Serper vs AnswerLine

Coverage

Serper's credit packs

As published on serper.dev, checked 2026-09-17. Credits are valid for 6 months; 2,500 free queries on sign-up; prices exclude sales tax.

Price per request

Serper figures are one query from its Starter pack and, in brackets, the Ultimate pack. This API's dollar figure is its credits per request at the credit price of the lowest paid plan. Reference plan: Lite.

How requests, geo, billing and delivery work

Where Serper is stronger

• Google queries cost down to $0.30 per 1,000 on its largest pack; compare it with this API's Google Search price above.
• Their FAQ states 1 to 2 second responses, with rate limits from 50 to 300 queries per second.
• There is no subscription: packs are prepaid and last six months.

Choose this API when you measure more than the ten blue links: the AI Overview with its sources, answers from ChatGPT, Perplexity, Gemini, Copilot and Grok, city-level location for Google, and async batches. The guide below shows both requests.

Which to pick

Pick Serper for high-volume Google results where speed and price per query come first: rank checks on large keyword sets, search grounding for an LLM application, or any workload that needs organic results in one to two seconds. Its packs are prepaid and last six months, with no subscription.

Pick this API when what you measure sits above or beside the ten blue links: the Google AI Overview with its source rail and citation pills, and the AI assistants’ answers for the same topics. It returns Google Search too, so one integration covers both.

The two solve different problems, and running both is a reasonable setup.

What each is optimised for

Serper’s public site positions it as a fast Google SERP API: real-time requests, no caching, rate limits from 50 to 300 queries per second by pack, and endpoints for search, news, images, maps, places, videos, shopping, scholar, patents and autocomplete (checked 2026-09-17, serper.dev). It does not list AI Overviews or AI assistants.

This API is optimised for completeness of what a user sees. A Google Search request can return organic results, ads, People Also Ask (including their AI Overview answers with include.paaAioverview), People Are Saying, the local pack, the knowledge graph, shopping cards and the AI Overview, from a location and device you choose. That takes longer than a bare organic query, so this API offers synchronous calls for interactive use and async tasks for volume.

Migration: the same Google request

On Serper

The request format below is the one Serper’s playground generates: a POST with the key in an X-API-KEY header and a JSON body (checked 2026-09-17, serper.dev/playground).

curl -X POST https://google.serper.dev/search \
  -H "X-API-KEY: $SERPER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{ "q": "electric bike for commuting", "gl": "us", "hl": "en" }'

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": "electric bike for commuting",
    "country": "US",
    "hl": "en",
    "location": "Portland,Oregon,United States",
    "include": { "aioverview": { "markdown": true } }
  }'

Parameter mapping:

The difference in pages matters for rank tracking: one request returns up to ten pages, charged for the pages fetched, instead of one request per page.

Adding AI answers to a rank tracker

Adding this API next to Serper covers two more layers of a keyword’s visibility: whether the AI Overview cites you, and whether the assistants recommend you when asked the same question in natural language.

import os, requests

API = "https://api.answerline.dev"
headers = {"Authorization": f"Bearer {os.environ['API_KEY']}"}
keyword = "electric bike for commuting"
day = "2026-09-17"

tasks = [
    {
        "taskType": "GOOGLE",
        "payload": {"query": keyword, "country": "US", "include": {"aioverview": {}}},
        "idempotencyKey": f"ebike-google-US-{day}",
    },
    *[
        {
            "taskType": engine,
            "payload": {"prompt": f"What is the best {keyword}?", "country": "US"},
            "idempotencyKey": f"ebike-{engine}-US-{day}",
        }
        for engine in ("CHATGPT", "GEMINI", "COPILOT", "GROK")
    ],
]
for t in tasks:
    t["webhook"] = {"url": "https://your-app.com/hooks/answers"}

r = requests.post(f"{API}/v1/async/task/batch", headers=headers, json=tasks, timeout=60)
print(r.json()["summary"])

Keep Serper for the daily organic positions across your whole keyword list, and run this batch for the subset you report AI visibility on. Join the two on keyword and day.

What to store from each

From Serper, most rank trackers keep the organic position and URL per keyword. From this API, store per keyword and day:

1. result.aioverview presence (null or not).
2. aioverview.sources[].url and position, the source rail.
3. aioverview.citationPills[], which sources the answer text cites inline, grouped by citationPillId.
4. For each assistant, result.sources[] and whether your domain or brand appears in result.text.
5. For Copilot and Grok, result.searchQueries, which shows how the assistant rephrased the question.

These give you share-of-voice metrics that organic rank alone can’t: see mentions vs citations for definitions.

Comparing cost

Serper sells credits in packs; as of 2026-09-17 its site describes each pack’s credits as that many queries. This API prices a Google Search task at 3 credits for the first page and 2 per extra page, plus 2 for the AI Overview, and 2 more for a synchronous call, with the credit’s dollar value set by the plan on pricing.

A bare organic query can be cheaper on Serper; the table above shows both prices. The useful comparison is the cost of the data you need:

1. If you only need organic positions, compare per-query prices directly.
2. If you need the AI Overview, Serper’s site doesn’t list it, so the comparison is this API against building or buying that layer elsewhere.
3. If you need assistant answers, price them per engine here and add them to your Serper spend.
4. Remember Serper credits expire after six months and this API’s monthly credits do not roll over; size both to what you use.

Running both side by side before cutting over

Start with a parallel run: the same prompts, markets and schedule on Serper and on this API for one or two weeks, stored in the same table with a provider column.

1. Take the subset of keywords where you want AI Overview and assistant data; Serper keeps running your full organic list.
2. Run the same keywords here as GOOGLE tasks with include.aioverview, in the same country and language.
3. Store organic positions from both sides for those keywords, so you can see whether positions agree.
4. Add the assistant prompts for the same topics as separate rows keyed by keyword and engine.

What to compare at the end of the run:

• Organic position agreement for the overlapping keywords. Small differences are normal between any two collections at different moments; large systematic ones point to location or device settings.
• AI Overview presence and citations for those keywords, which only one side returns.
• Latency budget: whether async tasks fit your reporting schedule.
• Combined cost of Serper for bulk organic plus this API for the subset.

Cut over one engine at a time and keep the parallel data: it is the only record of how the switch itself moved your numbers.

Handling errors after the switch

Serper deducts credits only for successful responses and stops accepting queries at a zero balance, per its FAQ.

This API’s errors carry a stable code to branch on (authentication and errors):

Every response carries an X-Request-Id; send your own to tie a request to your logs, and quote it to support. The TypeScript SDK applies this retry policy for you.

Decision checklist

1. Only organic results, high volume, low latency? Serper.
2. AI Overview citations? This API.
3. ChatGPT, Perplexity, Gemini, Copilot or Grok answers? This API.
4. City-level or device-specific Google results? This API takes location, uule and device; Serper’s site describes country and language.
5. Webhooks and async batches? This API.

Pitfalls

• Expecting one-second AI data. AI answers and full SERPs take longer to return. Use async tasks and webhooks; don’t put a synchronous call in a user-facing request path without a timeout plan.
• Case of country codes. Serper examples use lower-case gl; this API’s country is an upper-case ISO code.
• Duplicated runs. Serper requests are independent; tasks here are deduplicated by idempotency key, so derive keys from keyword, engine, market and day.
• Comparing organic rank with AI citations. A page can rank first and be absent from the AI Overview, or the reverse. Report them as separate series.

Start with the quickstart or the Google Search engine page.

Other comparisons