Disrupting The Endpoint Era: Here’s What Is Next For The MSP Pricing Model

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Is The MSP Model At Risk? 

The Wall Street Journal recently reported, “The unemployment rate in the information technology sector rose from 3.9% in December to 5.7% in January, well above last month’s overall jobless rate of 4%, in the latest sign of how automation and the increasing use of artificial intelligence are having a negative impact on the tech labor market.” 

As I do with almost anything I read, I started to wonder how this may impact MSPs (consider this an occupational hazard, I guess). Is this just the beginning of a ripple effect that will eventually find its way to SMB customers? If so, how will this impact MSPs who rely on “per-seat” pricing and employee head count growth? Will MSPs be the driving force behind this AI adoption or will they get left behind? 

With more questions than answers, I turned to someone who I’ve always known to have the opposite fortune. That person is Jay McBain, Chief Analyst – Channels, Partnerships & Ecosystems at Canalys. As expected, he helped rationalize this story, which also led to a much deeper conversation around the MSP pricing model that has largely failed to evolve over the last decade.

The AI-Layoff Narrative

While blaming AI may make for a good headline, McBain believes that the data tells a slightly different story. “In 2023, there were 1,200 companies that laid off 528,000 people. In 2024, 547 tech companies have already laid off 152,000 people. But if you go back, the ‘Magnificent 7’—Apple, Microsoft, Google, Amazon, Meta, Nvidia, and Tesla—added hundreds of thousands of employees during the pandemic. It was an excessive hiring spree when money was free, interest rates were low, and the world was their oyster. Now, these companies are course-correcting.”

This period of zero interest rates has become known as the ‘ZIRP Era,’ and its fallout has impacted Silicon Valley and Hyperscalers alike. According to Jay and many others, this reset was bound to happen. “The excuse du jour is AI. IBM kicked this off, and a few others followed, saying they can find efficiencies. But the truth is, there’s no toolset or AI stack that can actually replace people at this point. We’re still in the early days. Companies are using AI as a justification, but these layoffs are really about cost-cutting after years of over-hiring.”​

Organizational Restructuring

While media outlets have been quick to bellring every time a layoff occurs, that is only half the equation. As Jay explains, “Even the layoffs you see today aren’t always pure job cuts. Salesforce lays off 1,000 people, but they have 1,200 job openings. It’s not about reducing headcount—it’s about shifting roles from legacy functions into AI-driven areas. Almost every company is hiring more than they are laying off right now.” 

When money was free, it seemed as if everything got the green light. Those days are now over and Big Tech is finding ways to adapt. According to McBain, “A lot of these layoffs are tied to companies shutting down their moonshot projects. When interest rates were low, tech companies could afford to throw money at things like self-driving cars, AR, or hardware divisions that never took off. Now, boards are demanding cost reductions, and many of those $300,000 ‘no-show jobs’—where people sat on the bench waiting for a project—are disappearing.”

Growth By Head Count 

Now that we’ve got a sense of reality around what’s happening to those ‘IT jobs’, it’s easier to comprehend the downstream effects on small businesses. Terms like ‘hiring freeze’ and ‘static team size’ are often used by pundits, but it seems as though those trends have yet to hit Main Street. Jay McBain clarifies, “SMBs don’t have the same pandemic hiring bloat that Big Tech did. They didn’t bring on thousands of extra employees just because money was cheap. So they’re not in the same situation where they need to slash headcount. If anything, many SMBs are still struggling to hire qualified tech talent, not cutting jobs.”

This is good news for MSPs, who have largely benefitted from customer head count growth as a means of new revenue. He went on to reiterate, “There’s no AI stack that can truly replace a human at this point. SMBs might experiment with AI-powered efficiencies, but they’re not at a place where they’re replacing people outright. The narrative that AI is causing mass layoffs just doesn’t hold up—especially outside of enterprise tech companies.”

Sunsetting The Endpoint Era 

While MSPs may not have to worry about AI-induced cuts just yet, it raises the question as to whether or not endpoint pricing is viable in this new age. Jay believes that “Pricing can’t be per person or per device. But it’s got to be predictable, repeatable, reliable, and scalable. If MSPs don’t rethink how they build their pricing models, they’ll be stuck in the old client-server mindset from 20 years ago.”​

This wouldn’t be the first time that MSPs are confronted with this urge to pivot. As a matter of fact, many chose to resist this urge the last time around, only to find themselves on the sidelines while SaaS ‘ate the world.’ As McBain recalled “In the cloud era, managed services couldn’t figure out how to monetize SaaS. There was never a dollar of Salesforce or HubSpot that we could reliably attach a monthly service to. We kind of missed an entire 10-year cycle by not adapting, and we can’t afford to do that again with AI.” 

