DataForSEO vs AnswerLine

Coverage

DataForSEO's prices

As published on dataforseo.com pricing pages, checked 2026-09-17. Pay-as-you-go; their pricing page states a $50 minimum payment. Standard queue is the cheapest and slowest mode, live the fastest.

Price per request

DataForSEO figures are its standard-queue price, with live mode in brackets. This API's figures are its dollar price per request at the Starter tier and, beside it, the Max tier. Lower prices apply from each bigger tier, reached by 30-day spend.

How requests, geo, billing and delivery work

Where DataForSEO fits

• Keywords, backlinks, on-page, domain analytics and their Labs datasets share one account and balance.
• The standard queue prices a Google SERP at $0.0006 for workloads that can wait.
• LLM Mentions and AI keyword search volume answer market-level questions without running prompts yourself.

This API fits when you need the answers ChatGPT, Gemini, Perplexity, Copilot and Grok users see, and one request contract with idempotent async tasks and signed webhooks. The guide below maps DataForSEO's task flow to async tasks here.

Which one for which job

DataForSEO fits a product that is SEO data first: keywords, backlinks, on-page audits and SERPs, where AI answers are a module next to them. Its standard-queue Google pricing suits a workload that tolerates waiting, and its LLM Mentions and AI keyword volume datasets answer market-level questions without running prompts yourself.

This API fits a product that needs the consumer answers users see on ChatGPT, Gemini, Perplexity, Copilot, Grok and Google, with one request contract, idempotent async tasks and signed webhooks.

The two combine: DataForSEO for keyword and backlink data, this API for AI answers.

Scraped answers vs model-API answers

DataForSEO’s AI Optimization API has two answer products, and the distinction matters for measurement (checked 2026-09-17 on docs.dataforseo.com):

• LLM Scraper returns results scraped from ChatGPT and Gemini: markdown, sources, search results, brand entities and fan-out queries.
• LLM Responses calls the providers’ model APIs for ChatGPT, Claude, Gemini and Perplexity and passes through the provider’s price.

A model-API answer is what a model returns for your parameters. A consumer answer is what the ChatGPT or Gemini app shows a user in a market, after its own search, ranking and product modules. For visibility tracking you want the second; the provider APIs comparison goes deeper.

Every engine on this API is the consumer surface. There is no model-API mode to mix up with it.

Migration: the task flow

DataForSEO is task-based: post up to 100 tasks per call, then find finished ones with tasks_ready and fetch each with task_get, or receive them at a postback_url. This API is also task-based, so most of the migration is renaming.

ChatGPT on DataForSEO

Their docs show a task post with a keyword, a location code and a language code, with HTTP Basic auth (checked 2026-09-17):

curl -X POST "https://api.dataforseo.com/v3/ai_optimization/chat_gpt/llm_scraper/task_post" \
  -H "Authorization: Basic $DATAFORSEO_CREDENTIALS" \
  -H "Content-Type: application/json" \
  -d '[{ "keyword": "best accounting software for freelancers", "location_code": 2840, "language_code": "en" }]'

Results are fetched later from task_get/advanced/$id, or pushed to a postback_url in gzip.

ChatGPT on this API

curl -X POST https://api.answerline.dev/v1/async/task/batch \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '[
    {
      "taskType": "CHATGPT",
      "payload": { "prompt": "best accounting software for freelancers", "country": "US" },
      "idempotencyKey": "acct-freelancers-US-2026-09-17",
      "webhook": { "url": "https://your-app.com/hooks/answers" }
    }
  ]'

The batch response answers each task by index; an item fails on its own (for example RESOURCE_ALREADY_EXISTS for a repeated key) without failing the rest.

Location codes vs country

DataForSEO identifies markets with numeric location_code values (2840 is the United States in their example) or names, plus a language. This API uses ISO 3166-1 alpha-2 country codes. ChatGPT, Gemini, Google Search and Google AI Mode answer from 213 countries, Gemini also takes a US state, and Google Search and AI Mode take a city in location; Copilot, Perplexity and Grok take any country, and Google News the 71 countries with a Google News edition. List what an engine supports with GET /v1/countries?model=chatgpt. Build your market table once, mapping each location code you use to a country.

Field mapping

result.searchQueries comes back empty on the ChatGPT answers served today. result.entities[] lists the brands and products the answer names, each with a type and name.

result.citationPills[] has no single counterpart: each pill carries the cited URL, domain, a citationPillId shared by the sources grouped in one chip, and its position in sources.

