Automating Backlink Monitoring via Ahrefs API

Vibe coding has helped automate dozens of routine processes over the past few years — many teams have already built internal tools on top of it and continue to develop them.

We've put together a summary of the guide from SEO blog Detailed on monitoring backlinks via the Ahrefs API. The article includes a detailed walkthrough, pricing, and key nuances.

Why does link tracking matter?

Every new backlink a brand earns in a niche is a signal. Someone published a study that journalists picked up. Someone launched a product that bloggers started talking about. Someone found a content format that publications are happy to link to.

If you track this kind of activity systematically, you get a real picture of what's working in the niche right now — which topics, data formats, and news hooks are generating links from authoritative sites.

The problem is that doing this manually across dozens or hundreds of brands is simply not feasible. There are platforms for monitoring mentions and backlinks, but they're expensive and lack flexibility.

That's why the guide's author chose the Ahrefs API, which had long been available only to Enterprise clients. In March 2026, Ahrefs opened up API access across all paid plans, and just a month later increased usage limits at no additional cost.

Step 1 — Define your list of brands to monitor

The number depends on the niche: a dozen brands or several hundred. The logic is straightforward — the more sites in the sample, the more trends and opportunities you can uncover.

The goal is to find content ideas you can improve with your own insights, so there's no reason to narrow the focus. For large platforms like Ahrefs, this means going beyond pure SEO and also monitoring brands in the AI visibility and marketing space more broadly.

Step 2 — Set filters in Ahrefs

For most use cases, the following filter set in the backlinks report works well:

• Backlink type: In content — links placed within the body text.

• DR: From 50 — sources with a sufficient authority level, reducing the risk of running into spammy or low-quality domains.

• Ref. page language: English (or another language) — the language of the referring page, adjusted to the specific task.

• New — links discovered for the first time within the specified time period.

• Last 3 months — the lookback window.

• Status: Newly published — links that appeared alongside the page's publication, rather than being added to existing content.

• Live links only — excludes links that were once present on a page but have since been removed.

• One link per domain — only one result per referring domain.

You can add extra filters for specific tasks — for example, checking follow and nofollow links separately. Trusted sites often use nofollow links, and even though they don't pass link equity, those mentions are still worth tracking.

Step 3 — API URL

One of the most convenient things about Ahrefs is that the service generates a ready-made API URL you can use directly in your request. It also immediately shows the expected cost in units per each row of data the request will return.

If you have basic development skills, the official API documentation provides clear guidance on how to optimize the URL to reduce costs. One example: removing referring page traffic data from the request when it's not needed for analysis — this brings the cost down from 66 to 13 units per row, a reduction of more than fivefold.

If you don't have a development background, you can ask an AI to adjust the ready-made API URL to match your filters and needs. It's worth setting the maximum number of rows per request to 50 — that's more than enough to get a clear overall picture for a specific brand.

One important nuance to factor into budget planning: the minimum cost of a single API call is 50 units, even if no relevant links are found for the given filters.

For scale reference — on the cheapest plan, you can theoretically check up to 2,000 sites before hitting the monthly unit limit.

Step 4 — Build an Interface for Storing Data

If you have experience working with API keys and app development — feel free to skip this step and move straight to analyzing results. For those who haven't done this before, there are several proven options that don't require deep coding knowledge.

One working approach is the Cursor and Google Firebase combo: Cursor writes and edits code based on text prompts, while Firebase stores data online with automatic backups and user authentication. 

Another option is Letaido, a platform developed by Ahrefs, built specifically for marketing workflows, where project development and hosting happen in one place without needing to connect multiple services. Other popular options include Lovable, Base44, and Claude Code.

An example of a working prompt you can give an AI assistant to get started:

"I want to use the Ahrefs API to pull fresh backlinks from a list of sites. Here is the API URL with the required filters from Ahrefs. I need the ability to store and sort data and re-run the analysis on a custom schedule. Help me build a simple interface for managing the list of sites and the results of each run."

Even for those building something like this for the first time, the output of simple text prompts often turns out to be fully functional. But there is an important security caveat: even if your chosen platform has built-in access control mechanisms, you should not publish the result publicly. 

There is a real risk of accidentally exposing API keys or other sensitive data to third parties — it's better to first sort out the privacy settings or consult someone with experience in application security.

Step 5 — Analyze the Results

The first table worth building from the collected data is a list of referring domains, sorted by how many different brands from your sample they have linked to over the past 90 days. The logic is straightforward: the more different brands that link to a specific site, the higher the objective probability that sooner or later a link to your site will appear there too.

It's useful to add a separate "favorites" tag for sites whose mentions are considered especially valuable — large, well-known publications like Fast Company, Fortune, or The New York Times. Based on this tag, it makes sense to create a separate report showing links from these premium sources and analyze it in depth.

Next, pass the collected data to AI and ask it to answer a specific set of questions:

• what common themes appear across the pages that are receiving links; 

• in what context the brand is mentioned — whether it's original research, a standalone product review, or a mention in a roundup; 

• whether there are any suspicious patterns in anchor texts; 

• what articles or studies are missing from the overall picture.

• any other notable insights in the data that the AI spotted on its own.

AI genuinely handles routine analytical work on large data volumes well, but combining automated analysis with your own manual review is always worthwhile — it's not a replacement for human judgment, but a way to speed up the initial screening and avoid missing obvious patterns.

If you need to scale the approach, there are several directions: pull more data about each link, more data about the referring page, check more rows per brand, run checks more frequently to spot opportunities faster, or launch monitoring for adjacent niches.

How Much Does It Cost?

Checking fresh backlinks for 180 sites in the guide cost 67,538 API units — less than the 100,000 provided even on the cheapest Lite plan. Higher-tier plans offer 400K, 1M, and 2M units respectively.

The article's author runs the check once per quarter — that's enough to have material to work with, and there's no need to check as many sites right from the start. Some sites in the sample didn't even return a full 50 rows of data.

Separately — AI analysis. Processing hundreds of fresh backlinks with an AI assistant and getting a structured report costs around $0.19 via Claude Opus 4.8 — more than sufficient for the task.

Conclusion

The approach works, but it's a tool that requires money and time to test. No technical skills are needed: Ahrefs generates the URL, and the interface is built with prompts that can be continuously improved.

The key thing to understand: what you get at the end is a list of relevant referring domains. Everything after that comes down to creating the content or products they'll actually want to link to.