Resumen
Este flujo de trabajo guias you a traves de evaluating SERP API opciones by running standardized probar consultas a traves de vendors y comparing precios, plataforma cobertura, respuesta latencia, y calidad de datos. It produces un decision matrix ese procurement puede usar to select el right vendor for your especifico consulta volume y plataforma requisitos.
Desencadenador
Manual activar cuando evaluating nuevo SERP API vendors
Programación
Manual activar durante vendor evaluation
Pasos del flujo de trabajo
Define evaluation criterios
List your mensual consulta volume, requerido plataformas, latencia requisitos, y budget ceiling. Estos parametros drive el comparacion.
Ejecutar probar consultas on cada vendor
Enviar el same 10 probar consultas to cada vendor API y registro tiempo de respuesta, resultado conteo, y calidad de datos.
Normalize precios to cost-per-1k consultas
Convert cada vendor's billing model to un comun unit: cost per 1,000 consultas at your projected mensual volume.
Puntuacion plataforma cobertura
Verificar cual plataformas cada vendor soporta (Google, YouTube, Amazon, Walmart, Reddit, TikTok) y marcar gaps contra your requisitos.
Generar decision matrix
Compile todos resultados en un ranked matrix showing cost, latencia, cobertura, y calidad for cada vendor.
Implementacion en Python
import requests
import time
import json
# Test against Scavio - repeat pattern for other vendors
API_KEY = "your_scavio_api_key"
TEST_QUERIES = [
{"query": "best CRM software 2026", "platform": "google"},
{"query": "wireless earbuds under 100", "platform": "amazon"},
{"query": "python tutorial for beginners", "platform": "youtube"},
{"query": "paper towels bulk", "platform": "walmart"},
{"query": "best project management tool recommendation", "platform": "reddit"},
]
def evaluate_vendor(name: str, api_key: str) -> dict:
results = []
for test in TEST_QUERIES:
start = time.time()
try:
res = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": api_key},
json={"platform": test["platform"], "query": test["query"]},
timeout=15,
)
latency_ms = round((time.time() - start) * 1000)
if res.status_code == 200:
data = res.json()
result_count = len(data.get("organic", []))
results.append({
"query": test["query"],
"platform": test["platform"],
"status": "ok",
"latency_ms": latency_ms,
"result_count": result_count,
"has_metadata": bool(data.get("ai_overview") or data.get("related_searches")),
})
else:
results.append({"query": test["query"], "platform": test["platform"], "status": f"error_{res.status_code}", "latency_ms": latency_ms})
except Exception as e:
results.append({"query": test["query"], "platform": test["platform"], "status": f"exception: {str(e)}"})
ok_results = [r for r in results if r["status"] == "ok"]
avg_latency = sum(r["latency_ms"] for r in ok_results) // max(len(ok_results), 1)
avg_results = sum(r["result_count"] for r in ok_results) // max(len(ok_results), 1)
return {
"vendor": name,
"queries_tested": len(TEST_QUERIES),
"success_rate": f"{len(ok_results)}/{len(TEST_QUERIES)}",
"avg_latency_ms": avg_latency,
"avg_result_count": avg_results,
"platforms_tested": list(set(t["platform"] for t in TEST_QUERIES)),
"details": results,
}
# Evaluate Scavio
# Verified pricing: $0.005/credit, 1 credit per request
# Plans: Free 250/mo, Project $30/7k, Bootstrap $100/28k, Startup $250/85k, Growth $500/200k
evaluation = evaluate_vendor("scavio", API_KEY)
print(f"Vendor: {evaluation['vendor']}")
print(f"Success: {evaluation['success_rate']}, Avg latency: {evaluation['avg_latency_ms']}ms")
print(f"Avg results: {evaluation['avg_result_count']}")
print(f"Platforms: {', '.join(evaluation['platforms_tested'])}")Implementacion en JavaScript
const API_KEY = "your_scavio_api_key";
const TEST_QUERIES = [
{ query: "best CRM software 2026", platform: "google" },
{ query: "wireless earbuds under 100", platform: "amazon" },
{ query: "python tutorial for beginners", platform: "youtube" },
{ query: "paper towels bulk", platform: "walmart" },
{ query: "best project management tool recommendation", platform: "reddit" },
];
async function evaluateVendor(name, apiKey) {
const results = [];
for (const test of TEST_QUERIES) {
const start = Date.now();
try {
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: { "x-api-key": apiKey, "content-type": "application/json" },
body: JSON.stringify({ platform: test.platform, query: test.query }),
});
const latencyMs = Date.now() - start;
if (res.ok) {
const data = await res.json();
results.push({ query: test.query, platform: test.platform, status: "ok", latencyMs, resultCount: (data.organic ?? []).length });
} else {
results.push({ query: test.query, platform: test.platform, status: `error_${res.status}`, latencyMs });
}
} catch (err) {
results.push({ query: test.query, platform: test.platform, status: "exception" });
}
}
const ok = results.filter((r) => r.status === "ok");
const avgLatency = Math.round(ok.reduce((s, r) => s + r.latencyMs, 0) / Math.max(ok.length, 1));
const avgResults = Math.round(ok.reduce((s, r) => s + r.resultCount, 0) / Math.max(ok.length, 1));
console.log(`${name}: ${ok.length}/${results.length} success, ${avgLatency}ms avg, ${avgResults} avg results`);
return { vendor: name, successRate: `${ok.length}/${results.length}`, avgLatency, avgResults, details: results };
}
await evaluateVendor("scavio", API_KEY);Plataformas utilizadas
Búsqueda web con grafo de conocimiento, PAA y resúmenes de IA
YouTube
Búsqueda de videos con transcripciones y metadatos
Amazon
Búsqueda de productos con precios, calificaciones y reseñas
Walmart
Búsqueda de productos con precios y datos de cumplimiento
Comunidad, publicaciones y comentarios en hilos de cualquier subreddit