract size, in this example, would shrink 80% (from 10 seats, down to 2). Yikes. But with a true usage-based pricing model (even outside of email credits), the contract size should *increase* by 2-3X (because they're sending double the emails and triple the outcome). This makes logical sense, because the tool is enabling this company to effectively 10X their output. Whoever figures out how to truly capture *outcomes* instead of seats will win big in the new era of gen ai tooling that will power these "10X Reps." PS - The operators of these systems may look more like a "Growth" person than a traditional SDR. (I think SDRs, Ops, and Growth Marketing are best suited to figure out this new role and power pipeline generation in the future.)

What's driving AI companies like OpenAI to reach $157B+ valuations in just a few years, while it's taking traditional SaaS companies decades to get there? Skeptics think market hype is driving this growth, but the real growth is due to 2 structural advantages in the business model: 1. Usage-based pricing unlocks larger contracts Traditional SaaS uses seat-based pricing. Companies pay the same amount per user per month, regardless of usage. AI agent companies, like Decagon, price based on volume—$X per conversation or $X per resolution. This naturally drives larger deals because the ROI is very measurable and can be benchmarked to labor costs. 2. The ROI is clearer and faster than traditional SaaS With AI agents, companies see ROI within weeks. Since they see reduced support volume, they save on outsourcing to human agents. If you’re charging less than what they would spend hiring, it’s an obvious choice. This combination of usage-based pricing and near-instant ROI allows AI agent companies to deliver clear, measurable value fast. I’m excited to see how AI agents continue to redefine what’s possible in the industry. Where do you see this going?

AI startups hit $70M ARR in seven months. Reading Elena Verna's latest piece on Lovable's meteoric growth made me pause. Not because of the numbers—though $70M ARR in seven months is staggering—but because of what it reveals about the career calculus we're all making right now. The data is stark: AI companies reach $1M ARR in 11.5 months versus 15 for traditional SaaS. Time to $5M? 24 months versus 37. But here's what struck me most: the fundamental shift from feature-based to usage-based value creation. In traditional SaaS, we’ve seen teams spend months convincing users to see value. Complex onboarding flows, feature tours, gradual adoption curves. The "aha moment" was often buried under layers of setup and learning. AI flips this completely. Value happens in the first prompt. Users get hooked not after weeks of usage, but after one meaningful interaction. This isn't just changing pricing models—it's rewiring how we think about product-market fit itself. What fascinates me most is the structural advantage this creates. AI-native companies aren't just moving faster; they're architected for speed at every level. While legacy companies fight billing system constraints and feature-based monetization, AI companies iterate pricing in days, not quarters. This creates a compounding effect that's hard to reverse. Traditional companies can't bolt on AI-native thinking after the fact. The gap isn't fixed—it grows. For those of us building careers in this space, the implications are profound. The old playbook of gradual feature adoption and seat-based growth is becoming obsolete. The new standard: deliver value immediately, monetize usage, and maintain architectural flexibility. Are we adapting our skills fast enough for this new reality? Read Elena’s article: https://lnkd.in/eze6UQEF — 👋 This is Shyvee Shi — former LinkedIn product leader, now building the AI Community Learning Program, powered by Microsoft Teams. If you're curious about building and upskilling with AI, you can join our AI Community and get access to curated resources, tools, programs and events via aka.ms/AICommunityProgram. ♻️ Repost to help someone learn, build, and grow in the AI era. #AI #ProductManagement

Last week Salesforce announced they acquired Informatica for $8 billion. This is another warning sign that the era of seat based pricing in SaaS is over. Outcome led agent pricing is coming. The change will rewrite not just SaaS economics but also GTM as we know it. Why this deal matters for Salesforce? 1. Clean real time data is the future of Agents: Informatica's pipes, MDM and 5K+ connectors will allow Salesforce to feed Agentforce with metrics and data that enterprise buyers care about. 2. Multiple Payoffs: Informatica is a simpler version of Mulesoft. There will be plenty of cross sell and upsell revenue opportunities with Informatica. 3. Outcome based pricing: You cannot charge outcome based pricing if you can’t measure outcomes. With acquisitions like Informatica - Salesforce is creating a moat for the future of Agents.  What’s Next for SaaS? Expect more acquisitions in this space. Microsoft, SAP, ServiceNow - anyone chasing Agent economics will try to acquire all data fabrics, systems of records, connectors and any data company that will help measure outcomes. Owning these datasets will help deliver on the outcome based pricing natively while others struggle to justify their value. How does this shake up GTM? 1.Agentic SaaS: The future of all SaaS is agents and tied to outcome based pricing. However, to measure outcomes and pay only for results - you need access to first party enterprise wide data ecosystems. Only a few big platforms will truly own native access. 2. Agent Pricing : Outcome pricing will Destroy seat based SaaS pricing: Customers will start small and keep paying as long as the agents keep delivering results. Transparent and clear ROI means faster churn when there’s no results and far less AEs to drive the same revenue. 3. Changes in sales orgs: Selling agents will become more strategic than selling SaaS. Management consultants will become the new SDRs. All the focus will be on making the business case. Sales and Renewals will take care of itself once there’s a valid business case. Agentic GTM will lead to fewer sales reps and more strategic closers. This is a once in a generation change for Salesforce. Salesforce knows that the seat based pricing will eventually cap out. By owning data and agents, Salesforce can sell measurable business outcomes. That is the way to dominate the next big wave in tech. Risk: The big risk to this approach is timing. If the entire SaaS ecosystem pivots to Agents and outcome based pricing too fast, then there will be far less sales reps and Salesforce CRM seat based subscription revenues will tank before the Agentforce outcome based revenue kicks in. Nailing that transition window will be key to their strategy. TAKEAWAY: Agents and Outcome based pricing will rewrite SaaS economics. It will likely slash head counts in GTM. Few large companies will dominate this space while others struggle for seamless access to data. Salesforce just fired an early shot, more will follow.