Exa vs Bing

A direct comparison of Exa and Bing's web search APIs, covering their core features, performance, and ideal use cases for connecting large language models to the internet.

Exa vs Bing: A Comparison for LLMs

When developing AI applications that require real-time web access, the choice of a search API is critical. Exa and Bing both offer APIs for this purpose, but they are built on fundamentally different philosophies, leading to significant differences in performance, relevance, and use cases.

Exa: The AI-Native Search Engine

Exa is a modern search engine built from the ground up for AI. Its core philosophy is to provide LLMs with a high-quality, relevant knowledge base from the web, designed to reduce hallucinations and improve factual accuracy.

Technology: Exa uses an in-house developed neural search index. It leverages embedding models and end-to-end Transformers to understand the semantic meaning of queries, rather than relying solely on keyword matching. This allows it to handle complex, nuanced queries that are common in AI workflows.
Relevance: Exa's search results are optimized for the needs of LLMs. It excels at finding specific, detailed, and technically relevant content. Case studies and internal benchmarks show that Exa consistently outperforms traditional search APIs like Bing in terms of result relevance for complex and technical queries.
Content Retrieval: The API is designed to return clean, ready-to-use content. Instead of just a title and snippet, Exa can provide the full text of a page, highlights, or custom summaries, making it easier for an LLM to consume and process the information without extra steps.
Freshness: Exa's web crawler is designed for real-time indexing, updating its database with new content frequently, ensuring that AI models have access to the latest information.

Bing: The Traditional Search API

Microsoft's Bing has been a long-standing web search provider, and its API has been a default choice for many developers. However, recent changes and its traditional architecture make it less suited for modern LLM applications.

Technology: Bing is a traditional search engine optimized for human-centric queries and ad-based revenue models. While it has incorporated some AI advancements, it still primarily relies on keyword-based ranking algorithms. Recent news indicates a shift away from a standalone Bing Search API towards Azure AI Agents, which has been a point of friction for some developers.
Relevance: Bing's search results are geared towards general-purpose queries and are often influenced by SEO. This can lead to less precise or overly broad results for complex, technical questions that require deep semantic understanding.
Content Retrieval: The Bing API typically returns standard search results with titles, URLs, and snippets. This requires developers to perform additional steps, such as web scraping, to retrieve the full content of a page for an LLM to use in a Retrieval-Augmented Generation (RAG) workflow.
API Usage: Developers have reported that migrating to the new Azure AI Agents can be complex and expensive, with higher costs per query and a more involved setup process compared to a simple API key. The new setup is also designed to be an add-on feature for specific Microsoft products, making it less of a flexible, standalone tool.

Conclusion

The core difference is in their design philosophy. Bing is a traditional search engine adapted for API use, but with limitations and a recent shift that makes it less developer-friendly for AI. Exa is an AI-native search engine specifically built to serve the unique needs of large language models. For applications that require deep semantic understanding, high factual accuracy, and streamlined content retrieval, Exa presents a compelling alternative to traditional search APIs. While Bing may still be a viable option for simple, keyword-based queries, Exa's neural search technology offers a significant advantage for sophisticated AI use cases like RAG and autonomous agents.

Looking to compare pricing between Exa and Bing?

Exa: The AI-Native Search Engine

Technology: Exa uses an in-house developed neural search index. It leverages embedding models and end-to-end Transformers to understand the semantic meaning of queries, rather than relying solely on keyword matching. This allows it to handle complex, nuanced queries that are common in AI workflows.

Relevance: Exa's search results are optimized for the needs of LLMs. It excels at finding specific, detailed, and technically relevant content. Case studies and internal benchmarks show that Exa consistently outperforms traditional search APIs like Bing in terms of result relevance for complex and technical queries.

Content Retrieval: The API is designed to return clean, ready-to-use content. Instead of just a title and snippet, Exa can provide the full text of a page, highlights, or custom summaries, making it easier for an LLM to consume and process the information without extra steps.

Freshness: Exa's web crawler is designed for real-time indexing, updating its database with new content frequently, ensuring that AI models have access to the latest information.

Bing: The Traditional Search API

Technology: Bing is a traditional search engine optimized for human-centric queries and ad-based revenue models. While it has incorporated some AI advancements, it still primarily relies on keyword-based ranking algorithms. Recent news indicates a shift away from a standalone Bing Search API towards Azure AI Agents, which has been a point of friction for some developers.

Relevance: Bing's search results are geared towards general-purpose queries and are often influenced by SEO. This can lead to less precise or overly broad results for complex, technical questions that require deep semantic understanding.

Content Retrieval: The Bing API typically returns standard search results with titles, URLs, and snippets. This requires developers to perform additional steps, such as web scraping, to retrieve the full content of a page for an LLM to use in a Retrieval-Augmented Generation (RAG) workflow.

API Usage: Developers have reported that migrating to the new Azure AI Agents can be complex and expensive, with higher costs per query and a more involved setup process compared to a simple API key. The new setup is also designed to be an add-on feature for specific Microsoft products, making it less of a flexible, standalone tool.

Conclusion