The Problem
Google's 2026 pricing changes for Custom Search JSON API and stricter rate limits on free search have made it harder for local LLM setups (Ollama, llama.cpp, vLLM) to access web search. Self-hosted users who relied on free Google search endpoints now face paywalls or aggressive rate limiting. SearXNG works but breaks frequently as upstream engines tighten anti-bot measures. Local LLM users need a reliable, affordable search backend that returns structured results.
The Scavio Solution
Use Scavio's REST API as the search backend for local LLMs. The API returns structured JSON that can be injected directly into LLM context windows. With 250 free credits/mo and $0.005/credit beyond that, it costs less than maintaining a SearXNG instance and is more reliable than free Google endpoints. A simple Python wrapper converts Scavio results into the prompt format your local LLM expects.
Before
Before: A developer running Ollama with a custom search tool hit Google's free tier limit after 100 queries/day. Switching to SearXNG required Docker setup, and results failed 20% of the time due to upstream rate limiting. Debugging search failures took more time than building the actual LLM application.
After
After: The same developer uses Scavio's API with a 10-line Python wrapper. 250 free queries/mo cover casual use. For heavier workloads, $30/mo provides 7,000 queries with zero maintenance. Search failures dropped from 20% to under 1%.
Who It Is For
Developers running local LLMs (Ollama, llama.cpp, vLLM) who need reliable web search after Google tightened free search access. Anyone whose SearXNG instance keeps breaking.
Key Benefits
- 250 free queries/mo covers casual local LLM search usage
- Structured JSON output injects cleanly into any LLM prompt format
- Zero maintenance vs SearXNG Docker setup and upstream breakages
- Reliability above 99% vs SearXNG's 80% success rate on rate-limited engines
- $30/mo for 7K queries replaces $50/mo SearXNG hosting with better results
Python Example
import requests
import json
API_KEY = "your_scavio_api_key"
def web_search(query: str, num_results: int = 5) -> str:
"""Search tool for local LLMs. Returns formatted context string."""
r = requests.post(
"https://api.scavio.dev/api/v1/search",
headers={"x-api-key": API_KEY},
json={"platform": "google", "query": query},
timeout=10,
)
data = r.json()
results = []
if data.get("ai_overview"):
results.append(f"AI Overview: {data["ai_overview"].get("text", "")}")
for item in data.get("organic", [])[:num_results]:
results.append(f"[{item["position"]}] {item["title"]}\n{item.get("snippet", "")}\nURL: {item["link"]}")
return "\n\n".join(results)
# Use in Ollama tool calling or prompt injection
context = web_search("python asyncio best practices 2026")
print(context)JavaScript Example
const API_KEY = "your_scavio_api_key";
async function webSearch(query, numResults = 5) {
const res = await fetch("https://api.scavio.dev/api/v1/search", {
method: "POST",
headers: { "x-api-key": API_KEY, "content-type": "application/json" },
body: JSON.stringify({ platform: "google", query }),
});
const data = await res.json();
const results = [];
if (data.ai_overview) results.push(`AI Overview: ${data.ai_overview.text || ""}`);
for (const item of (data.organic || []).slice(0, numResults)) {
results.push(`[${item.position}] ${item.title}\n${item.snippet || ""}\nURL: ${item.link}`);
}
return results.join("\n\n");
}
const context = await webSearch("python asyncio best practices 2026");
console.log(context);Platforms Used
Web search with knowledge graph, PAA, and AI overviews
YouTube
Video search with transcripts and metadata
Community, posts & threaded comments from any subreddit