We Built a Pay-As-You-Go Keyword & SERP API Because SEO Data Shouldn't Cost $140 Every Month

SEO data is useful. Paying a large monthly subscription just to access a few hundred keyword metrics is another story.

If you're an indie hacker, SaaS founder, developer, or someone building programmatic SEO workflows, you've probably experienced this problem.

You need keyword search volume, CPC, ranking difficulty, competitor URLs, backlink information, and SERP data. Maybe you're developing a keyword research feature, validating a niche, generating landing pages, or building an automated content research pipeline.

But accessing reliable search intelligence through established SEO platforms can become surprisingly expensive.

Many tools are designed around recurring subscriptions, large credit allowances, and agency-level workflows. If you only need a limited number of searches, you may end up paying for capacity you never use.

We wanted a different approach.

That's why we built SerpSpur, a keyword research platform and developer API designed around pay-as-you-go access, flexible credit usage, and a large search intelligence index.

Our goal is straightforward: make useful SEO and SERP data accessible to independent developers and smaller businesses without forcing everyone into an expensive monthly commitment.

The Problem With Traditional Keyword APIs

Most keyword research tools provide basic metrics such as:

• Monthly search volume

• Keyword difficulty

• Cost per click

• Paid competition

• Search intent

These metrics are useful, but they don't always provide enough information to make an informed SEO decision.

For example, a keyword difficulty score of 38 doesn't explain how competitive the current first page actually is.

Are the ranking pages supported by hundreds of referring domains? Are they relatively weak websites? Does the SERP contain forum discussions? Is Google showing an AI Overview that could affect organic clicks?

Those details matter when deciding whether a keyword is worth targeting.

We also wanted to address several other limitations:

1. Paying for unused API credits

A recurring subscription can be inconvenient when your search volume changes from month to month. We wanted users to have more flexibility with credits that do not expire.

2. Keyword difficulty without enough context

A single difficulty number doesn't tell the whole story. Developers and SEO professionals need more information about the backlink strength of competing pages.

3. Search volume without momentum

A 12-month average can hide recent growth or declining demand. Historical monthly data and shorter-term momentum provide additional context.

4. SERP features changing organic visibility

AI Overviews, featured snippets, local packs, and other search features can change how users interact with traditional organic results.

5. Missing opportunities in forum-based results

When Reddit, Quora, or niche discussion threads appear on the first page, that may indicate an opportunity for useful, experience-based content.

We wanted to bring these signals together instead of returning only a handful of disconnected keyword metrics.

What We Built: SerpSpur Keyword Intelligence

SerpSpur combines a web-based keyword research tool with a Universal REST API.

The platform uses a reported global search intelligence index containing approximately 12.5 billion combined Google and Bing keywords.

Here is an overview of the infrastructure figures documented for the platform:

Data categoryReported indexCombined Google and Bing keywords12.5 billionGoogle keyword database8.2 billion+Bing keyword database4.3 billionLive backlink records2 trillion+Indexed live pages197.3 billion+Referring domains708.6 millionGoogle SERP snapshots523.3 millionTracked LLM prompts338.4 millionGoogle AI Overview prompts312.7 millionChatGPT prompts25.7 millionAmazon SERPs25.9 millionGlobal business listings262.2 million

These are platform-reported index figures, not independently verified third-party benchmarks. We include them to explain the scope of the data infrastructure behind the product.

24 Data Points Instead of Just Search Volume and Difficulty

One of the main things we focused on was making keyword API responses more useful for actual applications.

A keyword lookup can return 24 SEO, PPC, and SERP intelligence data points.

1. Search Demand and Keyword Trends

Instead of relying only on average monthly volume, the API provides:

• Localized monthly search volume.

• Twelve-month historical search volume.

• Monthly search arrays for trend visualization.

• Year-over-year volume changes.

• Three-month momentum comparisons.

• Peak seasonality month.

• Total indexed SERP result count.

This information can help identify keywords that are growing, declining, seasonal, or relatively stable.

2. Actual Page 1 Backlink Competition

Keyword difficulty becomes more useful when you can examine the websites and pages currently ranking.

SerpSpur provides:

• Keyword Difficulty (0–100).

• Average referring domains across the Top 10.

• Average total backlinks.

• Average dofollow backlinks.

• Average Domain Rank of ranking domains.

• Average URL Rank of ranking pages.

For example, if a keyword has moderate search demand and relatively low difficulty, you can also inspect the average referring domains of the competing pages.

That provides a more practical starting point for estimating the link competition.

3. PPC and Commercial Metrics

For users researching commercial opportunities, the API also includes:

• Google Ads CPC.

• Low top-of-page bid.

• High top-of-page bid.

• Paid competition score.

• Competition level.

• Estimated monthly clicks for the number-one organic position.

• Estimated monthly traffic value.

The platform also considers Google AI Overview presence when calculating its estimated position-one click metric.

These figures are estimates, not guaranteed traffic or revenue predictions.

4. Search Intent and SERP Opportunity Signals

The remaining data points help developers understand what kind of content a keyword may require.

They include:

• Primary search intent.

• Secondary or overlapping intent.

• Parent topic.

• SERPSpur Opportunity Score.

• Active SERP features.

• Forum weakness detection.

The forum weakness indicator is particularly interesting.

If Google displays Reddit, Quora, or other forum discussions among its first-page results, the API can flag that condition.

