I built a SERP API that outranked SerpApi — here‘s what we learned
Why We Built TalorData
We were building AI agents and kept hitting the same wall: search data was too slow, too messy, and too expensive.
Existing SERP APIs were built for dashboards, not agents. They took 2–4 seconds to respond. They returned HTML that needed parsing. They charged for failed requests.
We spent months managing proxy pools and fixing broken selectors. Eventually we said: “There has to be a better way.”
So we built it.
What We Did Differently
1. Designed for speed
We optimized for P90 < 0.8s. When your agent is waiting, every millisecond matters.
2. Structured data first
No HTML parsing. No regex. Clean JSON with parsed titles, links, descriptions, and positions.
3. Pay only for success
Failed requests cost $0. Your budget aligns with results.
4. One API, four engines
Google, Bing, Yandex, and DuckDuckGo — all through a single endpoint.
The Results
Third‑party benchmarks rank TalorData #1 among six major SERP API providers:
General Score: 79.19
Zero‑result rate: 2.08% (lowest)
Average organic results: 8.85 per query (highest)
And the pricing? $0.25/1K at scale — significantly lower than incumbents.
Integrations
We built native integrations for:
LangChain (
langchain-talor-serp)LlamaIndex
Dify
n8n
MCP (Claude, Cursor, VS Code)
What‘s Next
We‘re continuing to expand our integration ecosystem and improve latency. The goal is simple: make search data as easy to use for AI agents as it is for humans.
Try It
500 free requests on sign‑up — no credit card required.
Would love to hear what you‘re building. Drop a comment 👇
