It seems like artificial intelligence is taking over the internet these days. According to an Ahrefs study, 63 percent of sites have received AI web traffic, with 98 percent of that traffic coming from ChatGPT, Perplexity and Gemini. To deliver accurate and relevant answers to user queries, these chatbots rely on steady streams of real-time data — a resource that can be quickly acquired through the use of search APIs.
What Is a Search API?
A search API is an interface that delivers user queries to a search engine and receives the search engine’s results. It provides chatbots, virtual assistants and other AI tools with real-time data from the internet, improving the accuracy and relevance of their answers to user queries.
A search API is an interface that sends user queries to a search engine and receives the results, connecting large language models and AI applications with real-time information from the internet. This makes it possible to develop advanced AI agents, AI assistants, chatbots and other tools. At the same time, these products may threaten an internet built on human-generated content, making it all the more crucial to understand search APIs and their role in reshaping the World Wide Web as we know it.
Search Engines vs. Search APIs for LLMs
To understand search APIs, it helps to start by reviewing search engines. A traditional search engine is software that continuously crawls the internet, analyzing web pages and storing them in a way that makes it easy to find and retrieve information — a process known as indexing. It can then quickly locate and share relevant content to address a user’s query. Google, Yahoo and Bing are just a few examples of popular search engines.
Search APIs fulfill a role similar to search engines, except with a focus on LLMs. A search API is an interface that directly interacts with a search engine, sending user queries to the search engine and conveying the results back to the user. Developers use search APIs to equip AI applications with search capabilities. All that’s needed is for an application to submit a query to the search API, essentially ‘calling’ that API to initiate the search process.
“Calling a search API is similar to going to a search engine and entering a keyword. For regular search users using a traditional search engine, the results are formatted into a list of links and widgets that helps them find the information they’re looking for,” Subu Sathyanarayana, VP of Engineering for Search at Brave, told Built In. “APIs like Brave Search API provide the same underlying data in a raw, programmatically accessible format, which developers can integrate directly into their applications.”
Why Do LLMs Need Specialized Search APIs?
Massive volumes of real-time data are what drive the performance of high-quality AI models, and there’s no better source for this information than the internet. The problem is that scraping data from across the World Wide Web is a gargantuan task that very few companies have the resources to take on.
“It’s like trying to imagine the universe, but no one knows the size of the universe. It’s not like you have a ready-made map that you can read,” Or Lenchner, CEO at Bright Data, told Built In. “Same goes for the internet: It’s not like there’s a book describing all of the websites, or a database describing all the websites. So, just crawling the whole web, it’s something that you never know if you accomplished or not.”
Thankfully, search engines like Google have a head start, having built out extensive search indexes that span decades and are constantly updated. Connecting to these search engines via search APIs delivers the vast amounts of real-time data needed to train large language models and create AI apps that generate relevant responses to user queries. Without search APIs, LLMs would be limited to training on smaller, static data sets that can quickly become outdated and reduce a model’s performance.
However, simply compiling data from the internet can expose AI models to inaccurate information, tainting their performance with biases and inaccuracies. That’s why some search engines filter the data they collect, and search APIs can come with filtering features as well. These measures ensure that LLMs are only fed reliable content from trustworthy sources to improve their accuracy and credibility in the eyes of users.
How Do Search APIs for LLMs Work?
Generative AI tools may be able to respond to user queries in a matter of seconds, but there’s a lot that goes into this process behind the scenes. Here’s a more detailed overview of how search APIs support these applications:
- Submit a user query: A user submits a natural language query, setting criteria like keywords, filters and sorting requirements.
- Call the search API: An application typically converts a request into HTTP format and sends it to the API. This is known as ‘calling’ the API.
- Process the request: Upon receiving the request, the search API reviews the query and analyzes elements like keywords and filters.
- Translate the request: The search API translates the request into a language supported by the relevant search engine, such as JSON or SQL.
