Build vs buy: your own GEO scraper, or one API call

A GEO scraper is never one scraper — it is six, plus the platform underneath them. Each engine needs browser automation, session handling, and its own parser; the fleet needs a proxy pool, retries and backoff, drift detection, monitoring, and storage. The buy side is one API call: AgentGEO returns answerText, sources[], and provider metadata for ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot, and Gemini — one JSON contract across all six engines.

Build-vs-buy is a legitimate question, and this page treats it as one. Building is right for a small set of teams — the criteria are listed below without spin. For everyone else, the in-house pipeline quietly becomes an infrastructure product nobody budgeted for, staffed by the engineers who were supposed to be shipping yours.

What "build" actually means at six engines

The demo scraper is a script. The production pipeline is a system, and every component in it exists because something failed without it:

• A browser farm — headless Chromium at scale, with the memory footprint and flakiness that implies.
• A proxy pool — rotating residential or ISP addresses so six different anti-bot systems keep letting you in.
• Per-engine parsers — six DOMs, none of them a contract, each drifting on its own release schedule.
• Retries and backoff — engines throttle, challenge, and time out on their own clock.
• Schema-drift detection — something has to notice that yesterday's selector now returns nothing.
• Monitoring and alerting — an unmonitored pipeline this fragile is already down; you just have not noticed yet.
• Storage and normalization — six shapes of scraped HTML flattened into one queryable model of answers and citations.

Multiply the maintenance across six engines that each redesign without notice, and the real unit of cost becomes headcount: an infra team you never budgeted, building a product that is not yours.

The first 90 days of DIY

Compressed from how these projects actually go:

• Week 1: the demo works. One engine, one laptop, a green run. The project gets a thumbs-up.
• Week 3: the first ban wave. Sessions die, CAPTCHAs appear. You add a proxy pool and a solving service — the first recurring bills arrive.
• Week 6: Perplexity ships a redesign. Parsers break in production, and you find out from a dashboard full of nulls. First emergency selector rewrite.
• Week 9: you build real infrastructure. Retry queues, backoff, alerting — because silent failure turned out to be worse than failure. The scraper now has an architecture.
• Week 12: you are an infra team. Maintenance is the roadmap. The scraper has an on-call rotation, and the product feature it was supposed to feed is still waiting on data.

The buy side: one contract, six engines

Buying collapses the whole stack into a single authenticated call. Same request shape, same response shape, whichever engines you name in surfaces — and the managed-scraper engine, maintained for you means the anti-bot arms race is simply not your problem.

curl -X POST https://api.agentgeo.org/v1/fetches \
  -H "Authorization: Bearer ag_live_..." \
  -H "Content-Type: application/json" \
  -d '{"query": "best crm for startups", "surfaces": ["chatgpt", "perplexity", "gemini"]}'

# → one answers[] entry per requested surface:
# {
#   "answers": [
#     { "surfaceKey": "chatgpt", "answerText": "...",
#       "sources": [{ "title": "...", "url": "https://...", "position": 1 }] },
#     { "surfaceKey": "perplexity", "answerText": "...", "sources": [ … ] },
#     { "surfaceKey": "gemini", "answerText": "...", "sources": [ … ] }
#   ]
# }

Walkthroughs for every stack: Python and curl, plus Node and TypeScript.

The build-vs-buy test

Three criteria separate the teams that should build from everyone else:

• Collection is the product. If gathering AI answers is the thing you sell — or a strategic asset your company must own end to end — the pipeline deserves a real team, and building can win.
• You need surfaces beyond the six. AgentGEO covers ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot, and Gemini. If your case hinges on a surface outside that set, DIY may be the only option for that slice.
• Compliance demands full control. Some environments require every component of the collection path to run inside your own infrastructure.

Score zero of three, and the build is momentum, not strategy.

Build vs buy at a glance

The same eight dimensions, both columns honest:

What the buy side includes

One contract replaces the component list above. Out of the box you get:

• Six engines: ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Copilot, and Gemini — surface keys chatgpt, perplexity, gemini, google_ai_overview, copilot.
• Raw answer text per engine — the primitive every GEO metric is computed from.
• Structured citations — sources[] with title, URL, and position, identical shape on every engine.
• Provider metadata — surfaceKey and providerFields on each answer, so records stay attributable.
• MCP server — claude mcp add agentgeo -- npx -y agentgeo-mcp --key ag_live_... for Claude Code, Cursor, Codex, or any MCP client.
• Usage billing — subscription plus overage under a spend cap; the free tier needs no credit card.

Spend the headcount on your product

If collection is your product, build it well. If AI visibility is a feature — of your SaaS, your agency reporting, your internal pipeline — the scraper is a detour. One call returns the answers; your team returns to the roadmap.