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 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 DataForSEO is stronger

• 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.

Choose this API for the consumer answers on Perplexity, Copilot and Grok as well as ChatGPT and Gemini, US state targeting on the assistants, and one request contract with idempotent async tasks and signed webhooks. The guide below maps DataForSEO's task flow to async tasks here.

Which to pick

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

Pick this API if you need the consumer answers users see on Perplexity, Copilot and Grok as well as ChatGPT and Gemini, with country and US state targeting on each assistant and one request contract with idempotent async tasks and signed webhooks. It also covers Google Search with the AI Overview.

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", "state": "TX" },
      "idempotencyKey": "acct-freelancers-US-TX-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 and state

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 on every engine and a two-letter US state on the assistants. List what an engine supports with GET /v1/countries?model=chatgpt and GET /v1/states?country=US. Build your market table once, mapping each location code you use to a country and optional state.

Field mapping

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 is several times cheaper than live mode (see the table above). This API prices async tasks below synchronous calls too, but the difference is a fixed surcharge rather than a speed tier. Tasks wait in your queue until a concurrency slot of your plan is free, and fail uncharged if they have not started within 72 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 as async tasks, using credits per engine from credits, and choose a plan on pricing.
4. Add DataForSEO’s $50 minimum payment and this API’s monthly plan cycle (unused credits do not roll over) to your cash-flow view.
5. Remove any engine one side can’t serve before comparing totals, rather than counting it as zero.

Running both side by side before cutting over

Start with a parallel run: the same prompts, markets and schedule on DataForSEO and on this API for one or two weeks, stored in the same 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 and, for US markets, an optional state.
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 to compare at the end of the run:

• Freshness: time from submission to result on each side, for the queue mode you’d use in production.
• Answer overlap: do both sides mention the same brands for the same prompt and market? Compare against day-to-day variation on one provider before blaming either.
• 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 plan you’d need here.

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

DataForSEO reports task status codes inside its responses and, per its FAQ, charges only when you get a result; code written for it usually checks each task’s status in tasks_ready and task_get.

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. 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 has consumer Perplexity, Copilot and Grok endpoints; DataForSEO offers Perplexity through the model API only, lists a Bing Copilot Search feature, and has no Grok endpoint.
3. Markets. Do you need US state granularity? Map your location codes to country and state 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. Can your service verify HMAC signatures and deduplicate by task id? Then drop polling entirely.

Pitfalls

• 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.

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