Every major mediation platform now claims some version of “AI-powered optimization.” The claim is true in most cases, but it means different things depending on the network, and the difference decides how much of your waterfall actually benefits. Some platforms run machine learning on bid density and floor prices in real time. Others use AI mainly for creative generation or fraud filtering, with the core auction still running on rules set months ago.
This guide breaks down which mobile ad networks are using AI where it actually moves revenue, what each one is good at, and where the gaps sit so you can build a stack instead of guessing.
Why UndrAds Isn’t on This List
| Before the network breakdown, one distinction is worth making upfront: UndrAds is not an ad network, and it won’t show up as a line item competing with AppLovin or Unity for auction space. It’s an AI layer that sits on top of whatever mediation setup you already run, watching performance across your existing networks and reacting to drops before they compound. The reason this matters here is that every network below has the same blind spot. AppLovin’s AXON needs weeks to retrain on a shift. Unity LevelPlay’s auction ranks the networks you’ve already connected, but nothing in that ranking watches for a demand partner underdelivering at 2 AM in a timezone your team isn’t awake for. That gap between what a network’s own optimization covers and what happens in the hours nobody’s watching is what autonomous ad ops exists to close, and it’s a separate decision from which network wins your auction. Worth keeping in mind while reading the comparison below. |
What “AI-Powered” Actually Means in an Ad Network
Three distinct things get bundled under this label, and mixing them up leads to picking the wrong network for the wrong reason.
Bid optimization models learn from your app’s historical auction data (which networks win at which times, at which floor prices, in which geos) and adjust bidding behavior without a human resetting rules. AppLovin’s AXON and Moloco’s DSP fall here.
Mediation and waterfall optimization uses ML to rank ad sources dynamically instead of a static waterfall order. Unity LevelPlay’s auto-optimization algorithm and AppLovin MAX both work this way, comparing eCPM performance across networks in real time rather than relying on a manually ranked list.
Creative and targeting AI covers things like automated creative variant generation, contextual matching, and playable ad personalization. This is where Mintegral’s optimization models and most UA-focused AI tools operate.
A network can be strong in one category and weak in another. Knowing which one you’re evaluating for matters more than the “AI-powered” label itself.
The Networks Worth Evaluating
AppLovin MAX
AppLovin now commands 39% of iOS ad revenue share and controlled roughly 73.1% of ad share among the most-downloaded mobile games in 2025. Its bidding model, AXON, runs a hybrid auction where real-time bidders compete against a waterfall, with AppLovin’s own exchange demand deeply integrated into the ranking.
The tradeoff is ramp time. AXON typically needs 2 to 4 weeks for integration and 60 to 90 days for meaningful optimization, and smaller apps without enough volume to train the model see slower gains. Pricing runs on take rates rather than a flat CPM, which complicates margin forecasting for finance teams used to fixed line items.
Best fit: casual, hybrid-casual, or mid-core studios with meaningful iOS scale and 60+ days of patience before judging results.
Unity LevelPlay
LevelPlay is the rebranded ironSource mediation stack combined with Unity Ads, and it remains the natural pick for any game built on the Unity engine. Its auto-optimization algorithm compares eCPM performance across mediated networks in real time rather than relying on a fixed order, and playable installs through Unity Ads run around $0.60 on Android versus roughly $2.50 for standard video on the same network, a meaningful cost advantage for studios leaning on playable formats.
Editor-level integration is the other draw. Setup happens directly inside the Unity Editor rather than through a separate SDK implementation process, which shortens the path from build to monetized release.
Best fit: Unity-built titles, studios already inside the Unity ecosystem, teams running playable ad formats at volume.
Moloco
Moloco crossed a $1 billion annual revenue run rate as advertisers moved budget toward programmatic inventory with stable ROAS. Its DSP applies machine learning primarily to user acquisition targeting and bid shading rather than publisher-side waterfall management, so it fits studios buying installs more than studios optimizing ad revenue on existing traffic.
Best fit: UA-heavy teams running performance campaigns, particularly where ROAS stability matters more than raw CPI.
Mintegral
Mintegral processes more than 100 billion ad requests and serves over 1 billion impressions daily, with AI-driven models matching creatives to player behavior patterns across more than 26,000 app partners and 20-plus international ad exchanges. Its playable ad format has shown up to 20% higher revenue compared to standard video in puzzle and casual genres specifically, where interactive previews convert advertisers better than passive video.
