Serper.dev vs SearchData API (2026): Pricing, $9 Indie Pack, 54-Week Trends & Remote MCP

import requests

HEADERS = {"Authorization": "Bearer sr_live_your_api_key"}
serp = requests.get("https://api.searchdataapi.com/v1/search", params={"q": "ai agents", "country": "us"}, headers=HEADERS).json()
trends = requests.get("https://api.searchdataapi.com/v1/trends/interest", params={"keyword": "ai agents", "country": "US"}, headers=HEADERS).json()
print(len(serp["data"]["organic_results"]), trends["data"]["current_interest"])

const headers = { Authorization: "Bearer sr_live_your_api_key" };
const [serp, trends] = await Promise.all([
  fetch("https://api.searchdataapi.com/v1/search?q=ai+agents&country=us", { headers }).then((r) => r.json()),
  fetch("https://api.searchdataapi.com/v1/trends/interest?keyword=ai+agents&country=US", { headers }).then((r) => r.json()),
]);
console.log(serp.data.organic_results[0], trends.data.velocity_label);

curl -s "https://api.searchdataapi.com/v1/search?q=best+vector+database+for+rag+2026&country=us&lang=en" \
  -H "Authorization: Bearer sr_live_your_api_key"

{
  "code": 0,
  "message": "success",
  "data": {
    "query": "best vector database for rag 2026",
    "country": "us",
    "lang": "en",
    "total_results": 10,
    "organic_results": [
      {
        "rank": 1,
        "title": "Best Vector Databases for Production RAG in 2026 — Benchmark",
        "url": "https://supabase.com/blog/vector-database-rag-benchmark",
        "domain": "supabase.com",
        "snippet": "Compare pgvector, Qdrant, Pinecone, and Weaviate across recall@10, p95 query latency, and hybrid BM25 + dense vector indexing."
      },
      {
        "rank": 2,
        "title": "Qdrant vs Pinecone vs pgvector: Choosing a RAG Vector Store",
        "url": "https://qdrant.tech/benchmarks/",
        "domain": "qdrant.tech",
        "snippet": "Filtered HNSW vector search benchmarks at 10M and 100M embeddings with quantization memory footprints."
      }
    ],
    "people_also_ask": [
      {
        "question": "Which vector database is fastest for RAG?",
        "answer": "In-memory Rust engines like Qdrant and Milvus achieve sub-10ms p95 latency at 10M+ vectors, while PostgreSQL pgvector (HNSW) simplifies transactional hybrid search."
      }
    ],
    "related_searches": [
      "pgvector vs qdrant",
      "hybrid search rag pipeline",
      "langchain vector store"
    ]
  },
  "meta": {
    "credits_charged": 2,
    "credits_remaining": 11998
  }
}