What is OpenRTB?

OpenRTB is an IAB Tech Lab protocol for real-time bidding (RTB). It defines how supply-side platforms send structured bid requests and how demand-side platforms reply with bids, creatives, and metadata. The goal is one shared format so exchanges, DSPs, and SSPs can trade impressions in milliseconds without custom one-off APIs for every pair of systems.

What happens in an OpenRTB auction?

When a user loads a page or app with ad slots, the publisher side packages context into a bid request: site or app identifiers, impression sizes, user/device hints, regulations, and optional extensions (such as supply-chain data). The SSP broadcasts that request to eligible DSPs.

Each DSP evaluates the request against advertiser rules, bids or passes, and returns a bid response with price, creative markup, and tracking. The SSP runs the auction (often second-price), notifies the winner, and returns the ad to the client. The whole round trip typically must finish within a tight timeout.

What sits inside a bid request?

Common objects describe the impression, publisher property, device, geo, and privacy signals. Buyers use those fields for targeting, frequency caps, and brand-safety filters. The exact shape changes by OpenRTB version; mobile, video, native, and CTV placements add their own objects.

OpenRTB is not the only way to buy media (deals, programmatic guaranteed, and walled gardens use other workflows), but it is the main language of the open auction web and in-app RTB.

Why OpenRTB matters for fraud and wasted spend

Standard fields make it easier to apply policy at scale: block bad categories, enforce geography, read supply transparency signals, and route spend toward verified paths. They do not guarantee truth. Fields can be wrong or manipulated, which is why ad fraud and invalid traffic remain separate problems.

Click fraud on search or social is a different stack, but the same discipline applies: treat reported context as hints, then validate outcomes. Teams often pair buy-side hygiene with how fraud is detected in analytics and with click-level tools that flag suspicious clicks. For background on tactics in display and video, see ad fraud techniques and display ad fraud.

Device and network context (for example OS and environment signals) still matter for separating real users from automation.