Is All Supply Created Equal? The Case for Choosing DSPs Based on Model Intelligence vs Inventory

The article debunks the prevalent claim that all DSPs are equal because they access the same supply. It argues that while inventory may overlap, the machine learning models behind each DSP produce fundamentally different decisions. Two DSPs evaluating the same impression (same user, same moment, same app) can bid $0, $4, or $11 based on their predicted lifetime value for that user. These predictions diverge due to different training data, feedback loops, and optimization signals, meaning duplicate inventory rarely yields identical outcomes.

Key insight: DSP selection should be treated as portfolio building. In any probabilistic system, using multiple models against the same opportunity set increases the chance of finding incremental value. One DSP may excel in certain audience clusters, another in creative-response patterns or time-of-day segments. The concern about self-competition inflating costs is misplaced because well-optimized models specialize—they converge on users they’re confident about and diverge elsewhere. If both drive profitable ROAS, the system is working.

A further advantage emerges when a DSP operates within a closed ecosystem that owns both demand and supply. Such unified platforms benefit from reduced signal loss, faster learning, and real-time feedback loops—advantages structurally unavailable to third-party DSPs buying indirectly. This data asymmetry is unavoidable.

Actionable takeaway: Advertisers should not replace one DSP with another but run multiple simultaneously. Adopt a portfolio mindset, measure each platform’s contribution to the overall outcome, and leverage specialized strengths. The intelligence atop access is what matters; competition among DSPs pushes every model to improve.