Table of Contents
Intro
Not Your Daddy’s SaaS – New Business Models or Bust
Subscription Pricing? That’s So 2020
Forget 90% Margins – Meet the AI Cost Monster
New Value, New Tolls: Agents as Marketplaces & Platforms
From Waitlists to Enterprises: GTM in the Agentic Age
Conclusion: Adapt, or Be Automated (Out)
FAQ
Picture this: a non-human coworker that doesn’t gossip, doesn’t take coffee breaks, and works 24/7 without complaining – all for a fraction of your salary. Tech investors are salivating. Sam Altman himself prophesied that this is the year “the first ‘agents’ … join the workforce”, and VCs have poured $8.2 billion into “agentic AI” startups in 2024 alone. The hype machine is in overdrive, screaming that autonomous AI agents will replace everything from your Slack to your assistant and your assistant’s assistant. But behind the buzzwords and billion-dollar valuations lies a gnarly question: How do these agentic startups plan to make money – and how is that different from good old SaaS?
Not Your Daddy’s SaaS – New Business Models or Bust
Agentic startups can’t rely on the comfortable per-seat, high-margin playbook of SaaS. Their most viable business models flip the script: charging for usage, outcomes, or marketplace transactions instead of fixed licenses. Why? Because an AI “employee” that actually does work incurs real costs (it literally burns cash in cloud compute) and can replace dozens of human users – rendering SaaS’s per-seat pricing about as relevant as an AOL dial-up CD. The winning agentic startups are ditching SaaS conventions and embracing models that reflect three uncomfortable truths:
(1) AI agents have unit costs that traditional software never did,
(2) one autonomous agent can do the job of many humans (so charging per human user is bonkers)
(3) if you can’t quantify the value your agent creates, you’re just another demo waiting to be open-sourced by OpenAI.
In short: the agentic era demands business model innovation as bold as the tech itself – or these startups will end up as cautionary tales on TechCrunch.
Subscription Pricing? That’s So 2020
The first casualty of the agentic revolution is the beloved SaaS subscription. Per-seat licensing makes zero sense when one AI agent might do the work of 50 employees. Think about it: if your AI can replace an entire marketing team, you gonna charge for one seat? That’s a fast track to the nonprofit sector. Smart founders are waking up to this reality. Flo Crivello, CEO of Lindy, bluntly states: “seat-based pricing makes no sense” in a world where we might soon see the first “1-person, billion-dollar business.” Lindy, an AI “chief of staff” service, walked the talk: it ditched flat subscriptions in favor of usage-based pricing. Users buy credits (5,000 for $50 in Lindy’s Pro plan) and consume them as the AI completes tasks. In other words, you pay for what the agent does, not how many logins you have. Money talks and, as Crivello puts it, “dollars are the only truth” when it comes to validating demand.
Other agentic upstarts are experimenting too. Some, like Cognosys, still offer SaaS-like tiers ($15/month “Pro” or $59 “Ultimate” for unlimited use) – presumably betting that most users won’t push “unlimited” to its breaking point. (If they’re wrong, those margins are gonna look uglier than a WeWork income statement.) Meanwhile, truly bold players are pricing in realms SaaS never dared. Take Hippocratic AI, a healthcare agent startup: it launched a “staffing marketplace” for AI nurses, effectively charging by the hour. They claim their AI agents cost about $9/hour versus a human nurse’s ~$60/hour. Yes, you read that right – an AI billed like an hourly employee, not licensed like software. It doesn’t get more anti-SaaS than that. The uncomfortable truth for traditionalists? Agentic startups are selling outcomes, not software. And when your product delivers actual work output – answering emails, coding features, or handling patient calls – the business model naturally shifts to transactional or usage-based pricing. SaaS subscriptions are the walking dead.
