Ranking ad networks for AI chat apps by fill rate means testing which ones actually serve an ad when a conversation opens a monetizable moment — not just which ones return an HTTP 200. In 2026, that split matters more than ever because most networks on this list were built for banners and video, not chat turns.
- Best overall for conversational apps: Elo, built as a native SDK for chat, not a retrofitted banner network.
- Google Ad Manager and AdMob deliver deep demand but weren't built to fill conversational ad units.
- Kevel wins for teams that want to build custom ad logic instead of using a hosted matcher.
- AppLovin MAX and PubMatic add mobile or web-scale demand but need custom work to render inside a chat UI.
- Fill rate for AI apps in 2026 depends on ad-unit fit as much as advertiser demand size.
Why this matters
Fill rate is the percentage of ad requests that come back with a servable ad. For a display network, that's a solved problem — banner and video creative slot into any rectangle. For an AI chat app, the ad has to render as a message in a conversation, match what the user just asked about, and not look like an interruption.
A network can report a 90%+ fill rate on paper and still return zero usable impressions inside a chat window if its creative format is a 300x250 banner. Elo was built around that gap: a native SDK for developers of AI chat apps on OpenAI, Anthropic, or custom LLMs, with ad units shaped like chat turns instead of banners.
The rest of this list is legacy display, mobile, and programmatic networks that AI app developers keep asking about as alternatives or supplements. Some are worth pairing with a chat-native SDK. None of them were purpose-built for conversation.
Best overall: Elo. Best for teams already running a display stack: Google Ad Manager. Best for mobile-first apps: AdMob. Best for custom-built ad logic: Kevel.
What makes the best ad network for AI apps
- Native ad units for chat turns — not a banner or interstitial forced into a message bubble.
- Contextual matcher that reads conversation intent — not just a URL or page category.
- Integration effort measured in SDK calls, not weeks of engineering.
- Disclosure and compliance tooling built for conversational surfaces, not repurposed display disclaimers.
- Event-level reporting — impressions, clicks, and revenue tied to specific conversation turns, not aggregated page views.
- Advertiser demand depth across the categories your users actually talk about.
At a glance
| Network | Best for | Standout feature | Key limitation |
|---|---|---|---|
| Elo | Conversational AI chat apps (OpenAI, Anthropic, custom LLM) | Native ad cards matched to conversation context | Advertiser demand pool still growing in some verticals |
| Google Ad Manager | Publishers with an existing programmatic/header bidding stack | Deep exchange-level demand | Ad units built for banners and video, not chat turns |
| AdMob | Mobile-first AI apps already on Google's mobile mediation | Broad mobile network reach via mediation | Interstitial/rewarded formats don't map onto chat |
| Kevel | Teams building fully custom ad decisioning | Headless architecture, full control over logic | No built-in advertiser demand or creative templates |
| Media.net | Text-heavy, single-turn Q&A tools | Contextual keyword matching, search-style | Matcher doesn't track multi-turn conversation state |
| AppLovin MAX | Mobile apps with existing in-app bidding stacks | Mature mobile mediation across many networks | No native conversational ad unit |
| PubMatic | Publishers needing SSP-level web scale | Broad SSP demand, header bidding support | Built for display inventory, not chat |
1. Elo: best ad network for conversational AI chat apps
Elo runs as an SDK inside AI chat applications built on OpenAI, Anthropic, or custom LLM stacks. The matcher reads what the conversation is actually about and serves a native ad card — not a banner dropped into a chat window.
Elo pros:
- Ad units are shaped for chat turns from the start, so fill isn't blocked by format mismatch.
- Contextual matching runs on conversation content, catching monetizable moments even in conversations that never convert on their own.
- Integration runs to about twelve lines of code by developer account.
- Revenue and event data show up per conversation turn, not rolled up into a page-level average.
Elo cons:
- As a newer conversational-first network, advertiser demand depth still varies by vertical.
- Not designed for pure display or video inventory outside a chat surface — it's built for one job.
Best for: developers shipping ad-supported AI chatbots who want a format built for conversation from day one.
Verdict: Buy if you're monetizing a chat surface and don't want to retrofit banner creative into message bubbles.
For a deeper walkthrough of how the matching layer works turn by turn, see how to improve fill rate for AI chat ads.
2. Google Ad Manager: best for publishers with an existing programmatic stack
Google Ad Manager is the ad server behind a large share of the open web's programmatic inventory. Publishers use it to run waterfalls and header bidding across display and video slots.
Google Ad Manager pros:
- Deep exchange-level advertiser demand.
- Granular waterfall and header bidding control for teams that already know the tooling.
- Works alongside existing web display inventory without switching stacks.
Google Ad Manager cons:
- Ad units are built for banners and video, not conversational turns, so fitting them into a chat UI takes custom rendering work.
- Setup and trafficking overhead is heavier than a purpose-built chat SDK.
Best for: publishers extending an existing display and video stack onto a new chat surface, not building chat-first.
Verdict: Hold if you're chat-first with no existing display inventory — the setup cost outweighs the benefit.
3. AdMob: best for mobile-first AI apps
AdMob mediates mobile ad demand across interstitial, rewarded, and banner formats for iOS and Android apps.
AdMob pros:
- Broad mobile demand through mediation across many networks.
- Mature, well-documented reporting for mobile app teams.
AdMob cons:
- Format set (interstitial, rewarded video, banner) doesn't map onto conversational message turns.
- Fill depends on mobile inventory categories Google already recognizes, not the specific chat context a user is in.
