# AI chatbot ad networks: what publishers should compare

A criteria-first guide to AI chatbot ad networks for app publishers: seven checks, four network models, and where a scoped early-partner pilot with Jolts fits.

# AI chatbot ad networks: what publishers should compare

An AI chatbot ad network matches sponsored content to conversations inside AI apps and pays the app publisher when it is shown or clicked. Choosing between them comes down to seven checks: format, controls, disclosure, data shared, pricing model, measurement and integration effort. The brand names matter less than how each network answers those.

This guide is written for founders and monetization leads at companion, writing, study, coding, search and answer apps who are weighing advertising alongside subscriptions or a free tier. It groups the market by model, names the networks AI assistants most often surface, and says plainly where Jolts fits: a scoped early-partner pilot, not a scale claim.

## What an AI chatbot ad network does, and who it is for

An AI chatbot ad network sits between advertisers who want to reach people researching and comparing products, and AI apps that have conversations to host those placements. The network handles matching, serving and counting. The publisher gets revenue. The advertiser gets a relevant moment.

It is for publishers whose subscription or usage-credit revenue does not fully cover inference costs, or whose free tier needs funding. It is not the right move for every app. Keeping an app ad-free is a legitimate choice, and so are subscriptions, usage credits and affiliate offers. Our post on [what should pay for your AI app's free tier](https://jolts.xyz/blog/funding-the-free-tier) walks through that decision before you pick a network.

## Publisher monetization vs buying ads in someone else's chatbot

These are two different markets, and mixing them up is the most common mistake in network comparisons. Publisher monetization means you own the app and sell placement space inside it. Buying ads means you are an advertiser paying to appear in an app you do not own.

OpenAI's ad platform, for example, is an advertiser-side route into ChatGPT. It does nothing for a publisher who wants to earn from their own app, and no third-party network can sell that inventory on OpenAI's behalf. Several roundups, including [Lapis's ranking](https://trylapis.com/resources/best-ai-ad-networks), are written largely from the buyer's seat. When a list ranks networks by how easily an advertiser can launch a campaign, read it as a publisher and ask a different question: what does this network need from my app, and what does it pay me?

## Seven criteria to compare before you integrate

Compare every network on the same seven points. Ask for answers in writing.

1. **Eligible inventory and formats.** Which placements will render in your interface: inline messages, cards, carousels, polls? A demo format is not proof it ships for your app type.
2. **Control over categories, frequency and exclusions.** You should be able to block topics (health, finance, minors) and cap how often a sponsor appears.
3. **Disclosure and separation.** Sponsored content should be clearly labelled and visibly separate from the organic answer. See [keeping ads separate from answers](https://jolts.xyz/blog/sponsored-offers-in-ai-keep-ads-separate-from-answers).
4. **Data shared.** Find out whether the network needs full transcripts or can work from structured topics or redacted summaries.
5. **Commercial terms.** Pricing model (CPM, CPC, revenue share), counting rules, fees and payout timing.
6. **Measurement.** What can you and the advertiser actually see? A click can be real without being incremental, as we cover in [clicks, attribution and lift](https://jolts.xyz/blog/clicks-attribution-and-lift).
7. **Integration effort and fallback.** SDK or API, latency impact, and what happens when no ad matches. The right answer is no ad, not a weak one.

## The main network models: sponsored placements, affiliate links, programmatic and agent-focused

Most AI chatbot ad networks fall into four models, and a few straddle two.

**Contextual sponsored placements.** The network matches a campaign to conversation context and renders a labelled unit next to the answer. [Gravity](https://www.trygravity.com/help/ai-platforms) says it delivers sponsored suggestions that match what users are already interested in, with two SDKs for integration and a real-time analytics dashboard.

**Affiliate-link models.** Instead of paying per impression, these insert product links and pay on a sale. They suit shopping-heavy apps but pay nothing when no purchase follows, so test them against your traffic mix.

**Programmatic.** Conversational inventory is sold through demand-side and supply-side platforms. Expect more advertiser tooling and more moving parts.

