Will AI Transform—or Threaten—the Per-Seat SaaS Model?

Charging for software has always been a tricky business, and I’ve watched firsthand how it’s evolved. In the consumer world, the early App Store days saw a quick one-time fee of a few bucks for an app, yet developers still had to shoulder an ongoing burden of updates and maintenance. We learned that subscription models often made far more sense—both for recouping the continuous costs of building software and for matching the actual value delivered over time.

In the business world, that realization hit even earlier. Microsoft and Adobe used to sell annual licenses for their products, but now pretty much everything has moved to monthly or annual subscriptions. That, in essence, is SaaS (Software-as-a-Service), where customers pay recurring fees, typically per seat, for software access, support, and upgrades.

Why Per-Seat Pricing Made Sense for So Long

Per-seat pricing took hold for a few reasons:

1. Simplicity and Alignment: Paying by user felt straightforward for both finance teams and product teams. As headcount grows, so does usage—and the bill.

2. System of Record: Many of the best SaaS products evolve into a company’s central repository (think Slack’s messages, Notion’s docs, Figma’s designs). Because that data can be mission-critical, businesses often want everyone to have a seat, ensuring no one is locked out.

3. Predictable Revenue: On the provider side, it’s fairly stable. Each new user is new revenue. That’s made planning and growth forecasts easier.

The Downside: Economic Shifts and Headcount Reductions

As great as this model seems, it can create serious vulnerabilities during economic downturns or mass layoffs:

• Immediate Revenue Loss: When a company shrinks its workforce, that triggers an automatic drop in paid seats.

• Budget Scrutiny: Tightening the belt often leads to prioritizing which tools truly matter. Anything considered non-essential can get dropped or scaled back.

We saw net-dollar-retention (NDR) dip at many SaaS companies during recent slowdowns. That’s painful because it sets off a cycle of cost-cutting on the SaaS-provider side, which doesn’t do any favors for innovation or morale. Historically, the sector bounces back once the economy recovers, but per-seat pricing remains especially exposed to workforce fluctuations.

The AI Angle: Fewer Humans, Same (or Greater) Output

An even bigger wild card now is artificial intelligence. As code generation and large language models continue improving, it’s not inconceivable that fewer engineers or analysts can accomplish the same workload. I’ve had conversations with multiple VCs about whether the widespread adoption of AI will shrink the total number of employees at a company who need access to certain tools. It’s still speculation, but I think it’s fair to say it’s where many of the biggest tech companies and investors think the market is heading.

For a per-seat SaaS vendor, this is an existential threat. If, say, a 50-person engineering department becomes a 30-person department thanks to AI boosts in productivity, that’s a 40% seat reduction. Multiply that across thousands of customers, and you suddenly have major downward pressure on the revenue model.

Rethinking the Pricing Model

If AI truly reshapes headcount, seat-based pricing might have to take a back seat. Here are a few alternative models I’ve been thinking about or observing:

1. Usage-Based

• Datadog Example: You pay per log, metric, or trace consumed. It’s great for onboarding—minimal friction—but costs scale with usage. The risk is that some customers might try to limit usage to keep costs down, which can undercut the inherent value of the software.

2. Compute/Consumption Model

• AWS Model: You’re billed for compute time, storage, and data transfer. If AI-driven workloads grow, they’ll naturally require more compute. This ties revenue to actual resource consumption rather than to the number of humans using the platform.

3. Reads vs. Writes

• One idea I’ve heard (and like) is making “writes” free and then charging for “reads.” Creating and storing new data is essentially free, encouraging more data in the system, while reading or analyzing that data is where incremental costs kick in. Slack’s approach to old messages—paying for historical access—kind of nods in this direction.

4. Freemium Hybrid

• Attract a broad user base with free or low-cost seats, but charge heavily for advanced features, more storage, or AI-driven insights. This can widen the top of the funnel without giving away too much value for free.

The Facebook/X Paradox

Though it’s a B2C example, Facebook’s model of free user creation but monetized reads (ads) is fascinating. X (formerly Twitter) is also experimenting with a subscription that effectively lets you pay for an ad-free reading experience. In the B2B world, we could see SaaS companies offering free seats for everyone to create and collaborate, yet charging heavily for higher-value interactions like analytics, AI-driven recommendations, or large-scale data queries.

Value capture will need to change

I’m personally convinced that system-of-record software will be more essential than ever, even if a company’s seat count drops. AI runs on data, and it needs somewhere to “live.” The question is how to monetize in a world where the number of human users may not be the best proxy for how much value is being created.

Sometimes, the answer might be usage-based or compute-based billing. In other cases, maybe it’s a more creative model that charges for action and insight rather than people. But if AI truly delivers on its promise to reshape the workforce, the per-seat pricing structure could look more like an artifact from a different era.

I suspect we’ll see more SaaS businesses shift to these alternative models in the next few years—partly due to macroeconomic forces and partly because AI is challenging every assumption about headcount-based metrics. If the seat count in a typical organization does begin to shrink, I believe those who adapt their pricing to reflect actual resource consumption, data access, or compute cycles will be in a better spot to survive and thrive.