Quick answer: An SEO analytics API gives you programmatic access to SEO data: on-page signals, content structure, rankings, or backlinks. Here's the part most people miss: a large share of on-page and content analysis doesn't require a dedicated SEO platform at all. You can fetch any page as clean, structured content with a general-purpose API like link.sc and compute headings, metadata, word counts, internal links, and structured data yourself. Platforms earn their keep for the data you genuinely cannot compute: backlinks, search volume, and historical rankings.
I want to draw that line clearly in this post, because I see teams paying for enterprise SEO platform seats to answer questions a 30-line script handles.
What "SEO Analytics API" Actually Covers
The term gets used for at least four different things, and they have very different build-vs-buy answers:
| Category | Examples of data | Can you compute it yourself? |
|---|---|---|
| On-page signals | Title, meta description, headings, canonical | Yes, easily |
| Content analysis | Word count, structure, internal links, schema | Yes, easily |
| Rankings | Positions per keyword and location | Yes, via a search API |
| Off-page data | Backlinks, domain authority, search volume | No, you need a provider's index |
The first two rows are just "fetch the page and look at it." The third row is a search API problem, which I covered in the rank tracking API guide. Only the fourth row requires a proprietary index that took someone years and a lot of crawling infrastructure to build.
So before signing a platform contract, ask which rows you actually need.
The Signals You Can Compute by Fetching Pages
Everything on-page is visible in the page itself. The historical blocker wasn't access, it was parsing: raw HTML is miserable to analyze, especially on JavaScript-heavy sites where the content you care about doesn't exist in the initial HTML at all.
A fetch API that renders the page and returns clean output removes that blocker. One request:
curl -X POST https://api.link.sc/v1/fetch \
-H "x-api-key: lsc_your_api_key" \
-H "Content-Type: application/json" \
-d '{
"url": "https://example.com/blog/some-post",
"format": "markdown"
}'
From the response you can compute, per page:
- Title, including whether it exists, its length, and keyword presence
- Heading structure: is there exactly one H1, do H2s follow a logical hierarchy
- Word count and content depth, on the actual rendered content rather than HTML noise
- Internal and external links, their anchor text, and where they point
- Structured data, by checking for JSON-LD blocks
- Image alt text coverage
That list covers most of what an on-page audit tool reports.
A Working Example: Page Analyzer in Python
Here's a small but genuinely useful analyzer. It fetches a page as markdown and scores the basics:
import re
import requests
API_KEY = "lsc_your_api_key"
def analyze_page(url: str) -> dict:
resp = requests.post(
"https://api.link.sc/v1/fetch",
headers={"x-api-key": API_KEY},
json={"url": url, "format": "markdown"},
)
md = resp.json().get("content", "")
h1s = re.findall(r"^# (.+)$", md, re.MULTILINE)
h2s = re.findall(r"^## (.+)$", md, re.MULTILINE)
links = re.findall(r"\[([^\]]+)\]\(([^)]+)\)", md)
internal = [l for l in links if url.split("/")[2] in l[1]]
words = len(md.split())
title = h1s[0] if h1s else None
return {
"url": url,
"title": title,
"title_length": len(title or ""),
"h1_count": len(h1s),
"h2_count": len(h2s),
"word_count": words,
"internal_links": len(internal),
"external_links": len(links) - len(internal),
"issues": [
issue for issue, bad in [
("missing title", not title),
("multiple or missing H1", len(h1s) != 1),
("thin content", words < 300),
("no internal links", len(internal) == 0),
] if bad
],
}
print(analyze_page("https://example.com/blog/some-post"))
Point that at a list of URLs from your sitemap and you have a content audit that runs whenever you want, outputs data in whatever shape your team needs, and costs a few credits per page. Because link.sc renders JavaScript before extracting content, it reports what's actually on the page, which matters if your site is built on React or Vue where a naive HTML fetch sees an empty shell.
Adding the Competitive Layer
On-page analysis of your own site is half the picture. The other half is comparing against pages that outrank you, and this is where combining fetch with search gets interesting.
The workflow: search your target keyword, take the top 5 results, fetch each one, and run the same analyzer.
curl -X POST https://api.link.sc/v1/search \
-H "x-api-key: lsc_your_api_key" \
-H "Content-Type: application/json" \
-d '{"q": "how to sterilize canning jars", "engine": "google"}'
link.sc's search returns structured results under serpData.results, each with a targetUrl. Slice off the top 5 client-side, run each targetUrl through the fetch call above, and point the same analyzer at the output. Now you can answer questions like: what's the median word count in the top 5, which H2 topics do all of them cover that my page doesn't, how many of them use FAQ schema. That gap analysis is exactly what expensive "content optimization" tools sell, and the core of it is a loop over search results. If you want to go deeper on mining SERPs for content signals, I wrote about extracting People Also Ask data separately.
When a Dedicated SEO Platform API Is the Right Call
I promised honesty, so here it is: there are things you cannot replicate with fetch and search, and pretending otherwise wastes your time.
Backlink data. Knowing who links to a page requires having crawled the whole web and inverted the link graph. Providers like Ahrefs, Semrush, and Moz maintain those indexes, and if link data drives your decisions, pay for one.
Search volume and keyword difficulty. These come from clickstream panels and aggregated data you don't have. The numbers are estimates everywhere, but they're estimates you can't produce yourself.
Deep historical data. If you didn't collect it, you can't backfill it. Platforms have years of ranking and SERP history.
The pragmatic setup I recommend: use a platform API for volume and backlinks, and build your on-page, content, and ranking layers on a fetch/search API where you control the logic and the cost scales with usage instead of seat count.
Start With One Question
Don't build a "platform." Pick one question you currently answer manually, like "which of our 200 blog posts are thin, missing an H1, or short on internal links," and script it with the analyzer above. The link.sc docs cover the fetch and search endpoints, and the free tier is enough to audit a few hundred pages a month without paying anything.
Ready to build your own SEO analysis pipeline? Sign up for link.sc and get 500 free credits a month.