There's Never Been a Better Time to Be an MSP

Let's start off with a little bit of a throwback. Remember when the cloud first started eating your reseller lunch? System hardware sales evaporated. The lucrative attached services that came with them went with it. The revenue model you'd built over the years suddenly needed a complete rethink. For many of you, that's exactly what converted you from resellers into MSPs in the first place. Necessity, meet invention.

Here we are again. Same movie, different decade, except this time, the disruption is running back toward you.

AI began its commercial life as a cloud story. Intelligence lived in massive server farms, behind API calls, metered by the token. You either helped clients access it safely and cost-effectively, or you watched from the sidelines. Either way, it wasn't a business you could build much on. Thin margins, high commoditization, no real differentiation.

AI just seemed like a hill too big to climb.

Token costs are now the number one business problem in enterprise AI. The average enterprise AI budget ballooned from $1.2 million annually in 2024 to $7 million in 2026, with inference costs consuming 85 cents of every dollar spent. Agentic AI, which chains reasoning steps and tool calls autonomously, doesn't grow token consumption proportionally; it grows it exponentially. Clients who dove in headfirst are now staring at monthly bills in the tens of millions. The economy is broken, and everyone knows it.

So, what can MSPs do? The solution to the broken cloud economics is, believe it or not, bringing workloads back on premises.

A new class of self-hosted autonomous agents, like the fabled OpenClaw, being the most prominent example, is now the most-starred repository in GitHub history with nearly 350,000 stars, running on local hardware that keeps data off the cloud, handles the 80% of inference that doesn't require a frontier model, and routes only the heavy reasoning work to paid APIs. What does this ultimately do? Token consumption drops dramatically, data privacy objections disappear, and control returns to whoever owns the infrastructure.

That infrastructure is now astonishingly affordable. Apple Silicon's unified memory architecture makes a Mac mini M4 with 64GB of memory capable of running a 34-billion-parameter AI model entirely in memory, something that previously required an NVIDIA multi-GPU rig costing $5,000 to $10,000. The Mac mini costs $599.

They're currently sold out nationwide.

Now, put all of these things together, and you have a genuine managed service opportunity. Not the "we help you use ChatGPT" kind, we're talking on-premises AI infrastructure that an MSP designs, deploys, and manages. A cluster of local inference nodes handling routine workloads, hybrid routing to cloud models for complex tasks, persistent memory, and automation running on infrastructure you built and understand. Real recurring revenue, real differentiation, and real client lock-in, because when a client's workflows are woven into infrastructure you architected, switching costs are substantial.

This also opens doors in healthcare, legal, and financial services, where data privacy concerns have kept AI adoption cautious. On-premises inference, with data that never leaves the building, is a compelling answer to an objection that cloud-first vendors simply can't address.

The evolving MSP looks at this moment and sees exactly what it is: the same opportunity that cloud disruption once represented, arriving from the opposite direction. The question isn't whether AI belongs in your service catalog; the question is whether you're going to architect it for your clients or let someone else do it.

If you're not already an Apple partner, this might be the right time to add that fruit to your computing portfolio. The hardware wave is already here.

For a while at the end of last year, the press was swollen with news about what went from being called Clawdbot to Moltbot and finally to OpenClaw within three days. There were huge security and privacy concerns with a tremendous technical learning curve to get started. While it was getting great press, OpenClaw also got a bad rap.

It’s creator, Peter Steinberger, has now gone to work for OpenAI ostensibly to create a friendlier version. OpenClaw itself was donated to an open-source foundation backed by some heavy hitters. And many, many other providers are introducing their own versions of this unique AI harness.

For a deeper dive into the details, check out the latest from AgenticMSP.

While many MSPs feared their value proposition was slipping away back when cloud first started emerging, now with all the billions and trillions of dollars we read about being invested, it is quite cool that it is the economics of this very thing that will drive AI back into our customers’ premises and back into the very center of the MSP’s wheelhouse.

Posted by Howard M. Cohen on May 18, 2026