AI Overview API alternative

Providers

This API's AI Overview fields

result.aioverview is null when Google didn't show one, otherwise an object with:

• markdown (with include.aioverview.markdown: true) and the plain answer text.
• sources: the source rail, each with position, url, label, description and, when Google served it as a redirect, redirectLink.
• citationPills: the inline citation chips, one entry per (pill, source) pair sharing a citationPillId, each with label, url, domain and position in sources.
• relatedLinks: a chip's "View related links" items that aren't in sources, grouped by the same citationPillId.

Full definitions are in the API reference and the OpenAPI document.

What to compare

Most Google SERP APIs return the AI Overview in some form. Providers differ on four points:

1. Does one request return it, or does your code need a follow-up call against an expiring token?
2. What is returned: blocks of text and a reference list, or also which sources the answer cites inline?
3. What is charged when Google shows no overview for the query?
4. Can you pin the market: country, city, language and device?

This API answers them as: one request; markdown, the source rail, citation pills and related links; a fixed add-on priced on the request; and country, location or uule, hl and device.

How providers retrieve the overview

Other providers, per their own pages, checked 2026-09-17:

• SearchApi returns ai_overview in its Google results; when Google loads it separately, the response carries a page_token for its google_ai_overview engine, which its docs say expires in less than a minute (searchapi.io).
• Scrape.do returns ai_overview in its Google Search endpoint; a deferred overview carries a session_key, valid for 90 seconds and usable once, for a follow-up call costing 5 credits (scrape.do).
• ScrapingBee returns ai_overviews in its Google API only on regular requests, which cost 15 credits instead of 10 for a light request (scrapingbee.com).
• Oxylabs returns ai_overviews inside google_search results when rendering is enabled; its docs describe this as maximizing the chance of getting one (developers.oxylabs.io).
• Bright Data says adding brd_ai_overview=2 to a SERP API request increases the likelihood of receiving the overview (brightdata.com).
• Apify publishes a Google AI Overviews Scraper actor and an AI Overview add-on to its Google Search actor, charged when an overview is captured (apify.com).
• ScraperAPI’s Google SERP structured endpoint documents no AI Overview field (docs.scraperapi.com).
• Serper’s public site did not list AI Overviews (serper.dev).

Token-based follow-ups put a timing constraint on your code: a rate-limit delay or backlog between the first and second request loses the overview for that search. “Increases the likelihood” and “maximizes the chance” wording means presence rates can differ between providers for the same query, so test your own query set on each candidate before comparing prices.

The request on this API

curl -X POST https://api.answerline.dev/v1/monitor/google \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "how long does a heat pump last",
    "country": "US",
    "include": { "aioverview": { "markdown": true }, "paaAioverview": true }
  }'

The overview-related parts of the response:

• result.aioverview: null if Google showed none, otherwise the answer (with markdown when requested), sources, citationPills and relatedLinks, as described above.
• result.peopleAlsoAsk[]: with include.paaAioverview, questions whose answer is itself an AI Overview carry type: "AIOVERVIEW", their own markdown and sources.
• result.organicResults[] and the other SERP blocks, from the same page, so you can compare rank and overview citation in one row.

The two include flags share one add-on: enabling both costs the same as enabling one.

Measuring AI Overview visibility

An AI Overview tracker usually needs four metrics per query, market and day. All four come from one response:

1. Presence: aioverview !== null. Track the share of your query set that shows an overview; it can move for reasons unrelated to your content.
2. Rail citation: your domain appears in aioverview.sources[].url. Record its position.
3. Inline citation: your domain appears in aioverview.citationPills[].domain. Being cited inside the text is closer to being quoted than appearing in the rail.
4. Organic overlap: your organicResults[].position for the same query, to see whether overview citations follow rank or not.

A TypeScript sketch:

import { Client } from "@answerline/sdk";

const client = new Client({ apiKey: process.env.API_KEY!, baseUrl: "https://api.answerline.dev" });
const domain = "example.com";

const { result } = await client.google({
  query: "how long does a heat pump last",
  country: "US",
  include: { aioverview: {} },
});

const aio = result.aioverview;
const row = {
  present: aio !== null,
  railPosition: aio?.sources.find((s: { url: string }) => s.url.includes(domain))?.position ?? null,
  citedInline: aio?.citationPills?.some((p: { domain: string }) => p.domain.endsWith(domain)) ?? false,
  organicPosition: result.organicResults.find((r: { link: string }) => r.link.includes(domain))?.position ?? null,
};
console.log(row);

For a full query set, submit GOOGLE tasks in batches of up to 500 with idempotency keys and a webhook, as the async guide shows, and write one row per delivery.