The regional strength is APAC. Mintegral’s advertiser base lifts blended eCPMs by 12 to 18% for studios expanding into anime, strategy, and mid-core audiences in Japan, Korea, and Southeast Asia, a demand pool most Tier-1-focused networks miss entirely.
Best fit: studios with real APAC user bases, casual and puzzle genres running playable formats.
Liftoff and Vungle Exchange
Liftoff’s combined offering pairs its DSP with Vungle’s programmatic exchange, positioning it as an AI-powered DSP built for performance at scale. Like Moloco, its AI weight sits on the UA and targeting side rather than publisher-side waterfall optimization, making it a complement to a mediation stack rather than a replacement for one.
Google AdMob
AdMob remains the most widely integrated network by SDK footprint, used by studios ranging from Niantic to Supercell for its stability and broad platform support. Its AI involvement is lighter than the networks above, mostly automated bidding within its own exchange and standard mediation reporting, rather than a proprietary model retrained on your specific inventory. For most studios, AdMob functions as one demand source inside a broader mediation layer (like LevelPlay or MAX) instead of the primary revenue engine. A full breakdown of how AdMob works and where it fits is worth reading before deciding how much of your waterfall to route through it, and studios finding its limits worth checking AdMob alternatives built for higher-scale inventory.
What Switching Actually Costs
Every comparison above assumes you can test freely, but mediation platforms are not interchangeable overnight. Migrating means an SDK swap, adapter reconfiguration, and a reporting pipeline change, and the new platform’s model needs time to learn your app’s specific auction behavior before it optimizes anything. During that window, eCPMs can drop 15 to 30% before recovering, which on a $10K/month app translates to real dollars lost during the test itself.
The practical answer most experienced ad ops teams land on is running two mediation layers in parallel on a subset of placements before committing broader allocation, measuring revenue per DAU rather than fill rate or eCPM in isolation. That takes discipline most lean teams don’t have bandwidth for, which is a separate problem from picking the right network.
Building the Right Stack
Most studios end up running two or three networks rather than one, since demand density varies by geo and format enough that a single network rarely wins every auction. A reasonable starting stack for a casual mobile game looks like AppLovin MAX or Unity LevelPlay as the primary mediation layer, AdMob as a secondary demand source, and Mintegral added specifically for APAC-heavy titles. UA-side tools like Moloco or Liftoff sit alongside this, not inside it, since they’re solving install acquisition rather than in-app monetization.
The mistake worth avoiding is treating “AI-powered” as a single feature to check off. Ask which of the three categories above a network’s AI actually touches, how long its model needs to learn your inventory, and what happens to your revenue in the weeks it’s still learning.
FAQ
Do AI-powered ad networks eliminate the need for a mediation setup? No. AI optimization happens inside a mediation layer, it doesn’t replace the need to configure one. Platforms like Unity LevelPlay and AppLovin MAX are mediation platforms first, with AI models optimizing the auction that runs inside them.
How long does it take an AI-powered network to actually start optimizing? Most models need real usage data before they improve anything. AppLovin’s AXON typically needs 2 to 4 weeks to integrate and 60 to 90 days to reach meaningful optimization. Expect a learning window with any network switch, not instant lift.
Is AppLovin or Unity LevelPlay better for a Unity-built game? LevelPlay integrates directly through the Unity Editor and is the default choice for Unity titles, while AppLovin’s edge comes from AXON’s bidding model and iOS scale. Many studios run both, using each for different placements or markets rather than picking one exclusively.
Which network is best for studios with a large APAC user base? Mintegral has the deepest APAC advertiser relationships among the major networks, with reach into Japan, Korea, and Southeast Asia that lifts blended eCPMs 12 to 18% for studios with that geographic mix.
Do I need a separate tool to catch performance drops between network optimization cycles? If your team reviews performance every few hours rather than continuously, there’s a real gap between a drop happening and someone reacting to it. That’s a monitoring and reaction problem, distinct from which network you route demand through, and it’s what autonomous ad ops is built to close.
Can I run more than one AI-powered network at the same time? Yes, and most studios do. Running two mediation layers on a subset of placements before shifting broader allocation is the safer way to compare performance without risking a full revenue dip during the switch.
Get a Free Ad Ops Audit
If you’re running one or more of these networks already and want to know how much revenue is leaking between optimization cycles, talk to the UndrAds team about a free ad ops audit on your current setup.
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