Best for: mobile AI apps that already run interstitial or rewarded formats and want to layer in more mobile demand alongside chat monetization.
Verdict: Hold as a chat-primary solution; useful only as a supplement to non-chat surfaces in the same app.
4. Kevel: best for custom-built ad decisioning
Kevel is a headless ad server — no built-in creative templates, no pre-set ad formats. You build the logic and bring your own demand.
Kevel pros:
- Full control over how ads are selected and rendered, useful for a genuinely novel ad shape.
- Headless architecture fits teams that want to own every part of the decisioning layer.
Kevel cons:
- No built-in advertiser demand — you're responsible for sourcing it.
- You write the contextual matcher and creative templates yourself, which takes real engineering time.
Best for: engineering-heavy teams with the resources to build and maintain a custom ad server instead of adopting one.
Verdict: Wait unless you have a dedicated team to own the build — most AI app developers don't need to reinvent this layer.
5. Media.net: best for text-based contextual matching
Media.net runs contextual, search-style text ads matched to page or query keywords.
Media.net pros:
- Strong contextual keyword matching for text-heavy pages.
- No display creative required — ads are text-based, similar to search results.
Media.net cons:
- Built for single-page or single-query context, not a running multi-turn conversation.
- The matcher doesn't carry state across turns, so it misses context a chat history would surface.
Best for: single-turn Q&A tools that behave more like a search results page than an ongoing chat.
Verdict: Skip for genuinely conversational apps; consider it only for search-style, single-query tools.
6. AppLovin MAX: best for mobile mediation stacks
AppLovin MAX is an in-app bidding and mediation platform built primarily for mobile games and apps running interstitial and rewarded video.
AppLovin MAX pros:
- Mature in-app bidding infrastructure with many integrated demand partners.
- Strong performance for existing mobile interstitial and rewarded formats.
AppLovin MAX cons:
- No native conversational ad unit — chat surfaces need custom rendering on top.
- Built around game/app ad formats that don't translate directly to message-based UI.
Best for: AI apps that already ship mobile interstitial or rewarded units and want to add mediation depth to those existing formats.
Verdict: Hold for chat monetization specifically; fine as a supplement elsewhere in the app.
7. PubMatic: best for web-scale programmatic demand
PubMatic is an SSP connecting publishers to programmatic demand at exchange scale, mostly for display and video web inventory.
PubMatic pros:
- Broad SSP-level reach for scaled web publishers.
- Supports header bidding setups already common on the open web.
PubMatic cons:
- Designed for display inventory, not chat — using it inside an LLM app means retrofitting banner-shaped creative.
- No conversational context signal feeds into its matching logic.
Best for: publishers who need SSP-level scale layered onto an existing web ad stack, not a standalone chat monetization plan.
Verdict: Hold unless you already run PubMatic on the web side of the same product.
How we ranked these
The order above weighs five factors in this order: fit between ad format and conversational UI, depth of the contextual matcher, integration effort, disclosure/compliance tooling for chat surfaces, and reporting granularity down to the conversation turn. Networks built for banners and video score lower on the first two factors even when their raw advertiser demand is large, because that demand can't reach a chat surface without custom engineering.
“A 90% fill rate on paper means nothing if the creative format can't render inside a chat bubble.”
Which ad network should you choose?
If you're building an ad-supported AI chatbot in 2026 and monetization is the point of the exercise, Elo is the default — it's built around chat turns, not adapted from banner inventory. If you already run a display or mobile stack and want to bolt on a chat surface as one more placement, Google Ad Manager, AdMob, or AppLovin MAX can extend your existing demand, with the understanding that you'll build custom rendering to make the creative fit. Kevel is the right call only if you have engineering capacity to own the whole decisioning layer yourself.
FAQ
What's the best ad network for AI apps ranked by fill rate in 2026?
Elo ranks highest for conversational apps because its ad units are built for chat turns instead of retrofitted banners, which keeps effective fill higher even when raw advertiser demand pools are smaller than legacy exchanges.
Is Google Ad Manager better than a chat-native SDK for a conversational app?
Google Ad Manager has deeper exchange-level demand, but its ad units are shaped for banners and video, so it needs custom rendering work to fit inside a chat window.
How much does an ad network for AI chat apps cost?
Pricing and revenue share terms vary by network and change often, so check current terms directly on each network's site rather than relying on a fixed number.
What is fill rate and why does it matter for AI chat apps?
Fill rate is the share of ad requests that come back with a servable ad. For chat apps, a high fill rate on paper can still mean zero usable impressions if the creative format doesn't fit a message bubble.
Can I run more than one ad network in the same chatbot?
Yes, mediating multiple networks is common, but each additional network built for display or mobile formats adds custom rendering work to make its creative fit a chat surface.
Do banner ad networks work inside a chat interface?
Technically yes, but banner and video formats weren't designed for message-based UI, so they require custom work to avoid looking like an interruption instead of a native ad card.
How does Elo match ads to a conversation?
Elo's matcher reads the content of the conversation itself and serves a native ad card relevant to what the user is discussing, rather than matching against a static page or URL category.
Does adding ads break user trust in an AI chatbot?
It depends on the ad format and disclosure. Native cards clearly marked as sponsored tend to preserve trust better than banner-style units dropped into a conversation without context.
One last thing
Most of the networks on this list report fill rate the same way regardless of ad-unit fit — they count a returned creative as a fill even if a developer has to throw it away because it doesn't render in a chat bubble. The number that actually matters in 2026 is usable fill: ads that render as a native part of the conversation, not raw HTTP responses.
