Types of media buying: five models and the two questions that separate them

There are five main types of media buying today: an in-house team, a traditional agency, a DSP or programmatic setup, smart bidding inside an ad platform, and autonomous AI media buying. They aren’t five speeds of the same thing. Each one gives a different answer to two questions: who makes the buying decision, and who takes responsibility for the outcome if it doesn’t arrive.

The five types of media buying

The five models differ in who makes each buying decision and how far that decision can reach.

In-house team. People inside the brand plan, buy and optimize. They have full context and direct control, but the hours in a day limit them.

Traditional agency. An external team runs the same process at more scale, across more platforms, usually on a weekly or monthly review cycle.

DSP or programmatic. Software bids in real time inside the auctions is connect to, while people set and review the strategy around it.

Smart bidding. An ad platform’s own algorithm optimizes toward a goal inside that platform’s ecosystem.

Autonomous AI media buying. A decision layer decides and executes across channels, continuously, inside boundaries defined up front.

Question one: who decides

The first difference between media buying models is who makes the decision, and how often it can be made. In-house and agency models, people decide. In programmatic and smart bidding, software decides inside one environment. In autonomous media buying, a system decides across environments.

That isn’t a ladder from manual to advanced. A person approving a change and an algorithm making it in milliseconds are different arrangements, each with its own boundary. Many brands run several of these models at the same time, which is a management question of its own, covered in how to run several media buying models at once.

Question two: who takes responsibility for the outcome

The second difference, and the one comparisons often skip, is who answers for the result when a decision turns out to be wrong. In most setups, that responsibility is defined by habit: the team owns it, or the agency relationship absorbs it, or the platform’s reporting explains it.

It can also be defined explicitly, in a contract, with a number and a consequence attached. That changes more than the paperwork. It changes what the provider is incentivized to build, a point we develop in AI accountability in marketing.

Why the two questions matter more than speed

Comparing media buying models by speed alone hides the trade-off that actually matters. Speed tells you how fast a decision is made, not who stands behind it.

Our position at Mainkore is straightforward: the industry has automated the first question much faster than the second. Decisions are now made in milliseconds, while responsibility for the outcome is still often informal.

Mainkore is autonomous advertising decision intelligence. It operates above DSPs and ad platforms, decides across channels inside the boundaries each advertiser sets, and has done so across more than 12,000 campaigns. On the second question, our answer is explicit: we’re so confident in our technology that we back its performance contractually. You can see what that means in practice in what autonomous AI in advertising actually is.

FAQ

The full comparison, all five models across eight criteria and no vendor names, is in our media buying models guide. Want to see how your current setup answers both questions? Talk to us.

Mainkore. Two questions, answered every time: who decides, and who answers for it.