ScrapeAtlas Review 2026: API Pricing, Credits & Limits

Short answer

ScrapeAtlas is worth a small pilot for developers who need public social data inside their own applications. Its attraction is reducing the work of maintaining separate retrieval integrations. Its value depends on whether the exact fields your workflow needs arrive reliably enough to justify the integration and review time.

Affiliate disclosure: DiscoverAI may earn a commission if you sign up or buy through our ScrapeAtlas affiliate link, at no additional cost to you. This relationship does not determine our verdict.

Evidence and review scope

This is an official-documentation review checked November 10, 2026. We did not create an account, call the API, buy credits, contact support or audit data deletion. Ratings are withheld. The evaluation below is a proposed test, not a benchmark we ran. Public examples and customer testimonials are provider material, not our results.

What ScrapeAtlas does

The API documentation describes public profiles, posts, comments, videos, commerce listings and advertising data returned as JSON. Inputs depend on the endpoint: a URL, handle, identifier or search query. Your application remains responsible for storage, scheduling and analysis.

This makes it a potential data layer for creator research or a source-backed assistant. It does not establish that a generated summary is correct. Keep the source URL and retrieval time beside every conclusion so an editor can inspect the evidence. Select the endpoint around the actual research question rather than collecting every available field.

Pricing: credits are not completed research tasks

The official pricing section lists these one-time USD packs:

No card is required for the starter grant. Purchased credits do not expire. Successful retrievals generally cost one credit, including partial results and valid empty collections; LinkedIn post search, Quora search and Threads user search are listed at two. Each extra page is another request. Failed requests and internal retries carry no extra credit charge according to the provider.

The practical budgeting question is cost per accepted record. Before buying, record requests, pagination, manual cleanup and downstream model costs for one complete job. A cheap request that cannot support the intended conclusion has little value. Keep a small spending ceiling until that job succeeds repeatedly.

Partial responses need explicit handling

The response guide distinguishes success, partial, failed and challenged outcomes. A partial result can return HTTP 200 with known omissions. Check status and coverage warnings rather than accepting every successful HTTP response as complete. Preserve unknown values as unknown; an absent metric should not silently become zero.

For a research application, show incomplete coverage next to the result. Stop a summary from saying there were no relevant comments when retrieval was incomplete. Rate limits and unavailable sources should produce visible gaps, with bounded retries and a route for manual checking. These are our implementation recommendations, not controls we verified in a deployed integration.

AI agents and MCP: confirm compatibility

The MCP page labels the integration a preview and describes Streamable HTTP with a bearer API key in a custom header. It also contains language saying hosted access is on its way. Meanwhile, the privacy policy describes OAuth assistant connections. Those public descriptions do not establish support for every client today. Ask which authentication path is live for your specific assistant before relying on it.

For an agent pilot, limit the question and approved endpoints. Review the proposed calls before allowing repeated retrieval. Treat returned posts as source material: instructions inside a retrieved post should never control the agent's next action. Keep credentials in the client's secure connection settings rather than prompts.

Privacy and retained searches

The privacy policy says retrieval workers do not maintain a response cache or archive. Application usage records omit target URLs, handles, search text and response content. Dashboard Search social behaves differently: account-linked search text and related metadata can be retained for up to 90 days, and AI providers receive relevant material for suggestions and summaries. These are provider statements, not audited findings.

Decide which interface your team will actually use before assessing data handling. API-only research and an interactive dashboard search have different records. Avoid confidential terms in test queries and inspect the current policy for your workload.

The public data notice limits retrieval to supported public information; it does not provide private inboxes or account-only content. It also explains that public visibility grants no blanket permission to reuse material. Plan how your application handles source changes and deletion requests before retaining results.

Billing and operational boundaries

The terms describe non-refundable delivered digital credits and consumed usage, subject to stated exceptions and applicable rights. They require secure handling of credentials and respect for published limits. Confirm the checkout total and refund terms before a large top-up.

Keep the first purchase small while evaluating fit. A larger balance does not make incomplete data complete or guarantee that a source remains reachable. Agree the endpoint and service expectations with the provider if your product cannot tolerate an outage.

Proposed buyer test

Use a narrow, permitted research task and a small set of public source pages that you can inspect manually. Write down required fields and failure conditions before opening the trial.

1. Pick ten representative records, including a sparse page and one recently updated page. Record a manual reference with retrieval time.
2. Call only the necessary endpoints. Save request identifiers, status, coverage warnings and credit deductions without recording secrets.
3. Compare every required field with the source. Count missing, stale and mismatched values separately from accepted records.
4. Test pagination boundaries. Check whether repeated pages create duplicates and whether a partial result is clearly labelled in your application.
5. Exercise authentication and unavailable-source handling in a controlled test. Confirm retries stop and the user sees an actionable message.
6. If using an assistant, require source links and uncertainty in its summary. Check whether it invents a conclusion when a response is incomplete.
7. Measure total spend and review minutes for the completed task. Compare that with a manual baseline before buying a larger pack.

Proceed only when the pilot meets your own coverage and cost requirements. Save the failed cases as regression examples when the upstream platform changes.

Alternatives and verdict

A source platform's own API is a sensible first comparison when it provides your required access. A configurable scraping platform may fit custom extraction requirements better. Manual research can remain economical for a small, infrequent sample. These are workflow alternatives, not comparative performance rankings.

Our recommendation is to evaluate ScrapeAtlas as a retrieval component, with human review and visible coverage limits. Start with the starter grant, confirm MCP compatibility if needed and purchase against a measured workload. Use our AI tool buyer checklist to record the decision; the ScrapeAtlas profile summarizes the features and caveats.