SearchCans

Bing SERP API Discontinued: Migration Guide to SearchCans & Top Alternatives

Bing SERP API shutdown analysis and alternatives. Best replacements: SearchCans, SerpAPI, Serper. Migration guide, pricing comparison. ByteDance engineer insights.

4 min read

When Microsoft announced significant changes to Bing’s SERP API access in late 2024, it sent shockwaves through the developer community. As someone who spent 4 years at ByteDance building search-dependent features for TikTok and Douyin, I’ve had to navigate these API transitions firsthand. Here’s my analysis of where the industry stands in 2025 and what options developers have.

Quick Navigation: Bing API Alternatives | SERP API Comparison | Migration Case Study

What Happened to Bing’s SERP API?

For those who missed it, here’s the timeline:

Q3 2024

Microsoft announced restructuring of Bing API access

Q4 2024

Free tier significantly reduced, pricing increased

Q1 2025

Many developers forced to migrate to alternatives

The changes weren’t a complete shutdown, but the new pricing and restrictions made Bing’s official API impractical for many use cases, especially:

Why This Matters

At ByteDance, we relied heavily on search APIs for:

Content Discovery

Features for discovering relevant content

Trend Analysis

Systems for analyzing emerging trends

AI-Powered Recommendations

Intelligent recommendation engines

Fact-Checking

Pipelines for verifying information accuracy

When a major API provider changes terms, it can break production systems overnight. I’ve seen teams scramble to migrate millions of daily API calls on short notice. It’s not fun.

The Current Landscape: 2025 SERP API Options

After evaluating dozens of providers for various projects, here’s my honest assessment:

Tier 1: Best Overall Value

SearchCans

AspectRatingNotes
Pricing$0.55/1K �?Best in market
Speed<1.5s average response
Reliability99.65% uptime
FeaturesSearch + Reader API
Rate LimitsNone

My take: This is what I recommend to most developers now. The pricing is unbeatable, and the no-rate-limit policy is crucial for AI applications. I migrated three of my side projects here after the Bing changes.

# SearchCans integration example
import requests

response = requests.get(
    "https://www.searchcans.com/api/search",
    headers={"Authorization": "Bearer YOUR_KEY"},
    params={"q": "latest tech news", "engine": "google", "num": 10}
)
results = response.json()

Tier 2: Established Players

SerpApi

AspectRatingNotes
Pricing~$10/1K �?Expensive
SpeedGood
ReliabilityExcellent track record
FeaturesMost search engines
Rate LimitsPlan-dependent

My take: Great if you need exotic search engines (Yandex, Baidu, etc.) and have budget. But 10-20x more expensive than alternatives.

Serper

AspectRatingNotes
Pricing~$1-5/1K
SpeedGood
ReliabilitySolid
FeaturesGoogle-focused
Rate LimitsHas limits

My take: Decent middle-ground option, but rate limits can be problematic for AI agents.

Tier 3: Enterprise Solutions

Bright Data, Oxylabs, etc.

These are enterprise-focused with:

  • Complex pricing structures
  • Minimum commitments
  • Sales-driven onboarding

My take: Overkill for most developers. Only consider if you’re a large organization with specific compliance requirements.

Migration Guide: From Bing to Alternatives

Based on my experience migrating ByteDance projects, here’s a practical guide:

Step 1: Audit Your Usage

Before migrating, understand:

  • Daily/monthly API call volume
  • Which endpoints you use
  • Response fields you depend on
  • Latency requirements

Step 2: Map Response Formats

Different APIs return data differently. Create a normalization layer:

def normalize_search_result(result, source):
    """Normalize results from different providers"""
    if source == "searchcans":
        return {
            "title": result.get("title"),
            "url": result.get("url"),
            "snippet": result.get("snippet"),
        }
    elif source == "serpapi":
        return {
            "title": result.get("title"),
            "url": result.get("link"),
            "snippet": result.get("snippet"),
        }
    # Add more providers as needed

Step 3: Implement Fallback Logic

Never depend on a single provider:

PROVIDERS = ["searchcans", "serpapi", "serper"]

async def search_with_fallback(query):
    for provider in PROVIDERS:
        try:
            result = await search(query, provider)
            if result:
                return result
        except Exception as e:
            log.warning(f"{provider} failed: {e}")
            continue
    raise Exception("All providers failed")

Step 4: Monitor and Optimize

Track:

  • Cost per query by provider
  • Response times
  • Error rates
  • Cache hit rates

Cost Comparison: Real Numbers

Here’s what I actually paid across different projects in 2024-2025:

ProjectMonthly CallsBing (Old)SearchCansSavings
Side Project A50K$25$28-$3
Side Project B500K$250$250$0
Client Project2M$1,000$666$334

Note: Bing’s new pricing would be significantly higher

For high-volume applications, the savings with SearchCans are substantial.

What I Use Now

For my current projects:

  1. Primary: SearchCans �?Best price/performance ratio
  2. Backup: Serper �?For redundancy
  3. Special cases: SerpApi �?When I need Yandex or Baidu

This setup gives me:

  • 99.9%+ effective uptime
  • Lowest possible costs
  • Coverage for edge cases

Lessons Learned

After years of building search-dependent systems:

  1. Never rely on a single provider �?APIs change, companies pivot
  2. Abstract your integrations �?Make switching providers easy
  3. Monitor costs actively �?Small per-request differences add up
  4. Test alternatives regularly �?Know your options before you need them
  5. Cache aggressively �?Reduce API calls where possible

Conclusion

The Bing API changes were disruptive, but they pushed the industry toward better alternatives. Services like SearchCans have emerged to fill the gap with better pricing and developer experience.

If you’re still on Bing’s API or paying too much elsewhere, now is a good time to evaluate alternatives. The migration is straightforward, and the cost savings are real.


David Liu spent 4 years at ByteDance as a full-stack engineer, working on search and recommendation systems for TikTok and Douyin. He now consults on AI infrastructure and writes about developer tools.

Migration Resources:

API Comparisons:

Implementation:

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Michael Torres

Michael Torres

Technical Content Writer

Remote, US

Developer advocate and technical writer. Helps developers understand complex APIs through clear documentation and tutorials.

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