Forget 90% Margins – Meet the AI Cost Monster
Let’s pour one out for SaaS economics: those sweet 80-90% gross margins on software are getting clobbered by AI. Traditional SaaS is basically “build once, sell twice… or a million times” – the cost of serving another user trends toward zilch. Not so with agentic AI. Every time an AI agent runs off to book your flights or crunch your market research, it’s racking up cloud bills. GPUs ain’t cheap, and OpenAI sure as hell doesn’t give nonprofits rates to fledgling startups. Flo Crivello admits Lindy’s margins “are healthy but not quite software-like” because “LLMs are so pricey”. In plain English: each marginal user has a non-trivial cost. If SaaS is a fine dining restaurant with prix fixe pricing, agentic AI is an all-you-can-eat buffet where every extra plate of shrimp cuts into your profit.
This forces agent startups into a tough balancing act. They must charge (much) more per user than old-school SaaS to avoid going bust, or find ways to radically cut compute costs (hello, proprietary models and optimization). Many do both. Lindy started charging from day one – no free ride, no massive freemium user base – precisely because they needed “a real signal” that customers would pay and to cover those API costs. (When each new user literally lightens your VC bank account, you get over the “but growth first!” mantra real quick.) Some startups are building their own models or fine-tuning open-source ones to escape the OpenAI tax. Others bake in usage limits or throttle heavy consumers even on “unlimited” plans – the fine print that says don’t actually run this thing 24/7 or we’ll politely ask you to upgrade to Enterprise. The net effect is an inversion of SaaS logic: scale doesn’t automatically improve margins until you conquer the unit economics. As one analysis noted, top SaaS companies enjoy such high margins that AI startups could charge less and still be fine at, say, 65% margin. But that assumes these AI companies can reach 65% in the first place. Right now, many agentic ventures are intentionally operating with lower gross margins while they figure out how to not go bankrupt on cloud spend. It’s a wild new world where customer usage is a blessing and a threat – a far cry from the SaaS glory days of “land and expand” without breaking a sweat.
So if you’re a founder diving into agentic AI, remember: every interaction has a cost. Either price it in or find a sugar daddy investor to subsidize it. (Many have, by the way – see that $8B VC figure again.) But eventually, the piper – or rather, the GPU cluster – must be paid. The only path to long-term viability is mastering the unit economics or making your AI so indispensable that customers will fork over enough to keep you afloat. In agent-land, usage is king but cost is the executioner.
New Value, New Tolls: Agents as Marketplaces & Platforms
Perhaps the biggest departure from SaaS is the endgame vision many agentic startups have: to become platforms or marketplaces that take a cut of value transacted, rather than just charging a software fee. Think of how Android or iOS make money – not by billing you for the OS, but via app store commissions and ecosystem lock-in. Some AI founders have similar dreams (delusions?). Case in point: /dev/agents – a buzzy new startup that raised a casual $56 million seed at a $500 million valuation with basically zero product. Why the insane bet? Because their founders (ex-Googlers) pitch /dev/agents as the “operating system” for AI agents. And how do they plan to monetize such an OS? Not by selling licenses one by one. “It likely won’t be too different from how Android is monetized,” CEO David Singleton says. Translation: they want to facilitate a ton of AI agent activity – booking flights, ordering supplies, running errands across the web – and “take a cut” of the commerce or charge subscription fees for premium access. In Singleton’s words, “You can imagine a lot of commerce happening on this platform… We’d either take a cut of sales or charge users for subscriptions.” When a founder name-drops Android’s model, you know they’re aiming to be an infrastructure player, not a mere app. It’s a stark contrast to traditional SaaS, which sells the tool; these guys want to tax the whole economy of tasks their agents perform.
