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Solution providers were exploring AI as a broader service—not just access to a software tool—in 2024. Their offers could combine consulting, data preparation, infrastructure, implementation, and continuing monitoring or model updates. The examples show several possible ways to package and charge for AI, but no settled definition of “AI as a service” or standard pricing model.
What “AI as a service” meant in providers’ 2024 discussions
In a 2024 report, CRN described providers testing service offers that reach beyond licensing an AI application. Depending on the customer and project, the work could begin with choosing a use case and continue through data preparation, engineering, deployment, assurance, and operational support.
That was an emerging collection of approaches, not a standardized market category. A useful way to assess an offer is to ask what the provider will actually do, where the system will run, who will maintain it, and how the work will be charged.
What services providers were considering
| Provider | Examples described by CRN | What the examples illustrate |
|---|---|---|
| Virtusa | Consulting and engineering, proofs of concept and pilots, development toward production, AI assurance, monitoring, and curation of unstructured data. | A project can include custom development and quality work before and after deployment, rather than stopping at tool selection. |
| World Wide Technology (WWT) | Use-case definition, GPU-as-a-service, AI-platform-as-a-service, MLOps, managed data streaming and source management, and cloud-hosted infrastructure. | A provider may help at the strategy stage or supply and operate parts of the technical foundation. |
| Insight North America | Managed data, managed data services, and a managed NVIDIA platform; its Stephen Moss described a possible progression from managed data to managed AI. | AI operations depend on having data that can be prepared and maintained. |
| Cognizant | Discussion of consumption-based and per-user pricing examples, as well as the possibility of integrating AI into existing platforms. | Pricing and packaging were still questions to solve, not established market conventions. |
These examples are not a common service menu, and a provider’s mention of a capability does not establish that it is currently available under the same terms.
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Why data and ongoing operations matter
A deployed model is not necessarily a finished service. Virtusa’s examples included data curation and continued monitoring; WWT’s Neil Anderson described AI systems as “living breathing animals that you just iterate on constantly. They’re never done, is what we’ve learned.” The practical implication is to clarify whether a proposal covers post-launch data maintenance, monitoring, and updates—or only the initial build.
Insight’s Stephen Moss put the relationship between data work and managed AI plainly: “With managed data, we can get to managed AI. You can’t do managed AI and have no data.” In other words, AI operations may rely on an ongoing service foundation for data quality and availability, not merely a model or application.
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How the business model could work—and what remained unresolved
CRN reported, citing IDC, an AI market estimate of about $235 billion in 2024 and a projection of $631 billion by 2028. These are figures reported in the 2024 article, not a current measurement or a verified statement of market size today. The same report framed monetization as an open issue for providers.
Cognizant executive Rob Vatter pointed to different billing approaches in Microsoft products: Copilot for Security was described as consumption-based, while Microsoft 365 Copilot was per user. Those examples show that charging by usage or by seat were both in view; they do not establish a standard for managed AI, and the report did not provide a market-wide rate card.
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Providers also considered whether AI should be sold as a separate product or included in an existing platform. Vatter argued that useful integration could improve retention, while an additional charge for functionality customers did not value could frustrate them. The commercial question is therefore not only how to set a price, but whether the AI capability creates a clear enough benefit to justify a separate line item.
Virtusa also avoided the AIaaS label in part because some customers may associate “as a service” with giving up control or expecting indemnification. That concern makes the contract and operating responsibilities important: a label alone does not say who owns the data, controls deployment, handles incidents, or accepts risk.
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How to evaluate a managed AI proposal
The provider examples suggest practical questions for comparing proposals. They are evaluation dimensions, not an industry-standard taxonomy:
- Readiness and use case: Will the provider help define a useful problem, or is the customer expected to arrive with a production-ready use case?
- Scope: Does the work include advice, implementation, data preparation, infrastructure, assurance, and ongoing operations? Identify what is excluded.
- Deployment and control: Will the customer operate the system, will the provider manage it, or will it run on cloud-hosted infrastructure? Confirm where data and workloads reside and who administers them.
- Price basis: Is the fee per user, based on consumption, tied to outcomes, or bundled into another platform? Ask what triggers additional charges and whether usage can be forecast.
- Responsibility after launch: Who monitors quality and safety, maintains data, updates models, and responds when performance changes?
WWT described working with customers at different maturity levels, from use-case definition through infrastructure supply. Moss cautioned against pushing organizations into AI before there is a real solution: “We’re going to do ourselves a disservice … as an industry if we push people too fast into AI and we don’t give real solutions,” he said. “At that point in time, you’re selling stuff just to sell stuff.”
What this snapshot can—and cannot—tell buyers
The CRN reporting captures provider thinking and examples from 2024. It helps explain why “managed AI” could involve a continuing mix of data, software, infrastructure, and service work. It does not establish current 2026 prices, confirm that each named offer remains available, or show that providers converged on a single definition or commercial model. A buyer should therefore assess the specific scope, responsibilities, and terms in a current proposal rather than infer them from the category label.
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