Beyond Google: Bing, DuckDuckGo, Yandex, Baidu, Naver, Brave APIs

Google dominates global search, but six other engines have meaningful share, in specific countries, for specific audiences, or for specific data types. A scraper that ignores them misses entire markets.

This lesson is the tour.

Bing, Microsoft's, still big

• Market share: ~3% global, ~8-15% in some Western markets (US/UK), higher on Windows desktops.
• Why it matters: Bing Chat / Copilot AI integration; ad data different from Google; Yahoo Search is powered by Bing.
• Indexing differences: Bing crawls some sites Google doesn't (newer indie publishers); ranking weights differ.
• SERP-API support: universal, every major provider supports engine=bing.

When to add Bing tracking:

• Multi-engine SEO dashboards.
• B2B audiences (Bing skews older/professional/Western).
• AI search monitoring (Copilot answers can differ from Google's AI Overview for the same query).

DuckDuckGo, privacy-focused

• Market share: ~0.5% global, growing.
• Indexing source: mainly Bing's index, plus its own enhancements.
• Why it matters: privacy-conscious audiences; default in some browsers (Brave's default, occasionally Firefox).
• SERP shape: simpler than Google, no AI Overview, no Knowledge Graph (as of early 2026), fewer feature blocks.
• SERP-API support: most providers, sometimes labeled duckduckgo.

When to add DuckDuckGo tracking:

• Privacy-niche audiences.
• Reach measurement for privacy-positioned products.
• Cross-engine sanity check against Bing.

Yandex, Russian/CIS

• Market share: dominant in Russia (~50%), high in Belarus, Kazakhstan.
• Indexing differences: strong on Russian-language content; weaker on Western content.
• Why it matters: if your audience is Russian-speaking, Yandex is the primary engine.
• SERP shape: similar to Google with own KG-like blocks and AI surfacing.
• Geopolitical note: sanctions, payment processing complications. Some providers don't support Yandex; check.

Baidu, Chinese

• Market share: dominant in mainland China (~60-70%); smaller in HK/TW.
• Indexing source: China-specific, weighted heavily toward Chinese-language content.
• Why it matters: if you target China, Baidu (and the local 360 / Sogou) are mandatory.
• SERP shape: features differ, heavy use of Baidu Baike (their own knowledge layer), promoted content, and integrated tools.
• Operational note: China-targeted scraping faces GFW issues, some providers route through specific infrastructure for Baidu.

Naver, Korean

• Market share: dominant in South Korea (~60%).
• Indexing source: heavily integrates Naver's own properties, Naver Cafe (forums), Naver Blog, Naver Shopping.
• Why it matters: Korea SEO is fundamentally about Naver, not Google.
• SERP shape: very different from Western SERPs. "Search" includes shopping carousels, blog posts, café threads, news, all interwoven.
• Specialized: SEO in Korea requires Naver-native strategies (blog posts on the Naver platform itself often outrank external sites).

Brave Search, independent index

• Market share: small but growing; default in Brave browser; popular among technical/privacy audiences.
• Indexing: built its own independent index from scratch (one of the few not based on Google/Bing).
• Why it matters: alternative-index measurement; growing Brave-browser user base; ad-free, privacy-aware results.
• SERP shape: AI summaries (Brave Search AI), Goggles (user-defined ranking adjustments), web results.
• SERP-API support: supported by some providers, often labeled brave.

Configuring each in a SERP-API call

from typing import Literal

Engine = Literal["google", "bing", "duckduckgo", "yandex", "baidu", "naver", "brave"]

def search(q: str, engine: Engine = "google", **params):
  return requests.get("https://api.example-serp.com/search", params={
  "q": q,
  "engine": engine,
  "api_key": API_KEY,
  **params,
  }).json()

# Multi-engine comparison
for eng in ["google", "bing", "duckduckgo", "brave"]:
  data = search("api scraping guide", engine=eng)
  print(f"{eng}: {len(data.get('organic_results', []))} results")

When to add a second engine

Adding a second engine doubles API costs and parsing complexity. Worth it when:

• Geographic audience. Russia → Yandex; China → Baidu; Korea → Naver.
• Audience type. Privacy-niche → DuckDuckGo/Brave. B2B/older Western → Bing.
• AI search tracking. Bing Copilot vs Google AI Overview.
• Editorial sanity. Cross-engine comparison catches Google-specific anomalies.

Don't add an engine just because it exists. Each one adds cost, parsing maintenance, and dashboard complexity.

Per-engine quirks to know

Engine	Quirk
Bing	Feature parity with Google is good; AI Copilot block in JSON now
DuckDuckGo	Fewer features; rank tracking more stable (less SERP feature noise)
Yandex	Cyrillic-heavy; encoding matters; geopolitical complications
Baidu	Mandarin-only indexing realities; GFW routing matters
Naver	Korean SERPs have unique block types; ranking on Naver's own platforms is the SEO game
Brave	Independent index; results genuinely differ from Bing/Google

A multi-engine snapshot script

def snapshot(q: str, engines=("google", "bing", "duckduckgo", "brave"), gl="us", hl="en"):
  out = {}
  for e in engines:
  try:
  data = search(q, engine=e, gl=gl, hl=hl)
  out[e] = [r["link"] for r in data.get("organic_results", [])[:5]]
  except Exception as ex:
  out[e] = f"error: {ex}"
  return out

print(snapshot("python web scraping"))
# → {'google': [...], 'bing': [...], 'duckduckgo': [...], 'brave': [...]}

For multi-engine SEO, fold this into your normalized storage with an engine column.

Hands-on lab

Conceptual + practical. Pick one non-Google engine relevant to your work (Bing if Western B2B; Naver if Korean market; Brave if privacy audience). Run a few queries through your SERP-API in that engine, compare against Google. Note differences: ranking order, presence/absence of feature blocks, dominant-source domains. The differences are your dashboard's competitive intelligence.