This doesn't automatically mean a keyword is easy to rank for. However, it gives content teams another signal to investigate before creating a page.

The API: Built for Developers and SaaS Products

We didn't want keyword research to remain limited to the SerpSpur dashboard.

The Universal REST API allows developers to integrate keyword intelligence into their own applications.

Main endpoint:

GET /api/v1/keywords

The API supports different research depths depending on how much keyword expansion you need.

Research modeReturned dataCreditsSandboxMock response for integration testing0VaultCached keyword cluster3SeedOne keyword with full metrics4LiteSeed + up to 8 related keywords5StandardSeed + up to 35 keywords15FullSeed + up to 100 keywords30MegaSeed + up to 250 keywords100

There are also additional endpoints:

• POST /api/v1/keywords/batch — Bulk keyword enrichment.

• GET /api/v1/keywords/golden — Discover low-difficulty, high-volume keyword opportunities.

• GET /api/v1/tai — Retrieve a Traffic Authenticity Index report.

• GET /api/v1/account — Check account credits, plan information, and API limits.

The idea is to let developers select the research depth that matches their application instead of paying the same amount for every type of query.

The 30-Day Cache Vault: A Different Approach to API Billing

One feature we particularly wanted to build was a cache-based research mode.

The Vault mode checks whether the requested keyword data is already available in the 30-day cache.

Its documented billing behavior is:

• A cached keyword lookup costs 3 credits.

• A cache miss returns HTTP 404.

• A cache miss consumes 0 credits.

This is useful when you're enriching large keyword lists and don't want to pay for unavailable cached results.

For bulk requests, Vault mode supports up to 50 keywords, while live batch mode supports up to 10 keywords.

The goal is predictable usage rather than charging users for every attempted lookup regardless of whether cached data exists.

Credit-Based Billing: Pay for What You Actually Use

We also wanted to avoid the feeling that users must keep paying every month just to preserve unused research credits.

SerpSpur uses a shared credit wallet for web-based research and API access.

The platform's documented credit system includes:

• Credits that do not expire.

• Different credit costs for different research depths.

• A free sandbox mode for testing API responses.

• A free account endpoint for checking balance and usage.

• Shared credit access between supported web and API workflows.

API Security and Usage Controls

If you're integrating an external API into your own product, authentication and spending controls matter.

SerpSpur includes several developer-focused controls:

API key security: Secret API keys are displayed once when generated and stored as SHA-256 hashes.

Multiple API keys: Each account can create up to 10 keys.

Permission scopes: Keys can be restricted to keyword research, TAI, or all supported endpoints.

Daily spending limits: Optional daily credit caps help control usage.

IP restrictions: Developers can configure IP or CIDR subnet whitelisting.

Rate limits: The documented limits are 30 requests per minute for Free and PAYG, 60 for Pro, and 120 for Agency, with a 5-request-per-second burst cap.

Atomic credit deductions: Credits are conditionally deducted before upstream processing. If an upstream search request times out, the documented billing logic automatically refunds the deducted credits.

These controls are intended to make API integration more predictable for both small scripts and larger applications.

A Simple API Integration Example

Here is a basic Python example showing how a developer can submit a bulk keyword request through Vault mode.

import os
import requests

API_KEY = os.getenv("SERPSPUR_API_KEY")

URL = "https://serpspur.com/api/v1/keywords/batch"

headers = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}
payload = {
    "keywords": [
        "seo audit tool",
        "keyword research tool",
        "broken link checker"
    ],
    "country": "us",
    "language": "en",
    "mode": "vault"
}
response = requests.post(
    URL,
    headers=headers,
    json=payload,
    timeout=30
)
print("Status:", response.status_code)
print("Credits deducted:",
      response.headers.get("X-SerpSpur-Credits-Deducted"))

print(response.json())

For developers who want to experiment with deeper keyword clusters, the API also supports different research depths.

Who Might Find This Useful?

We built SerpSpur with several types of users in mind.

Indie hackers: Research niches, validate product ideas, and add keyword intelligence to small SaaS applications.

SaaS developers: Integrate keyword data, CPC, search intent, and SERP information into existing products.

Programmatic SEO builders: Enrich keyword lists and organize content opportunities using structured API responses.

SEO agencies and freelancers: Research keyword competition, compare ranking pages, and identify content opportunities for clients.

AI content workflow developers: Use search intent, SERP features, and topical data to inform automated content research.

Marketing teams: Evaluate search demand, commercial intent, and competitive conditions before investing in content.

Try the Keyword Research Tool and API

You don't have to start by building a complete integration.

You can first explore the web-based keyword research tool and see how the returned data fits your workflow.

A Question for Other Indie Hackers and Developers

We're building SerpSpur around a simple idea: search intelligence should be accessible without requiring every developer to purchase an expensive recurring subscription.

But we're also interested in what other builders actually need.

If you're developing an SEO tool, keyword research SaaS, programmatic SEO system, or AI-powered content workflow:

Which keyword or SERP metrics are missing from the APIs you're currently using?

Would you find more value in backlink competition data, historical keyword momentum, forum opportunity detection, AI Overview information, or deeper related-keyword clusters?

I'd be interested in hearing how other developers are using search data and what you'd like to see in a more flexible keyword API.

We're building this in public and would genuinely appreciate practical feedback.