It’s not just /dev/agents chasing the platform play. Many agentic startups eye a future where they become essential intermediaries for getting stuff done, potentially earning affiliate fees, transaction commissions, or usage tolls along the way. Imagine an AI travel agent that books your hotel – why wouldn’t it take the same commission a human travel agent or Booking.com would? If an agent helps you shop for supplies, there’s an Amazon affiliate link hiding in there somewhere. One founder jokingly dubbed this the “AI Costco model” – give you a cheap membership (or free tool) and make money on everything you do with it. We’re already seeing hints: MultiOn, a personal agent that can, say, plan and book your entire vacation, demoed a flight booking in minutes. Connect the dots and it’s easy to see future revenue-sharing with service providers (airlines, hotels, etc.) as a viable model. Heck, Hippocratic AI’s healthcare agent marketplace essentially charges hospitals or clinics for supplying AI “nurses” on demand – a cut of what would’ve been a labor cost. They’re not selling software; they’re selling outcomes (patient calls handled) and taking their slice of the pie.
This platform/marketplace approach is both exciting and daunting. If it works, an agent startup could achieve network effects and a stranglehold on a new value chain – becoming the next Salesforce AppExchange or AWS Marketplace, but for AI-driven tasks. If it fails, well, they might find out that incumbents won’t sit idle while upstarts try to tax their ecosystems. (Cue Microsoft and Google, who are busy baking agents into Office and Cloud products – effectively bundling for free what startups want to charge for.) Still, the boldness is commendable. These startups are saying: we create so much value, we deserve a direct cut of it. That’s a different mindset from SaaS, which was happy to charge per user and let the customer reap all the upside of using the tool. The agentic model is more “we’ll help you make (or save) $100, and we’ll take $10 of that, thank you very much.” It’s outcome-oriented, arguably more aligned with customer success – or at least it forces the agent to actually deliver. Just be careful: if your agent starts demanding a raise (okay, kidding… mostly), you’ve basically built Skynet with a subscription plan.
From Waitlists to Enterprises: GTM in the Agentic Age
Business model isn’t just about pricing – it’s also about who and how you charge. Traditional SaaS often followed a straightforward GTM (Go-To-Market): target a department, woo the VP or CIO, land an annual contract, rinse, repeat. Agentic startups are taking a more chaotic route, sometimes starting with individual power-users and bottoms-up adoption long before any CIOs sign checks. Why? Because a lot of these AI agents feel almost like consumer apps or personal assistants, not enterprise software. They rack up tens of thousands on waitlists (MultiOn had 30,000 people queued for its beta by late 2023) by promising to lighten your workload, not just your company’s SaaS stack. The strategy: get savvy users hooked on an autonomous helper that will do their busywork, figure out where it provides so much value that a budget appears, and then upsell into organizations. It’s the old “land and expand” but starting with the end-user, not the buyer. This can work wonders – if the product is life-changing – or it can fizzle out if the novelty wears off and nobody wants to pay the tab.
The flip side is a few agent startups going straight for enterprise value from day one. These tend to be more specialized “vertical” agents. For example, Adept AI (one of the early well-funded agent companies) and Cognition Labs’ Devin agent both target software engineering tasks – a domain where time is literally money. An AI agent that can handle grunt coding work autonomously is a no-brainer for a CTO to pilot (every PR merged without a human saves $$$). In such cases, the business model might resemble SaaS more closely at first – e.g. an annual license or usage credits packaged in a big contract – but even there, savvy vendors peg pricing to key metrics like productivity gains or hours saved. One enterprise-focused founder quipped that they charge customers “per million keystrokes the AI doesn’t have to bother your developers with” – a tongue-in-cheek way of saying value-based pricing. Unlike horizontal SaaS, which often sells a swiss-army tool and hopes the customer figures out the ROI, these agent companies have to tie revenue to clear outcomes (or risk being axed when budgets tighten). As Insight Partners’ Praveen Akkiraju put it, if 2024 was LLMs, 2025 will be the year of agentic AI – but that will only come true if agents prove their worth in real business terms. Early deployments are keeping a “human in the loop” to build trust, effectively saying “look, our AI hasn’t burned the house down – now pay us.” It’s a far cry from Slack’s strategy of sneaking into teams virally, but for these high-stakes agents, caution is part of the business model. No one will hand the keys to an “autonomous AI” without proof it knows the road. So agent startups are wisely pairing bold tech with incremental trust-building – and often charging for the human oversight as a feature. (You might get an AI doing 90% of the work, but that 10% human QA could be the difference between a success story and an AI-goes-rogue headline. That safety net isn’t free.)