What the AI Overview add-on costs, and when

This API prices the AI Overview as a fixed add-on to a Google Search request. A one-page Google Search task is $0.00103 on every tier, each extra page $0.00071, and include.aioverview $0.00071 once (credits). The add-on is charged only when Google shows an overview for that query; include.paaAioverview is free.

Cost is predictable, and the decision left to you is which queries to request it for:

1. Request it everywhere at first. One full run tells you the presence rate per query group.
2. Drop queries that never show one. After a few weeks, queries that never returned an overview in any market can run without the add-on and be rechecked monthly.
3. Keep it on commercial and informational queries where overviews appear and citations matter to you.

Where a provider charges a follow-up call only when the overview is deferred, or charges per captured overview, cost per query follows the presence rate; a fixed add-on does not. Price your own query mix on each candidate using the presence rate from your first run.

Sampling and cadence

AI Overview presence and sources vary between runs of the same query.

• Measure presence as a rate, not a yes or no: run the same query more than once per week per market and report the share of runs with an overview.
• Separate volatility from change: before reporting that a citation was lost, check whether it also disappeared in earlier runs. Sampling vs census covers how many runs are enough.

A batch for one query group across markets:

import os, requests

queries = ["how long does a heat pump last", "heat pump vs furnace cost"]
countries = ["US", "CA"]
day = "2026-09-17"

tasks = [
    {
        "taskType": "GOOGLE",
        "payload": {"query": q, "country": c, "include": {"aioverview": {}}},
        "idempotencyKey": f"hp-{i}-{c}-{day}",
        "webhook": {"url": "https://your-app.com/hooks/serp"},
    }
    for i, q in enumerate(queries)
    for c in countries
]
r = requests.post(
    "https://api.answerline.dev/v1/async/task/batch",
    headers={"Authorization": f"Bearer {os.environ['API_KEY']}"},
    json=tasks,
    timeout=60,
)
print(r.json()["summary"])

Beyond Google: AI answers on the same topics

An AI Overview is one AI answer surface. The same question asked in natural language to ChatGPT, Gemini, Perplexity, Copilot, Grok or Google AI Mode can cite different sources. With the same key and task model, add those engines for the queries that matter most, keyed by the same query id so you can compare citation sets across surfaces. How AI engines choose citations discusses why they differ.

Evaluating any AI Overview API

Run the same 50 to 100 queries, in the same market and device, through each candidate on the same day, then compare:

1. Presence rate. For how many queries did each return an overview? A lower rate may be the provider, not Google.
2. Completeness. The answer text should match what Google shows a user in that market. Spot-check ten by hand.
3. Citations. Are rail sources and inline citations separated? Are redirect links resolved or exposed?
4. Second-request failures. For token-based providers, how many follow-ups expired or failed, and were they charged?
5. Cost per usable row. Divide spend by rows with a verified overview or a verified absence, not by requests.
6. Geo fidelity. Repeat five queries in a second country; local-intent queries should change.

Pitfalls

• Treating absence as failure. Google shows AI Overviews for some queries and not others, and the set changes. Store null as a result.
• Comparing across devices. Mobile and desktop results can differ; where a provider lets you choose, keep one device per series.
• Daily noise. Overviews and their sources vary between runs. Compare weekly aggregates, not single days; AI answer volatility covers sampling.
• Forgetting People Also Ask. Some questions expand into their own AI Overviews with their own sources; if you track citations, include them.
• Redirect links. Google can serve a source as a redirect. Store url for analysis and keep redirectLink when present, so you can audit what the page linked to; don’t count the same source twice because one copy is a redirect.
• Changing the query set mid-series. Presence rates are only comparable over a fixed set of queries and markets. Add new queries as a separate group and merge them after they have their own baseline.
• Tokens in logs. Keys in URL query strings end up in logs; this API takes the key in the Authorization header.

See the Google Search engine page for every field, or monitoring Google AI Overviews for a longer walkthrough.

Related: Google AI Overview API and AI Overview tracking tools.

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