The Bing Search API was retired on August 11, 2025. If your application depended on it for web search, real-time data, or feeding results into an AI pipeline, you need a replacement. Microsoft's recommended migration path (Azure AI Foundry) is a platform commitment, not a drop-in swap.

This guide covers five alternatives for developers and AI builders: what each one does well, where it falls short, and when to choose it.

The Bing Search API (Microsoft Bing Web Search API) was a REST API that gave developers programmatic access to Bing's search index. It returned web results, images, news, videos, and related queries as structured JSON, making it a go-to for building search features, aggregating real-time content, and powering AI pipelines that needed live web data.

Developers used it for knowledge bases, news aggregators, competitive monitoring, and feeding search results into LLMs for retrieval-augmented generation.

The Bing Search API was actually a suite of specialized sub-APIs under one umbrella:

All of these are gone. Replacements below cover the most common use cases, but no single API replicates the full breadth of the suite.

Microsoft announced the retirement of all Bing Search APIs on May 15, 2025, with full decommissioning on August 11, 2025.

The move was driven by Microsoft's strategic shift toward AI-mediated search: embedding search inside AI agents rather than exposing it as a raw data endpoint. The recommended migration path is Grounding with Bing Search inside Azure AI Foundry, which requires setting up an entire Azure project with resource groups and model deployment. It's a platform commitment, not a drop-in API replacement.

The discontinuation was controversial. API creation was silently disabled in March 2025, weeks before any official announcement, leaving developers scrambling. Teams that had built on top of Bing's API had roughly three months to migrate after the formal notice.

Before its discontinuation, the Bing Search API was a common building block across a wide range of developer applications:

• Real-time web search in AI pipelines: Feeding live search results into LLMs for retrieval-augmented generation (RAG) and grounding responses with up-to-date web data
• News and content aggregation: Pulling the latest articles and headlines from across the web into dashboards, newsletters, or media monitoring tools
• Competitive intelligence: Monitoring mentions, pricing changes, and new content from competitors by querying Bing programmatically on a schedule
• Knowledge bases and internal search: Augmenting internal documentation with web results, or building search features into products that surfaced relevant external content
• AI agent web access: Giving autonomous agents the ability to search the web as part of multi-step research and decision-making workflows (see OpenClaw + Firecrawl as a real-world example)
• SEO and SERP monitoring: Tracking keyword rankings and search result changes across Bing's index

Each of these use cases has a clear alternative path today. The right tool depends on which part of that stack matters most to your application.

Not all search APIs are equivalent replacements. Here are the criteria that matter most depending on your use case:

• Data freshness: How current are the results? Some APIs index content with a delay; others return live results. For competitive monitoring or news aggregation, recency matters.
• Pricing model predictability: Bing's tiered pricing was based on transactions and volume tiers. Look for tools with flat, per-request pricing so costs scale predictably as your usage grows.
• Output format: Bing returned raw SERP JSON that required post-processing before it was useful in an AI context. Prioritize alternatives that return AI-ready output (clean markdown, structured JSON) if your pipeline feeds results into an LLM.
• AI-readiness: If you're building RAG pipelines or AI agents, look for tools with native LLM integrations, agent endpoints, or structured extraction. A raw SERP API still leaves you with a post-processing step.
• Enterprise compliance: Teams handling sensitive queries in healthcare, legal, or financial contexts should check for SOC 2 certification, data residency options, and no-tracking policies. Not all alternatives meet these requirements.
• Full-page content vs. snippets: Bing returned snippets. If your use case needs the full content of a page (for summarization, extraction, or RAG), you either need an API that includes extraction or a separate scraping tool alongside.

Each alternative takes a different approach to web search and data extraction. Here's what they each offer.

Firecrawl is an excellent choice for autonomous web data extraction and acts as the complete web data layer for building AI agents and workflows in 2026. Over half a million developers and 80,000+ companies trust and use Firecrawl to power their AI applications and data pipelines.

Where Bing returned links and snippets from its index, Firecrawl is extraction-first, returning clean, LLM-ready markdown or structured JSON from the actual pages. Where the Bing API required post-processing before the data was useful in an AI context, Firecrawl's output is ready to go straight into your pipeline.

It's also fully open source (83,000+ GitHub stars) and self-hostable for teams with data residency or compliance requirements.

