Footprints AI vs Kevel
Kevel is an API-first ad server, a set of building blocks retailers and marketplaces use to build their own ad platform. Footprints AI is a complete, physical-store-first retail media platform, live in weeks, with in-store media and closed-loop measurement built in. The difference: Kevel gives engineers the toolkit; Footprints AI gives the finished operation.
Last reviewed June 2026 · Comparison based on publicly available information.
Footprints AI vs Kevel, capability by capability
| Capability | Kevel public positioning | Footprints AI positioning |
|---|---|---|
| Core strength | Ad-serving APIsBuilding blocks, ad server, audience and console APIs, to build your own custom ad platform. | Retailer-owned RMNA complete platform, physical-store media, shopper intelligence and closed-loop measurement, ready to run. |
| Build vs buy | A toolkit your engineering team assembles into a platform, bringing your own data and models. | A finished operation, live in weeks, no build-and-maintain burden. |
| In-store media | Can extend sponsored placements to in-store screens via API; in-store measurement depends on the retailer’s own data and build. | In-store screens, audio, POS, shelf and Wi-Fi as a measured channel. |
| Audience data | Kevel Audience activates your first-party data; bring your own ML. | Audiences from 100% shopper transaction behavior, with missions, cohorts and propensity. |
| Measurement | Online events and SKU-level catalog attribution. | Closed-loop SKU and store-level attribution, incrementality-ready with test and control. |
| Open APIs | API-first by design; best-in-class developer control. | Open APIs plus a complete, managed operation across in-store and online. |
| Best fit | Teams with engineers who want to build a fully custom ad platform. | Retailers that want the complete operation and proof, without building it. |
Based on public Kevel product pages and public Footprints AI platform information as of June 2026. Framed by use case, not by unsupported feature claims.
Capability scorecard
| Capability | Kevel | Footprints AI |
|---|---|---|
| In-store as a first-class media channel | Partial | Full |
| Store Twin forecasting to plan & pace delivery | None | Full |
| Audiences from real shopper behavior (100% coverage) | Partial | Full |
| One campaign workflow across in-store + digital | Partial | Full |
| Proof of delivery & real-time pacing (in-store & online) | Partial | Full |
| SKU + store-level attribution (uplift-ready) | Partial | Full |
| Built-in AI insights (missions, cohorts, basket shifts) | Partial | Full |
| Enterprise scale: private AI, white-label, multi-tenant | Partial | Full |
| Open APIs + integrations (DSP, CDP, retail systems) | Full | Full |
| Privacy-safe architecture for global rollouts | Full | Full |
Scorecard summarizes the comparison above. "Full" means the capability is a first-class product layer; "Partial" means it is present through adjacent infrastructure, a single channel or bring-your-own-data; "None" means it is not positioned as a core capability. Kevel earns full marks on open APIs and on privacy-safe, cookieless architecture; the gaps are in physical-store media, shopper intelligence and a ready operation. Scoring as of June 2026.
What is Kevel?
Kevel provides API-first ad-serving infrastructure, the Retail Media Cloud, that retailers, marketplaces and publishers use to build their own ad platforms. Its building blocks include an Ad Server (decisioning, forecasting, reporting), Kevel Audience (first-party segmentation), Kevel Console (white-label self-serve UI) and a product catalog for sponsored listings. Customers include Ticketmaster, Yelp, Strava, Klarna and Edmunds.
Kevel's philosophy is control and ownership: its APIs integrate with the retailer's own data, ML models and BI systems, run first or second-price auctions, and extend placements to on-site, in-app, off-site and even in-store screens. It is privacy-focused by design, working off first-party data without storing or learning from it.
Kevel is genuinely best-in-class infrastructure, but it is infrastructure: a toolkit a retailer’s engineering team assembles into a platform, bringing its own data, models and operations. It is not positioned as a finished physical-store media operation with built-in shopper intelligence and in-store incrementality. Footprints AI is the finished operation rather than the building blocks.
Sources reviewed: Kevel ad server, Audience and Retail Media Cloud pages, June 2026.
What is Footprints AI?
Retail media is hard to operate, not just to serve. IAB Europe reports that retailers cite operational setup (54%) and cost (40%) as the biggest barriers to retail media. Footprints AI is built to remove that burden, not hand a retailer a toolkit to build from scratch.