Conclusion: Adapt, or Be Automated (Out)
The bottom line? If you’re building an agentic startup, you damn well better be as innovative in your business model as you are in your technology. The “move fast and break things” mantra doesn’t apply when what you might break is your own bank account with each API call. The most viable models we see today shun the old SaaS orthodoxy of fixed subscriptions and fat margins. Instead, they embrace usage-based pricing, outcome-based value sharing, and platform economics – even if that means slimmer margins or trickier sales in the short term. This isn’t just a pivot in pricing strategy; it’s a fundamental realignment of what software businesses look like. Agentic startups aren’t selling software-as-a-service; they’re selling service-as-software – and that requires a radically different mindset. It’s about delivering results (and taking a cut), not just delivering tooling. It’s about scaling the work, not just the user count.
Will every agentic startup figure it out? Hell no. Many will crash and burn, victims of sky-high expectations and business models that didn’t pan out. Some might find that their grand “AI agent marketplace” is just an AWS feature waiting to happen, or that enterprises pat them on the head and build their own in-house agents for free. But mark my words: a few will crack the code, and those are the ones that will make today’s SaaS darlings look like Kodak in the age of digital photography. As an industry, we’re on the cusp of something big – if a bit unruly. So to the founders and investors reading: take a shot of courage, ditch the comfortable conventions, and charge headlong into this new agentic business frontier. Price boldly, charge for value, and don’t apologize for breaking the SaaS mold (it needs breaking). The startups that get it right will not just join the workforce – they’ll upend it, laughing their way to the bank. Welcome to the future; no humans in the loop required.
FAQ: Agentic Startups vs. Traditional SaaS Models
1. Why doesn’t the traditional SaaS subscription model work for agentic startups?
Because agentic AI performs work, not just provides tools. One AI agent can replace multiple human users, so charging per seat is nonsense. Plus, every task incurs compute costs — unlike SaaS, which scales cheaply. Usage-based or outcome-based pricing is a better fit.
2. What business models are agentic startups using instead?
The most viable models include:
Usage-based pricing (e.g. credits consumed per task)
Outcome-based pricing (charging based on value delivered or time saved)
Marketplace or transaction fees (taking a cut of actions performed by agents)
Enterprise licenses with usage thresholds
Flat subscriptions are fading fast.
3. Are agentic startups profitable like traditional SaaS companies?
Not yet. Many face lower gross margins due to expensive compute costs (thanks, GPUs and OpenAI API). The goal is to improve unit economics over time — but high margins like SaaS (80–90%) are rare in this space right now.
4. How are agentic startups handling Go-To-Market strategy?
Some start bottoms-up, targeting individual power-users first (like consumer apps). Others go enterprise from day one, especially in specialized verticals like engineering or healthcare. Trust-building and proof-of-value are critical for adoption, especially in high-stakes workflows.
5. Could traditional SaaS companies pivot into agentic models?
Yes — and many are trying. Microsoft, Google, and Salesforce are embedding agents into their platforms. But nimble agent-first startups still have an edge in speed, UX, and risk-taking… for now.
6. Is there a long-term moat for agentic startups?
Only if they master both a compelling value proposition (clear time/money saved), and a scalable, defensible business model (e.g., proprietary workflows, marketplace dynamics, or domain-specific agents). Otherwise, they risk becoming glorified features of bigger platforms.
Kurt Fischman is the founder of Growth Marshal and is an authority on organic lead generation and startup growth strategy. Say 👋 on Linkedin!
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