Bing returned links and snippets. Firecrawl's Search endpoint finds pages and extracts their full content in a single call, returning markdown or structured JSON ready to feed into an LLM.

from firecrawl import Firecrawl
 
firecrawl = Firecrawl(api_key="fc-YOUR_API_KEY")
 
results = firecrawl.search(
    query="best open source LLMs 2026",
    limit=5,
    scrape_options={"formats": ["markdown"]}
)
 
for r in results.data:
    print(r["title"], r["url"])
    print(r["markdown"][:500])

Describe what you need in plain English and Firecrawl's agent pulls exactly that. No CSS selectors, no XPath, no parsing logic required.

from firecrawl import Firecrawl
from pydantic import BaseModel, Field
from typing import List, Optional
 
firecrawl = Firecrawl(api_key="fc-YOUR_API_KEY")
 
class Article(BaseModel):
    title: str = Field(description="Article title")
    summary: Optional[str] = Field(None, description="Brief summary")
    published_date: Optional[str] = Field(None, description="Publication date")
 
class ArticlesSchema(BaseModel):
    articles: List[Article]
 
result = firecrawl.agent(
    prompt="Find the latest news articles on example.com and extract their title, summary, and publication date",
    schema=ArticlesSchema
)
 
print(result.data)

Many sites hide content behind "Load More" buttons, require form submissions, or spread data across paginated views. The Firecrawl Agent handles these interactions automatically: clicking buttons, filling fields, navigating pagination, without writing custom automation code for each site.

For batch workflows, Parallel Agents let you run hundreds or thousands of /agent queries simultaneously, using an intelligent waterfall that tries fast retrieval first and only escalates to deeper research when needed.

For sites that require authentication, multi-step flows, or interactions that go beyond standard scraping, Firecrawl's browser sandbox gives you a full programmable browser environment exposed via API. You can script real browser interactions, capture screenshots, fill forms, and extract content from sites that require login or complex user flows, all without managing headless browser infrastructure yourself.

This covers the scenarios that neither Bing's API nor most search-first tools ever handled.

Here's Firecrawl Browser Sandbox fetching dozens of patents with a single prompt:

• Scrape: Convert any URL to markdown or structured JSON
• Search: Find pages and extract their content in one call
• Agent: Autonomous multi-step data gathering
• Map: Discover full site structure in seconds
• Crawl: Navigate entire sites without sitemaps
• Browser: Programmable browser sandbox for authenticated and complex interactions

Choose Firecrawl when you need a complete web data layer for your AI agent or workflow, not just search snippets. It covers every part of the web data stack: finding pages, extracting their full content, navigating complex sites, and handling authenticated or dynamic flows via the browser sandbox. All in one API, with flat predictable pricing and LLM-ready output at every step.

You can use Firecrawl as a REST API, an MCP server for direct integration with AI tools like Claude and Cursor, or a CLI for scripting and local workflows. It also works with no-code tools like Lovable and n8n.

Try it free with 500 credits (no card required) or explore the docs.

Exa is an AI-powered search engine built specifically for machines. Unlike Bing's keyword-based index, Exa uses embeddings-based semantic search to understand meaning rather than matching terms, making it a strong fit for AI agents and RAG systems that need conceptually relevant results, not just top-ranked keyword matches.

Semantic understanding instead of keyword matching

Bing matched keywords and intent signals. Exa understands the meaning of a query — making it better at surfacing conceptually related content even when the exact words don't appear. This is particularly useful for research tools and AI agents that need thematically relevant pages rather than top-ranked results.

Find Similar endpoint

One of Exa's unique features: feed it a URL and get back semantically similar pages. There's no Bing equivalent. This is useful for competitive research, content discovery, and building recommendation-style features into your application.

Main APIs:

• Search: Semantic queries that understand context and intent
• Contents: Retrieve clean, parsed content from search results
• Find Similar: Feed one URL, get semantically similar pages
• Answer: Summarized responses with citations
• Research: Automated deep research with structured JSON output

Pricing: Free tier with 1,000 credits, then $49 for 8,000 credits. Credit consumption varies by query complexity (75–750+ credits per search), which can make cost prediction harder than flat-rate tools.

Choose Exa when semantic relevance matters more than broad coverage. It's well-suited for research tools, content discovery pipelines, and AI applications where you need pages that are conceptually related to a topic — not just pages that rank for a keyword.

Exa is search-first. For deep structured extraction beyond snippets, you'll still need a dedicated tool like Firecrawl alongside it. For a full breakdown, see Firecrawl vs. Exa and our Exa alternatives guide.

Tavily is a search API built specifically for AI agents and LLMs, with a focus on retrieval-augmented generation (RAG) applications. It takes a more direct approach than Exa: straightforward pricing, fast response times, and AI-optimized snippets formatted for LLM consumption.

Results formatted for LLMs, not humans

Bing returned raw SERP JSON that needed cleaning and formatting before it was useful in an LLM context. Tavily returns AI-optimized snippets with relevance scores, structured for direct injection into agent context windows without additional processing.

Transparent, predictable pricing

Tavily charges a flat $0.008 per credit with clear tiers starting at $30/month. You know exactly what you'll pay before making a request.

