Build vs buy: collecting AI answers yourself vs AnswerLine
Every team building an AI-visibility feature faces the same choice: run the collection layer yourself, or buy it per call. Here's the honest accounting.
What building means
| Component | In-house | AnswerLine |
|---|---|---|
| Sessions & browsers | Real consumer sessions — a fleet of real browsers with persistent state, plus the ops to keep them alive. | Included per request. |
| Parsing | One parser per engine, plus fixtures and someone on call when a layout change silently drops a field. | Contract documented in the OpenAPI spec; parser drift is our problem. |
| Geo coverage | Country- and US-state-level answers need egress from those locations — residential or ISP-grade proxies. | `country`, `usState` and `zip` are request parameters. |
| Queueing & retries | Prompt sets × engines × markets quickly become hundreds of thousands of runs; partial failure is the norm. | Async tasks, 500-task batches, idempotency keys and webhooks are the API. |
| Time to first answer | A senior engineer's quarter to reach 'mostly works on one engine' — before engine two. | One POST; the quickstart takes minutes. |
Where the honest boundary is
Buying makes sense when the data is an input to your product — visibility tracking, research, enrichment. It makes less sense when the collection itself *is* your product, or when you need fields no provider returns; in that case you're building anyway — scope it to the one engine that matters first.
The break-even is engineer-months, not the API bill: 500 / month, no card. of free usage is enough to run your ten hardest prompts and compare the response against what your pipeline produces today.
The longer version of this argument, with numbers, is in build vs buy.