Consumption-Based Pricing

Clues as to what this new pricing model looks like can be found all over tech. The SaaS industry has been pivoting to this consumption-based credit system over the past few years. Jay McBain agreed, “When Salesforce is charging $2 per transaction, and Zendesk is charging per resolution, it’s clear that AI and cloud services are shifting to micro-consumption. Customers aren’t going to pay twice—if their subscription price goes down, their usage-based costs go up.” 

He went on to say that “MSPs have to start thinking about a credit model. Hyperscalers are already doing this—AWS, Google Cloud, Microsoft all have billions in pre-paid credits from customers. The idea is that businesses don’t know exactly how much they’ll consume, but they’re willing to prepay for discounts. MSPs won’t be able to tack on fees to every AI or cloud transaction, but they can create predictable credit-based billing.”

No-Code Workflow Automation

Since it will be incredibly difficult to squeeze margin out of a $0.03 micro-consumption transaction, it opens up the question, as to where MSPs should look to add value? McBain believes that one area is workflow and process automation, which suddenly gets a lot easier thanks to AI. “Low-code was all about drag-and-drop workflows—what if this data drops over there, then triggers that? But AI is zero-code. Now, I can just say in plain English: ‘Take that order, update the inventory system, notify the finance system, and feed the marketing team the new customer info.’ AI handles the logic for you, which is a massive shift.”

“For example, a restaurant using Toast might get AI-driven automation that integrates ordering, finance, and marketing. It’s not about MSPs reselling the AI—it’s about helping customers connect those systems and ensure everything runs smoothly. MSPs that can help with integration and automation are going to be in demand.” While many MSPs hopped on the previous low code trend to automate customer business processes, these platforms had a lot of limitations and still required some advanced development skills to get right. No-code on the other hand is a near frictionless experience. If you’ve ever built a custom GPT in ChatGPT or an app in Cursor then you likely know what I mean. 

AI Governance & Security

While automating business processes sounds ideal, it’s not always that feasible for the typical MSP customer. This is especially true this early in the game, as many lag well behind the AI adoption curve. In the short term, Jay believes that “MSPs can also bring value by acting as a ‘federal government level’ data overseer. They’re the ones who can ensure AI models aren’t dumping private data into consumer systems, that workflows aren’t breaking, and that automation is aligned with compliance needs. 

He went on to express concern that “We’re going to miss the first three to five years of Agentic AI if we don’t step in as the security layer.”​ This was very similar to the sentiment of Jimmy Hatzell of Hatz AI, as I published just a few weeks ago. “We used to say in the early days of cybersecurity, ‘If you’re not involved with your customer’s security, someone else will be.’ It’s the same with AI. MSPs have a choice: Start working on it now and be part of the conversation, or risk being left behind as others take the lead.”

Moving Up Market 

For those MSPs that are willing to pivot off the endpoint pricing model and take a more consultative stance, there may be room in the elevator to head up to the next floor. Jay McBain described this opportunity perfectly as he stated, “If you want to move upmarket, it’s not about just being the ‘single throat to choke’ anymore—it’s about selling consulting services, design, and architecture. You’re not going to resell hardware or software at that level—it’ll likely go through a marketplace—but you can position yourself as the trusted partner that ensures everything integrates correctly. 

“Every medium-sized business—once you get to that 250-employee mark—has seven trusted partners, according to McKinsey. As you move into mid-market, enterprise, and government, there’s an opportunity for MSPs to play a complementary role, offering skills that no one else in the room has.” Earning the respect of those rooms does not always come easy, but you shouldn’t assume it’s impossible. If anything, moving up market gets harder until it doesn’t, as you suddenly find yourself with more resources and support than you know what to do with. This is also why MSPs love co-managed opportunities, as they allow for the burden of IT to be distributed across multiple parties. The challenge then becomes proving your value when the weight no longer lies squarely on your shoulders.

Conclusion

As the sun sets on the endpoint era, it’s important to reflect on what we’ve learned from the last few decades in the IT industry and prepare ourselves for this next cycle. This means understanding the consumption models, integrating AI, and offering services that provide ongoing business value—not just device management.