COMMENTARY: For MSPs and channel partners, the question is simple: where should each AI workload actually run? As AI moves into production, cloud cost, compliance, latency, and data control matter a lot more. That creates an opportunity for partners to help customers decide when public cloud makes sense, when dedicated infrastructure does, and when a mix of both is the better fit.
The
Microsoft-OpenAI partnership amendment earlier this year, which ended Azure cloud exclusivity sparked a wave of new cloud strategy planning across enterprises running AI workloads.
Enterprises have built workflows around OpenAI solely in Azure, and now they're facing migration decisions they weren't planning for six months ago. Many CIOs now have to consider whether to stay on Azure, move to a new platform like AWS, or adopt multi-cloud strategies to hedge their bets across providers.
AI and ML workloads now represent 22% of total cloud costs at SaaS and IT companies, according to
CloudZero, and that spending is harder to forecast than traditional infrastructure because AI introduces non-linear usage patterns that break standard finance assumptions.
The conversation has centered on portability and flexibility between cloud providers, which are valid concerns when a partnership that defined AI infrastructure strategy suddenly changes. But before companies commit to another provider or layer on multi-cloud complexity, they should evaluate whether dedicated internal AI infrastructure that removes cloud vendor dependency entirely makes more sense for their critical workloads. For the MSPs and channel partners advising these companies, this shift is an opening to guide clients toward the right decision rather than the reflexive one.
The cloud flexibility trade-off
Public cloud environments offer undeniable advantages, including easier scalability, global accessibility, managed services that reduce operational overhead, and access to cutting-edge capabilities without building them in-house. For many organizations, these benefits justify the complexity and cost of adoption. But AI workloads introduce new considerations that don't necessarily align neatly with public cloud economics or governance models.
AI carries unique requirements around data residency, compliance, latency, and cost predictability that can work against the flexibility hosted platforms promise to offer.
Training models or running inference at scale generates massive compute and storage bills that fluctuate based on usage patterns organizations don't always control. Data transfer fees can accumulate quickly when AI workloads pull from datasets distributed across regions or require frequent movement between environments. Compliance frameworks in regulated industries often impose restrictions on where data can be processed, which limits the portability that multi-cloud strategies are supposed to deliver.
Especially for workloads that involve sensitive data or are foundational to business operations, public cloud can become a liability rather than an asset. A healthcare organization processing patient records through AI-driven diagnostics might find that HIPAA compliance requirements restrict which cloud regions they can use, that data residency rules prevent them from moving workloads when better pricing becomes available elsewhere, and that the multi-cloud portability they're paying for can't actually be used without triggering regulatory violations. Some companies end up paying for optionality they can't fully use because regulatory or cost constraints lock them into specific configurations anyway.
When dedicated AI infrastructure makes sense
If AI workloads process sensitive data subject to strict compliance requirements, keeping that data on-prem removes the complexity of navigating cloud provider jurisdictions and third-party policies. Similarly, if workloads require consistent and predictable performance where latency spikes could disrupt operations, dedicated infrastructure eliminates a number of variables introduced by shared public cloud environments.
Public cloud billing for AI workloads can be unpredictable, especially when usage scales rapidly or when workloads involve large datasets that trigger egress fees. We see that organizations running AI at scale often find the total cost of ownership for dedicated infrastructure compares favorably to ongoing cloud expenses. The difference becomes more pronounced over time as cloud costs compound while on-prem infrastructure remains relatively fixed once the initial investment is made.
Dedicated infrastructure gives companies better control over configurations without needing to navigate service agreements or wait for features to become available in specific regions. When AI is core to a company's competitive differentiation, that level of control can be strategically significant.
The questions that matter
The OpenAI-Microsoft shift is just one example of how quickly tech stack configurations can change. Organizations that built AI infrastructure strategies around exclusive cloud partnerships now face decisions they didn't anticipate, and many lack the internal expertise or operational capacity to evaluate these choices without external support.
For SMB and mid-market companies, the stakes are even higher because a wrong infrastructure decision compounds over time in ways that are expensive to reverse.
The assessment starts with understanding which workloads are actually AI-dependent and what they require to run effectively. A company running experimental AI projects has fundamentally different requirements than one running production systems with sensitive data or one that can't tolerate any downtime. Experimental workloads can stay flexible across providers and absorb some unpredictability. But production systems need tighter control, predictable performance, and a foundation that won't introduce risk as it scales.
Cost calculations obviously matter, but they need to be based on realistic timelines. Public cloud pricing looks attractive initially, yet total spend for dedicated infrastructure often becomes favorable after 18-24 months once egress fees and data transfer costs compound. Compliance and performance requirements also constrain flexibility in ways that undermine the portability benefits organizations think they're paying for. If regulatory obligations already limit a client to specific regions, multi-cloud strategies will likely deliver less value than the complexity they introduce.
Where MSPs and channel partners come in
Most SMB and mid-market organizations don't have the internal expertise to audit their AI workloads, model the true cost of each option, and navigate their compliance constraints all at once, which is exactly where managed service providers and channel partners can deliver real value. The strongest partners in this moment are helping clients ask better questions about where each workload should actually run based on how the business operates, what it can tolerate, and what it needs to protect. That often means recognizing when a client's needs have outgrown a simple advisory conversation and require a partner with deeper expertise across infrastructure, cloud migration, cybersecurity, resilience, and managed services to stand up the right environment.
Moving beyond cloud provider comparisons
For some organizations, public cloud remains the right answer. For others, dedicated infrastructure offers better control and economics. The organizations navigating this shift most effectively aren't asking "which cloud?" They're asking "where should these workloads run based on our operational reality?" and bringing in partners who can help them execute on the answer.
ChannelE2E Perspectives columns are written by trusted members of the managed services, value-added reseller, and solution provider channels or ChannelE2E staff. Do you have a unique perspective you want to share? Check out our guidelines here and send a pitch to [email protected]. 
