We're trying to make useful AI skills available to the whole company while keeping costs manageable. Right now, we have some Copilot licenses and a limited number of Claude and ChatGPT enterprise licenses assigned to selected employees. However, employees are creating skills that may eventually need to be available company-wide, and assigning an individual license to every potential user would likely be rejected due to cost.
One option I'm investigating is rebuilding those skills with models deployed through Azure AI Foundry, then exposing them through an internal application or API. That could let employees use centrally managed AI tools without each person needing a separate Copilot, Claude, or ChatGPT license. Some use cases may eventually become ordinary scripts without an AI component, but others will still require a model.
For basic tasks such as proofreading, our Microsoft 365 E3 users already have the included Copilot functionality. The issue is access to more advanced features, such as the Microsoft 365 Copilot work experience, which requires an additional per-user license. We're also beginning to formalize AI governance, with IT owning company-wide skills and controlling how they are deployed.
Has anyone compared this approach in practice? For example, would deploying Claude Opus, Sonnet, or another model through Foundry and charging by API usage be more cost-effective than maintaining individual enterprise licenses? What architecture, licensing limitations, security considerations, and usage controls should we account for?
4 Answers
A useful comparison is total cost per completed workflow, not just the model’s token price. Include development and maintenance, hosting, networking, monitoring, security reviews, prompt and retrieval engineering, support, and the cost of failures or human review. A per-user license may be cheaper for broad everyday use, while a Foundry application can win for a smaller number of well-defined, high-value workflows.
You could pilot one or two skills, measure actual usage and quality, and compare that with the equivalent enterprise-license cost before attempting a company-wide rollout.
Separate the use cases before deciding on a platform. Generic tasks such as proofreading, summarization, and drafting may already be covered by the AI features in the productivity applications employees use. Those features also generally come with a clearer enterprise governance model than an ad hoc tool.
For specialized workflows that use internal data, business rules, retrieval, or automated actions, a Foundry-backed service makes more sense. It lets IT control the prompts, model access, data connections, and release process instead of giving every employee unrestricted access to a powerful model.
Be careful not to assume that an API-backed internal application automatically avoids all licensing obligations. Foundry model access, Microsoft services, third-party model terms, identity features, data storage, and any downstream application may each have separate commercial and usage conditions. Confirm with your Microsoft account team and the relevant model provider that your proposed employee-facing scenario is permitted.
From a governance perspective, treat each skill as an internal product: define an owner, approved data sources, retention rules, audit logging, acceptable-use boundaries, evaluation tests, and a process for handling model changes. A centrally hosted service can reduce seat management, but IT still needs controls to prevent uncontrolled usage and unexpected spend.
Foundry is a reasonable fit when you’re building domain-specific workflows, exposing AI through an internal API, or placing a controlled application in front of the model. In that setup, the organization owns the application and pays for model consumption rather than assigning a separate chat license to every employee.
The savings depend heavily on usage. You’ll need to estimate prompts, completion tokens, peak concurrency, document processing, tool calls, and any supporting services. A shared API can be cheaper than per-user licenses when usage is light or uneven, but it can also become expensive if everyone uses it like a general-purpose chatbot. Put quotas, authentication, logging, rate limits, and chargeback or departmental budgets in place from the beginning.
That’s the direction we’re considering. The goal isn’t necessarily to eliminate AI spending, but to replace fixed per-user licensing with centrally managed API consumption and make the company-wide skills available through an approved internal front end.