As more organizations deploy artificial intelligence (AI) applications, a growing number are relying on managed AI inference servers to run them in production environments.
A report published by The Futurum Group notes that by the end of 2025, the managed AI services market surpassed the AI training market for the first time.
The managed inference market reached $23.1 billion at the end of 2025, while AI training accounted for $16.3 billion. Looking ahead, The Futurum Group projects the managed inference market will reach $1.1 trillion by 2030, representing a 36 percent compound annual growth rate (CAGR). By comparison, AI training spending is expected to reach $42.2 billion, growing at a 21 percent CAGR.
Inference spending surpasses training
An AI inference server is an integrated system that processes input data, such as text, images, or audio, to generate outputs. While it was widely expected that inference-related investments would eventually exceed spending on training, the shift has happened much faster than many anticipated.
At the same time, organizations are encountering a new challenge: rising inference costs. Gartner predicts AI inference costs per agentic workflow will increase more than fivefold through 2028, even as the price-performance of AI models continues to improve. This “inference paradox” occurs because AI agents repeatedly reason through tasks, consuming significantly more tokens as usage expands.
MSPs can help organizations manage AI infrastructure
Limited infrastructure and AI expertise are driving demand for managed AI inference services. Today, most AI inference workloads are hosted by cloud service providers. However, many organizations are also deploying inference models in public cloud or on-premises environments, often with assistance from managed service providers (MSPs).
Those MSPs may help customers optimize and track AI costs, manage infrastructure, and improve operational efficiency. Dell recently reported that its AI-centric server revenue grew 422 percent year over year in the first quarter of 2026. A significant portion of that demand is tied to deploying AI inference workloads in on-premises environments.
AI routers help MSPs route prompts based on performance and cost. In fact, Stripe recently acquired OpenRouter, a provider of AI model-routing technology, for $7.1 billion, highlighting the growing importance of this capability.
The next challenge: Where AI runs
AI inference models are rapidly expanding beyond the cloud to the network edge and handheld devices. As AI spreads across environments, workload placement becomes critical.
Many AI models initially deployed in the cloud will likely move closer to the edge over time. MSPs can help ensure AI models run in the right place for optimal performance and cost.
What was once a future opportunity is now a growing MSP services market. MSPs that delay building expertise in AI infrastructure, inference management, and cost optimization risk falling behind as customer demand accelerates.
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