Migrate from Async Search & Trends Scrapers to <900ms Synchronous REST JSON — Save 85%+.

# BEFORE (Async container job: 15–40s container boot + dataset polling + CU overhead)
# run = ApifyClient(TOKEN).actor("apify/google-search-scraper").call(run_input={
#     "queries": "best vector database for rag",
#     "resultsPerPage": 10,
#     "countryCode": "us",
# })
# items = list(client.dataset(run["defaultDatasetId"]).iterate_items())

# AFTER (SearchData API: <900ms synchronous HTTP GET — 0 container boot fees)
import os
import requests

API_KEY = os.environ["SEARCHDATAAPI_KEY"]  # sr_live_... (12,000 free credits on signup)
HEADERS = {"x-api-key": API_KEY}

# 1. Live Google SERP Organic + People Also Ask (/v1/search)
serp = requests.get(
    "https://api.searchdataapi.com/v1/search",
    params={"q": "best vector database for rag", "country": "us", "lang": "en", "limit": 10},
    headers=HEADERS,
    timeout=15,
).json()
for hit in serp["organic_results"][:5]:
    print(f"#{hit['position']} {hit['title']} -> {hit['link']}")

# 2. 54-Week Google Trends 0–100 Interest Curve (/v1/trends/interest)
trends = requests.get(
    "https://api.searchdataapi.com/v1/trends/interest",
    params={"keyword": "ai agents", "country": "US"},
    headers=HEADERS,
    timeout=15,
).json()
print("Latest trend index:", trends["timeline"][-1])

// BEFORE: await apifyClient.actor("apify/google-search-scraper").call({ queries, resultsPerPage: 10 })
// AFTER: Native fetch() — zero SDK dependencies, <900ms response for LangChain / Vercel AI SDK / n8n

const API_KEY = process.env.SEARCHDATAAPI_KEY!;
const headers = { "x-api-key": API_KEY };

// Parallel SERP + Live News + 54-Week Trend Telemetry in <1s
const [serpRes, newsRes, trendsRes] = await Promise.all([
  fetch("https://api.searchdataapi.com/v1/search?q=" + encodeURIComponent("ai coding agents") + "&country=us&limit=10", { headers }),
  fetch("https://api.searchdataapi.com/v1/news/search?q=" + encodeURIComponent("ai coding agents") + "&country=US&when=24h", { headers }),
  fetch("https://api.searchdataapi.com/v1/trends/interest?keyword=" + encodeURIComponent("ai coding agents") + "&country=US", { headers }),
]);

const [serp, news, trends] = await Promise.all([serpRes.json(), newsRes.json(), trendsRes.json()]);
console.log({
  topHit: serp.organic_results?.[0]?.title,
  paaCount: serp.people_also_ask?.length ?? 0,
  breakingArticles: news.articles?.length ?? 0,
  points54w: trends.timeline?.length ?? 0,
});

# 1. Live Google SERP Organic + PAA (<900ms sync JSON — 2 Credits / query)
curl -sS "https://api.searchdataapi.com/v1/search?q=best+vector+database+for+rag&country=us&lang=en&limit=10" \
  -H "x-api-key: sr_live_YOUR_API_KEY"

# 2. 54-Week Google Trends Interest Over Time Curve (10 Credits)
curl -sS "https://api.searchdataapi.com/v1/trends/interest?keyword=ai+agents&country=US" \
  -H "x-api-key: sr_live_YOUR_API_KEY"

# 3. Real-Time Google News Wire (2 Credits / query)
curl -sS "https://api.searchdataapi.com/v1/news/search?q=nvidia+blackwell&country=US&lang=en&when=24h" \
  -H "x-api-key: sr_live_YOUR_API_KEY"

# 4. Sub-80ms Google Suggest Autocomplete (1 Credit / query)
curl -sS "https://api.searchdataapi.com/v1/autocomplete?q=best+crm+for&country=us&lang=en" \
  -H "x-api-key: sr_live_YOUR_API_KEY"

{
  "ok": true,
  "endpoint": "/v1/search",
  "meta": {
    "request_id": "req_sr_sync_91f2a",
    "credits_used": 2,
    "took_ms": 38,
    "cache_hit": true
  },
  "query": {
    "q": "best vector database for rag",
    "country": "us",
    "lang": "en"
  },
  "organic_results": [
    {
      "position": 1,
      "title": "Vector Database Benchmarks for Production RAG (2026)",
      "link": "https://benchmark.example.dev/vector-dbs-rag",
      "domain": "benchmark.example.dev",
      "snippet": "Side-by-side latency, recall@10, and hybrid BM25+HNSW filtering across pgvector, Qdrant, Pinecone, and Turbopuffer."
    }
  ],
  "people_also_ask": [
    {
      "question": "Which vector database is fastest for RAG agents?",
      "snippet": "Sub-10ms p99 recall depends on index quantization and metadata pre-filtering..."
    }
  ]
}