Why Token-Based AI Pricing Fails MSPs (And What We Do Instead)

Описание: Most MSPs operate on predictable, recurring revenue — but many AI tools introduce unpredictable costs.

In this video, Director of Growth at Thread, Bobby Jacobs, breaks down why token-based and consumption-based AI pricing models create real challenges for MSPs and other service businesses. While usage-based pricing may be easy for vendors, it often shifts financial risk downstream to partners who need stable, known costs to scale.

We share why Thread believes AI pricing for MSPs has been built the wrong way, how recurring revenue models change the economics of AI adoption, and why we chose to license by customer instead of by token usage — even when that decision wasn’t easy.

You’ll also hear how MSPs can think about standardization across customers, why small customers shouldn’t block AI adoption, and how predictable pricing enables MSPs to confidently deploy AI across their entire service desk without worrying about billing spikes.

If you’re an MSP evaluating AI tools and want to understand how pricing impacts adoption, margins, and long-term growth, this conversation is for you.

👇 Chapters / Topics Covered:
Why MSP revenue models require predictable costs
The problem with token-based AI pricing
How AI vendors pass risk downstream
Why Thread licenses by customer, not consumption
Standardizing AI across all MSP customers
How AI ticket credits make pricing predictable