AirDNA has announced Adapt, a dynamic pricing tool the company describes as AI-native and built from the ground up on its market dataset. A waitlist opened on April 29th, with general availability expected sometime this summer.
The launch marks AirDNA's most direct move from market intelligence into revenue management. The company has offered limited pricing features in the past, but Adapt is a full entry into the space. CEO Rohit Bezewada called it "the biggest bet we've made" in a LinkedIn post, arguing that existing pricing tools were "built in a different era" and that "dynamic pricing deserves to be rethought from the ground up for the agentic era, not retrofitted into it."
How It Works
Adapt is built in four layers. The foundation is AirDNA's dataset: 15 million listings tracked daily across Airbnb, Vrbo, and Booking.com, processing more than a terabyte of data per day. On top of that sit machine learning models trained on a decade of historical and pacing data. An adjustment layer lets operators set rules and choose from preset strategy playbooks. An AI layer ties everything together, which AirDNA says will make pricing easier to set up and adjust.
The company is betting that data depth gives Adapt an edge standalone pricing tools can't match. AirDNA’s Chief Economist Jamie Lane described the announcement as intentionally early, noting the tool is still a couple months from launch and that AirDNA wants host and property manager input while it's being built.
Where It Stands
AirDNA's data infrastructure is well established. But entering the dynamic pricing market means competing with tools that have years of pricing-specific iteration and deep integration networks. PriceLabs, for example, connects to more than 150 property management systems. AirDNA hasn't published a confirmed integration list for Adapt, and currently offers Airbnb sync through its earlier Smart Rates feature.
The product isn't available yet and concrete details are limited, so there's nothing to act on today. But AirDNA entering this space with its full dataset behind it is a development worth tracking as it gets closer to launch.
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