Is All Supply Created Equal? The Case for Choosing DSPs Based on Model Intelligence vs Inventory
There’s a pitch making the rounds in mobile advertising right now; perhaps you’ve even heard it yourself. It goes something like, “All DSPs access the same supply, so it doesn’t matter which one you use or how many.”
If you’re a brand or agency marketer who doesn’t live and breathe in-app advertising, the logic seems sound enough. In actuality, it would be like telling your hungry spouse who has a specific craving for spaghetti carbonara that you’ve made eggs benedict for dinner instead because it’s all just butter and eggs.
Sure, the base ingredients might be the same. But now someone is sleeping on the couch.
The Access Fallacy
When advertisers treat DSPs like interchangeable conduits to the same pool of apps and impressions, you’re collapsing a genuinely complex decision into a commodity comparison. By that logic, all banks are the same because they all hold dollars. Yes, DSPs often buy overlapping inventory. The interesting part is what happens next.
Same User, Different Decision
Here’s the unavoidable truth: Two DSPs can look at the exact same impression (same user, same moment, same app) and make completely different choices.
One may bid $4. Another passes entirely. A third bids $11 because its model predicts that a specific user is worth three times the average price. What each model is doing is buying a predicted lifetime value from a specific person at a specific instant. And predicting that value accurately is the entire game.
This is where machine learning models diverge sharply, even when their inventory access overlaps. Every DSP’s model is trained on different data, shaped by different feedback loops, and tuned to different optimization signals. That produces genuinely different decisions and outcomes. Duplicate inventory almost never yields identical results. It means multiple models competing to find value differently.
The Portfolio Argument
Instead, we recommend treating DSP selection like portfolio building. In any probabilistic system, more models against a similar opportunity set increases the chance of finding incremental value. One platform may be stronger in specific audience clusters. Another may have an edge in certain creative-response segments or time-of-day patterns. This is how the most sophisticated advertisers approach campaigns.
The concern that two DSPs will simply “bid against each other” and inflate costs misunderstands how well-optimized models behave. In practice, models specialize and they converge on the users they’re most confident about and diverge elsewhere. If both are driving profitable ROAS, the system is working. The goal isn’t to replace one DSP with another. It’s to expand the outcome frontier.
The Closed Learning Loop Advantage
There’s a further dimension worth considering here: the difference between a DSP accessing supply and a DSP owning the feedback loop around that supply.
When demand and supply operate within a unified platform, your advantages compound. Signal loss decreases. Learning velocity increases. Every impression, bid, and conversion feeds back into the same system in near real-time. That kind of closed loop cannot be replicated by a third-party DSP buying through the same inventory indirectly. The data asymmetry is structurally unavoidable.
So No, Not all DSPs Are Created Equal
At the end of the day, our industry continues to support a multi-DSP ecosystem because advertisers actively and deliberately choose to run across platforms simultaneously. At this point in mobile advertising, the models are the product. Access is table stakes. The intelligence on top of it is everything. And that competition makes every DSP smarter.