Native LangChain integration

Tavily is popular in the LangChain ecosystem with native integrations that make adding real-time web search to an agent straightforward. The setup is minimal compared to building a custom search integration.

Four core endpoints:

• Search: Real-time web queries with AI-optimized, ranked results
• Extract: Pull full content from specific URLs with JavaScript rendering
• Crawl: Navigate entire websites using natural language instructions
• Map: Discover website structure before extraction

For a detailed comparison, see Firecrawl vs. Tavily and our Tavily alternatives guide.

Choose Tavily when you need AI-optimized search results that feed directly into agent context without additional formatting, especially if you're already using LangChain. It handles the "find relevant content and rank it for my LLM" use case more directly than raw SERP output from Bing required.

Note that Tavily is search-first. For deep structured extraction beyond snippets, you'll still need a dedicated scraping tool.

Brave Search API stands out with its own independent search index of 30+ billion pages. Unlike search APIs that route queries through Google or Bing under the hood, Brave crawls and indexes the web itself, which is a meaningful distinction now that Bing's API is gone.

Independent index, no Bing or Google dependency

Brave runs its own crawler and index. For teams that chose Bing specifically as an alternative to Google, Brave offers genuine independence. You're not re-routing through the same underlying data. The index covers 30B+ pages with millions of new pages updated daily.

Privacy-first architecture

SOC 2 Type II certified with no user tracking. Brave reports not building user profiles or selling data, which is relevant for healthcare, legal, or financial applications handling sensitive queries.

Search Goggles for customization

Customize search behavior by discarding specific domains or re-ranking results. Build custom search experiences tailored to your use case without forking an entire search engine. This level of control wasn't available with Bing's API.

Specialized endpoints:

• Web Search: General queries across Brave's full index
• AI Grounding: Optimized results for LLM context
• Image, Video, News: Vertical-specific search
• Suggest: Autocomplete and query suggestions
• Spellcheck: Query correction

Choose Brave for privacy-centric applications or when you need a fully independent index not backed by Bing or Google. The straightforward per-request pricing and no-tracking policy make it a clean starting point.

Brave returns raw JSON SERPs rather than AI-optimized structured output, and like Bing, it doesn't provide full-page content extraction. For AI workflows that need complete page content, you'll need to pair it with a scraping tool. Read our Brave Search API alternatives guide for more.

SerpAPI takes a different approach from the other alternatives. Instead of building its own search index, it scrapes and parses search engine results pages from Google, Bing, DuckDuckGo, and 25+ other engines, returning them as clean structured JSON.

Access Bing and more via one API

If you were on the Bing Search API specifically for Bing results, SerpAPI can still give you Bing SERP data, plus Google, DuckDuckGo, Yahoo, Baidu, and more from a single integration. Multi-engine coverage in one place, which the native Bing API never offered.

Rich SERP structure

SerpAPI parses the full SERP: organic results, ads, knowledge panels, featured snippets, local packs, "People also ask," images, and more, all as structured JSON. This level of SERP detail wasn't always consistent with the native Bing API's output.

Real-time results

SerpAPI scrapes live results in real time, so you're always getting current data. Useful for rank tracking, competitive analysis, and SEO tools that need to monitor search result changes.

Pricing: Plans start at $50/month for 5,000 searches. Free tier includes 100 searches/month.

Choose SerpAPI when you need structured SERP data from multiple search engines — especially if you need Google results alongside Bing, or need rich SERP features like featured snippets, knowledge panels, and ad data.

SerpAPI returns SERP metadata (links, snippets, ads), not full page content. For AI pipelines that need to extract content from the actual pages, you'll need a separate tool like Firecrawl.

If you used the Bing Search API to feed data into an AI pipeline, Firecrawl replaces it and extends it.

Bing returned links and snippets from its index. Firecrawl finds those pages and extracts their full content in a single API call, clean, structured, and ready for an LLM.

For teams that used Bing's API to gather real-time web data for RAG or AI agents, Firecrawl's Search endpoint covers the search layer while adding structured extraction on top. More useful data, less post-processing.

The Bing Search API retirement left developers without a direct replacement from Microsoft. The five alternatives above each fill a different part of what Bing offered:

• Firecrawl if you need structured, LLM-ready content extracted from the web, not just links and snippets
• Exa if you need semantic search that understands meaning, not just keywords
• Tavily if you need AI-optimized search for RAG pipelines with predictable pricing and LangChain support
• Brave Search API if you need an independent index with privacy guarantees and no Bing or Google dependency
• SerpAPI if you need structured SERP data from multiple search engines including Bing and Google

For AI builders who were using Bing's API to feed content into LLMs, Firecrawl's combination of search, extraction, and Agent capabilities covers the use case, with cleaner output and no platform lock-in.

Try Firecrawl free with 500 credits (no card required), try the playground without signing up, or explore the docs to see how it works in practice.