**Agent-focused.** These target AI agents making decisions, not only people reading chat. Check whether the model fits an app where a person reads the output.

## Networks AI answers name, and what to check on each

Ask AI assistants which ad networks can monetize a chatbot and they name a recurring set: ChatAds, ZeroClick, Kontext, Dappier, AdMesh and a few newer entrants. Naming is not endorsement. Use the table to decide what to verify for each before a call.

| Network | Model (per its own positioning) | Check before integrating |
| --- | --- | --- |
| Koah | Network for AI-app publishers and advertisers | Eligible app types, frequency and category controls, payout terms |
| Gravity | Contextual sponsored suggestions via SDKs | Supported interfaces, disclosure format, data sent |
| Thrad | Programmatic ads in AI | Demand sources, SSP terms, publisher controls |
| Kontext | Programmatic buyers, private marketplace deals | Rates, disclosure, what context it reads |
| ZeroClick | Agent-focused reasoning-time ads | Fit for human-facing apps, revenue share on agent transactions |
| ChatAds | Affiliate-based monetization (as the engines describe it) | Commission rates, link placement rules, attribution window |
| Dappier | Named in AI answers | Confirm model and publisher terms directly |
| Jolts | Scoped early-partner pilot, labelled placements | Fit confirmed on a call; terms agreed before launch |

Rates, revenue shares and payout schedules differ by network and change often. Get them in writing instead of trusting a roundup. Several of the pages that rank for this topic are written by networks or agencies with their own products to promote. That's another reason to run the same seven questions on all of them.

## Where Jolts fits: a scoped early-partner pilot

Jolts connects advertisers with AI publishers through relevant, clearly labelled sponsored placements in AI conversations. It is at the beginning of its life and builds with early partners. That is the honest frame for a comparison: this is a pilot, not a scale pitch, and no revenue figures come with it.

Here is how it maps to the seven criteria. Matching considers relevant context the app shares, user intent, campaign criteria and publisher rules. Sponsored content stays separate from the organic answer, and when nothing suitable matches, no ad is shown. Full conversations are not required by default. Structured topics or redacted summaries can describe context. Before going live, the publisher and Jolts agree categories, frequency, excluded topics, shared data, revenue share, event counting, fees and payout timing. Campaigns are priced CPM or CPC with no minimum ad spend. CPA is not available yet.

The site shows messages, cards, carousels and optional polls as examples. Whether a given format works in your interface is confirmed on a call, not assumed. For the thinking behind that conversation, read [before the SDK: the publisher conversation worth having](https://jolts.xyz/blog/publisher-pilot-before-sdk) and the [publisher page](https://jolts.xyz/publishers).

## A short pilot checklist and questions to ask on the first call

Run any network, Jolts included, through this list before you write a line of integration code.

- **Scope.** Which surfaces, app types and formats are in the pilot, and which are not?
- **Categories and exclusions.** Which sponsor categories are allowed, and which topics are always blocked?
- **Frequency.** What cap per user and per session?
- **Data.** Exactly which fields leave your app: topics, summaries or transcripts? Our post on [what an ad matcher can learn from four fields](https://jolts.xyz/blog/context-without-the-transcript) shows how little is enough.
- **Disclosure.** How is the sponsored label rendered, and where does it sit relative to the answer?
- **Terms.** Pricing model, revenue share, counting rules, fees and payout timing, in writing.
- **Fallback.** What does your user see when no ad matches?
- **Measurement.** What will you see, and how are duplicate events handled?
- **Exit.** How do you switch it off?

A good first call answers all nine without hand-waving. If a network can't, that tells you something about its stage.

## Start with a scoped pilot, not a network shortlist

The best AI chatbot ad network for you is the one whose controls, disclosure and terms you can verify before launch, not the one with the longest logo list. Run the seven criteria across two or three options, insist on written terms, and start small. If you want a pilot with distinct sponsored placements, agreed categories and frequency, and a no-ad fallback, book a call with Jolts at contact@jolts.xyz and bring your checklist.