Google SERPs and the AI Overview

On DataForSEO the AI Overview is an item type inside Google Organic results; their live docs say load_async_ai_overview also returns overviews Google loads asynchronously, for an extra charge. On this API it is an add-on to the Google Search request:

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

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

const { task } = await client.createTask({
  taskType: "GOOGLE",
  payload: { query: "accounting software", country: "US", pages: 2, include: { aioverview: { markdown: true } } },
  idempotencyKey: "acct-google-US-2026-09-17",
});
const done = await client.waitForTask(task.id);
console.log(done.task.status, done.credits.creditsCharged);

pages fetches up to 10 result pages in one task, charged for the pages fetched. DataForSEO’s depth does the same per 10 results; if you migrate a depth of 100, use pages: 10.

Cost: standard queue versus live

DataForSEO’s standard queue lists a Google Organic SERP at $0.0006 against $0.002 in live mode (checked 2026-09-17, table above). There is no concurrency cap: tasks wait in your queue (about 100,000 tasks of a typical prompt at a time), until they start, and fail uncharged if they have not started within 4 hours.

To compare:

1. Split your workload into what must be fresh within minutes and what can wait hours.
2. Price the waiting part at DataForSEO’s standard queue and the fresh part at live, per engine.
3. Price the same answers here using dollars per request per engine from credits, at the tier your 30-day spend reaches on pricing.
4. Add DataForSEO’s $50 minimum payment and this API’s prepaid balance (top-ups from $1, no subscription) to your cash-flow view.
5. Remove any engine one side can’t serve before comparing totals, rather than counting it as zero.

Overlapping the two task queues

Give yourself a fortnight of overlap: the same prompts and markets posted to both, landing in one table with a provider column.

1. Choose prompts that already run on DataForSEO’s LLM Scraper for ChatGPT and Gemini, since those are the consumer surfaces both sides collect.
2. Map each location_code you use to a country.
3. Submit the same prompts here as tasks with a webhook, keyed by prompt, engine, market and day.
4. Store both results in one table with a provider column, the queue mode you used on DataForSEO (standard, priority or live), and the time each result arrived.

What the two weeks should tell you:

• Freshness: time from submission to result on each side, for the queue mode you’d use in production.
• Answer overlap: do both sides name the same brands for the same prompt and market? Two runs of one prompt already disagree somewhat, so establish that baseline first and blame neither side until you exceed it.
• Coverage gaps: tasks that failed here and DataForSEO tasks that returned no result.
• Total cost per usable answer, including DataForSEO’s minimum payment and the tier your spend would reach here.

Move the LLM Scraper engines first and leave the keyword, backlink and SERP calls where they are. They share an account, not a migration, and because DataForSEO charges per result, an endpoint you stop calling costs nothing while you decide about the rest.

Status checks after the move

DataForSEO carries a status code inside each task’s response and, per its FAQ, charges only when you get a result, so client code for it checks status twice: on tasks_ready and again on task_get. Here there is one status to check. The webhook delivery, or GET /v1/async/task/{id}, carries COMPLETED or FAILED, and a failed task is not charged.

Submission errors are a separate layer from task status, and each carries its own code, including the per-item codes inside a 200 batch response. Authentication and errors documents each one; rate limits explains the queue limit, the single error that rejects a whole batch rather than an item.

Checklist before migrating

1. Consumer or model answers? If you report what users see, only scraped answers count: DataForSEO’s LLM Scraper for ChatGPT and Gemini, or this API.
2. Engines. This API returns consumer Perplexity, Copilot and Grok answers; DataForSEO’s public docs (checked 2026-09-17) list Perplexity through the model API only, a Bing Copilot Search feature, and no Grok endpoint.
3. Markets. Do you need US state granularity? Here Gemini takes a US state, Google Search and AI Mode take a city, ChatGPT answers per country from 213 countries, and Copilot, Perplexity and Grok take any country. Map your location codes to countries first.
4. Other SEO data. If keywords and backlinks are core, keep DataForSEO for those.
5. Latency. Standard queue results can take up to 45 minutes per DataForSEO’s LLM Scraper pricing; plan dashboards accordingly on either side.
6. Receiver. Once your service verifies HMAC signatures and deduplicates by task id, drop polling entirely.

Where migrations go wrong

• Mixing LLM Responses into visibility metrics. Model-API answers and consumer answers differ; keep them in separate series if you use both.
• Reusing tag semantics for idempotency keys. Keys here must be unique per account; derive them from prompt, market and day so a retry cannot duplicate a task, and don’t reuse one across days.
• Assuming gzip postbacks. Deliveries here are plain JSON with a signature header; don’t decompress.
• Ignoring item-level batch errors. A 200 batch response can contain failed items. Check results[].success.
• Counting AI Overviews that weren’t shown. result.aioverview is null when Google showed none; store it as a zero, not a missing value.

Read the async tasks guide to set up batches, or the ChatGPT engine page for the full field list.

DataForSEO is a trademark of its owner. AnswerLine is not affiliated with or endorsed by DataForSEO.

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