Footprints AI is a next-generation retail media platform built first for the physical store. It treats in-store as a first-class, measured channel across screens, audio, POS, shelf and Wi-Fi, then extends across on-site and off-site. The platform spans five modules, Campaigns, Audiences, Sales & Shopper Insights, In-Store Measurement and Leads, so the retailer runs the whole operation in one place.
Where Kevel hands engineers the building blocks to assemble an ad platform, Footprints AI is the finished operation, physical-store-first, with audiences from 100% shopper behavior, in-store media and closed-loop SKU and store-level measurement built in, live in weeks. The operating promise: buy true reach, pay per view, get real sales attribution.
It operates at scale across 30+ retailers, 65 agencies, 550+ brands and 50M+ shoppers, tracking 5B+ shopping trips and 7,000+ campaigns, deployed as a white-label, multi-tenant platform a retailer owns, in-store and online.
The differences that matter
Building blocks vs finished platform
Kevel gives engineers APIs to build an ad platform. Footprints AI is the complete platform, live in weeks, no build required.
Physical-store-first media
Kevel can serve ads to in-store screens via API. Footprints AI runs the store as a measured channel: screens, audio, POS, shelf and Wi-Fi.
Measurement
Kevel tracks online events and SKU-level catalog attribution. Footprints AI adds closed-loop SKU and store-level incrementality with test and control.
Shopper intelligence included
With Kevel you bring your own data, models and BI. Footprints AI includes audiences from 100% shopper behavior and built-in insights.
Footprints AI proof, not promises
Footprints AI case studies include outcomes such as +390% sales uplift for Jack Daniel's, +55% sales lift for Persil, 10× ROAS for Mutti and +23.29% sales uplift for Coca-Cola. Across the platform, campaigns deliver 5 to 8× ROAS versus a roughly 2× industry average, with +25% new-to-brand buyers and +20% basket incidence among exposed shoppers.
Footprints AI campaign results, measured with closed-loop attribution. Incrementality is claimed only where the measurement design supports it. Figures as of June 2026.
Who should choose which?
Choose Kevel if
You have an engineering team and want maximum control to build a custom ad platform from APIs, on-site or in-app, owning every detail. Kevel is best-in-class infrastructure for that.
Choose Footprints AI if
You want a complete retail media operation, physical-store media, shopper intelligence and closed-loop proof, live in weeks, without building and maintaining the platform yourself.
The Footprints AI retail media stack
Retail Media Network Platform · build and scale a retailer-owned media business. In-Store Retail Media · turn physical stores into measurable media. Closed-Loop Measurement · connect exposure to SKU and store outcomes.
Footprints AI vs Kevel, answered
What is the main difference between Footprints AI and Kevel?
Kevel is API-first ad-serving infrastructure, building blocks to construct your own ad platform. Footprints AI is a complete, physical-store-first retail media platform with in-store media and closed-loop measurement built in. Kevel is the toolkit; Footprints AI is the finished operation.
Is Kevel a retail media platform or infrastructure?
Kevel is infrastructure: APIs for ad serving, audiences and a self-serve console that a retailer's engineers assemble into a platform, bringing their own data and models. Footprints AI delivers the platform, the data layer and the operation as one product.
Does Kevel support in-store retail media?
Kevel can serve ads to in-store screens through its APIs, but in-store measurement and store-context data depend on the retailer’s own build. Footprints AI runs in-store screens, audio and POS as a measured channel with SKU and store-level proof.
How fast can each go live?
Kevel timelines depend on the retailer's own engineering build on top of the APIs. Footprints AI is a ready operation that can go live in weeks, with in-store media, audiences and measurement included.
Is Footprints AI a good Kevel alternative?
For retailers that want a complete operation and closed-loop proof without building it, yes. For teams that want to engineer a fully custom ad server with total control, Kevel is a strong, focused infrastructure choice.
How does measurement compare?
Kevel supports reporting and catalog attribution around served ads and owned events. Footprints AI adds closed-loop SKU and store-level attribution with incrementality-ready test and control across in-store and online.
Skip the build, own the operation
Get a complete, physical-store-first retail media platform with closed-loop proof, live in weeks, on a platform you own.
Comparison based on publicly available information about Kevel as of June 2026. Kevel is a trademark of Kevel, Inc. Footprints AI is not affiliated with Kevel.
