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An AI capability may look like a feature in your product, but its behavior and availability can depend on a model, an inference service, prompts, data, connected tools, and the processes used to monitor and update them. You may not control every part of that chain. You do control important choices around integration, data handling, fallbacks, and how readily you can change providers.
What sits behind an AI feature?
The visible interaction—such as a generated answer, recommendation, or automated task—is only the user-facing part. Keeping it reliable can require maintaining the model and its interface, the prompts and evaluation process, the data path, integrations with other services, and operational controls. The exact arrangement varies by product: not every AI feature uses the same components or external providers.
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Microsoft’s Azure Well-Architected guidance warns that AI introduces ongoing maintenance burdens for models, tools, and data, and recommends lifecycle management, evaluation, prompt iteration, and adapting to technology changes. Microsoft’s AI workload guidance treats this as continuing product and operations work, not a one-time integration.
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“You don’t control” is a useful warning, not a literal description of every decision. A team typically cannot dictate how an external provider operates its model or service, but it can define how the product uses that service and what happens when conditions change.
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- Provider-controlled: the external model or platform’s implementation, availability, and changes, to the extent set by the service and agreement.
- Team-controlled: integration boundaries, prompt and evaluation practices, data selection and routing, product behavior, monitoring, and fallback design.
- Shared or contract-dependent: operational and security responsibilities. The actual allocation depends on the deployment model and applicable agreement.
Microsoft’s shared responsibility model distinguishes duties across SaaS, PaaS, and IaaS: providers and customers carry different operational responsibilities in each. It is governance guidance, not a substitute for the contract governing a specific service. See Microsoft’s shared responsibility overview.
What can fail, and what happens next?
A dependency becomes a product risk when a failure or change in one component disrupts a customer-visible workflow. Google Cloud’s reliability guidance notes that component failures in production AI and ML systems are unavoidable, and recommends designing for graceful degradation: essential functions should keep working, potentially with reduced performance. Google Cloud’s AI and ML reliability guidance discusses monitoring and fallback approaches such as simpler models or cached data.
For each AI-powered workflow, make the consequence of a dependency failure explicit:
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- If a tool or data source cannot be reached, can the product still provide a useful result without silently implying that it has current information?
- If output quality changes after a model or prompt update, how will the team detect the regression and decide whether to roll back or adapt?
- If the feature cannot safely continue, can the rest of the product remain usable?
These are design questions, not promises that any particular fallback will suit every use case. A cached answer, for example, may be inappropriate when freshness matters; a simpler model may not meet the same quality bar. Define what reduced service means for the feature and when to stop rather than return a misleading result.
How portable is the feature?
Portability is a practical switching question: how much work, cost, and time would it take to move the capability to another provider or implementation? The UK Government advises teams to weigh the value of a service against portability, estimate exit costs and timing, and prepare for possible provider changes. Its guidance puts the tradeoff plainly: “An exit strategy should be balanced between the impact of changing provider and the benefit of staying.”
A sophisticated prebuilt model can provide substantial value even if it is difficult to replace. That is not automatically a reason to avoid it. The decision is whether the value justifies the resulting dependence and whether the team understands the consequences of leaving. The same government guidance advises product teams to make decisions with the possibility of changing provider in mind. See UK Government guidance on defining a cloud journey.
Modular components and clear interfaces can make dependencies easier to isolate and failures easier to contain, as Google Cloud’s reliability guidance explains. But abstraction is not free: supporting multiple providers can add engineering, testing, and operational complexity. Choose the degree of portability to match the impact and likelihood of a forced or beneficial switch, rather than treating a multi-provider design as a universal requirement.
Map the dependency before it becomes urgent
For each AI capability, document the chain that must work and the decisions that remain yours. Keep the map specific to the workflow, because the data, tools, and fallback needs may differ across features.
- Trace the path: identify the model or service, integration layer, prompts, tools, data inputs, and operational controls involved in producing the user-visible result.
- Assign responsibility: record which duties belong to your team, the provider, or both, then check the applicable service agreement for binding terms.
- Set data boundaries: list what data leaves your organization’s systems, where it goes, and what governance and traceability the workflow requires.
- Define failure behavior: decide how the product responds to unavailability, latency, inaccessible tools or data, and unacceptable output quality; document any fallback and its limits.
- Monitor and evaluate: establish how the team will notice operational problems or quality changes and assess model, prompt, and data updates over time.
- Estimate an exit: record what must change to switch provider or implementation, the likely work and timing, and which product behaviors or data flows make migration difficult.
Review the map when the feature, its provider arrangement, or its importance to the product changes. A low-impact assistant and a workflow that blocks a core customer task do not need the same resilience or exit investment.
Choose a proportionate exit plan
An exit plan need not mean building a second production integration immediately. It can begin with knowing what a switch would involve and keeping the product boundary clear enough to make that choice later. For a high-impact capability, teams may justify more investment in tested fallbacks, portable data and prompts, or a migration path. For a lower-impact feature, documented dependencies and a credible estimate may be proportionate.
Use the tradeoff explicitly: compare customer value with switching cost, operational burden, resilience needs, and data-governance requirements. The goal is not to eliminate dependence; it is to understand it well enough to choose, operate, and—if circumstances warrant—change it.
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