Before Anyone Compared Brands at All

Opening Scene

Before appliance comparison became a well-established consumer practice, many buyers simply purchased whatever brand a familiar local store happened to carry, without genuinely comparing available alternatives. Organizations choosing AI/ML infrastructure providers have historically followed this exact same pattern: defaulting to whatever provider they already had a relationship with, rather than genuinely comparing available options.

In Plain English

In AI and machine learning’s earlier cloud era, organizations often selected a provider for AI/ML workloads simply because that provider already hosted their broader infrastructure, without a deliberate, feature-by-feature evaluation of what each provider’s AI/ML services actually offered. As these services have matured and genuinely diverged in capability, this default, relationship-driven selection has become an increasingly costly habit to leave unexamined.

The Old Way

Before deliberate, systematic AI/ML provider comparison was a well-established organizational practice, selection often followed considerably simpler patterns:

• Organizations often selected an AI/ML services provider simply because it matched their existing, broader cloud infrastructure relationship.
• There wasn’t yet a well-established practice of evaluating AI/ML-specific capabilities independently from broader cloud infrastructure decisions.
• Genuine capability gaps between providers sometimes went unnoticed, since the selection was never actually driven by a feature-by-feature comparison in the first place.

Defaulting to an existing infrastructure relationship, without independent AI/ML-specific evaluation, is what deliberate, systematic comparison directly addresses.

What’s Changing (and Why AI Is the Reason)

1. Organizations increasingly evaluate AI/ML services independently from their broader cloud infrastructure relationship, recognizing genuine capability differences worth considering.
2. This connects directly to the multi-cloud strategy principles covered in this content library’s dedicated series, where best-of-breed selection sometimes means using a different provider specifically for AI/ML workloads.
3. As AI/ML capabilities have become an increasingly significant, differentiating factor in overall organizational strategy, the cost of an unexamined, default provider choice has grown correspondingly more significant.

The Metaphor, Fully Extended

The Appliance Showroom	Managed AI/ML Services Concept
Buying whatever brand a familiar local store happened to carry	Selecting a provider based on existing infrastructure relationship
Not genuinely comparing available alternatives	Not genuinely evaluating AI/ML-specific capabilities independently
A simpler pattern before comparison became standard practice	A simpler pattern before AI/ML-specific evaluation became standard
An increasingly costly habit as options genuinely diverged	An increasingly costly habit as provider capabilities genuinely diverged

Quick Recap

• Organizations have historically defaulted to their existing cloud provider for AI/ML services without independent evaluation.
• As AI/ML capabilities have matured and diverged, this default habit has become increasingly costly to leave unexamined.
• Best-of-breed multi-cloud principles can apply specifically to AI/ML service selection.
• Growing strategic significance of AI/ML capability makes deliberate evaluation increasingly worthwhile.

Where This Fits in the Series

Article 2 covered the historical pattern of defaulting to an existing provider relationship. Article 3 turns to the first specific category worth comparing: the test kitchen every brand provides.