- Execute the request: The search API sends the request to a search engine, which compares the request against its index and pulls relevant information.
- Process the results: The search engine sends its results to the search API, which cleans the data, removes any noise and structures the data as needed.
- Send a response: After organizing the results, the search API returns a response to the application that submitted the request.
- Refine the results: Depending on the application, the results may undergo further processing before being converted into a user-friendly format.
Search APIs may also require users to create an API key to verify their identity, especially if an AI application deals with private information. After entering the correct key, a user can view the application’s natural language response.
Examples of Search APIs for LLMs
Given the increasing importance of search APIs in the age of AI, here are some of the more prominent search APIs to know.
Exa
Exa’s search API is described as the “first meaning-based web search API powered by embeddings.” The tool is designed with agentic AI in mind, offering a range of filters for feeding AI customized content, summaries of web pages when requested and low latency. In its most recent funding round, Exa tacked on $85 million to raise its valuation to $700 million.
You.com
You.com’s search API gives users access to more than 10 billion web pages, including news content seconds after it’s published. It also integrates with Amazon Web Services, Databricks and OpenAI’s gpt-oss models. You.com is now valued at $1.5 billion after the company earned $100 million in Series C funding.
Tavily
Tavily has designed a search API to support various AI tools, including AI agents, AI assistants and chatbots. The API brings in real-time data from the web, and a quick process for getting an API key makes it easy to get started. With the buzz around AI agents, Tavily secured $20 million in Series A funding.
Parallel Web Systems
Parallel Web Systems backs deep research for AI agents with its suite of APIs, including its search API. Intended to simplify the process of scraping the web and extracting insights, the API cuts down on latency while enabling users to tailor results to their needs. Expect to hear more from Parallel Web Systems, which recently scored $30 million in funding.
Sonar
Sonar is Perplexity’s API platform that fuels two APIs: Sonar and Sonar Pro. While the Sonar API is lightweight and best for creating simple Q&A features, the Sonar Pro API can handle more complex, multi-step queries. In addition, Perplexity has added its Perplexity Search API to the lineup, giving developers more options to choose from.
Brave Search API
Brave’s search API is informed by a search index containing 30 billion pages that undergo 100 million daily updates. An AI grounding feature ensures the API pulls information from authoritative sources to increase accuracy and reduce hallucinations. As a result, the search API can be used to train foundation models, develop coding assistants and more.
Firecrawl
Making a name for itself through its open-source crawling tool, Firecrawl has also released a search API that lets users customize the type of output, set parameters for more personalized searches and decide whether to scrape the results. Firecrawl is coming hot off a funding round of $14.5 million as the company looks to expand.
Linkup
Linkup is a French startup that offers two versions of its search API to users, with Linkup Standard completing basic searches while Linkup Deep uses chain-of-thought reasoning to perform more detailed searches. Either way, Linkup makes an effort to connect to reliable sources and works well with platforms like LangChain and Zapier.
SerpApi
Although SerpApi has search APIs for Bing, Yahoo, DuckDuckGo and other search engines, the startup is best known for its Google Search API, which is compatible with popular programming languages like Python, JavaScript and Ruby. Interestingly enough, OpenAI has reportedly been using Google search results scraped by SerpApi to refine ChatGPT’s outputs.
Bright Data
Bright Data is a web data platform that provides several APIs, including a SERP API — a type of search API that collects data directly from a search engine results page, or SERP. Bright Data’s API taps into some of the biggest search engines, including Google, Yandex and Bing. It can then deliver outputs in HTML, JSON or Markdown formats in as little as one second.
Search APIs and the Future of Internet Browsing
Search APIs have the power to transform how the internet operates by driving a range of generative AI applications — and not everyone’s thrilled about it.
Take AI overviews, for example. According to a Pew Research survey, Google users who come across an AI-generated summary click on a traditional search link during only 8 percent of visits. This means reduced traffic for various websites and less incentive for digital advertisers to pay for ad space on web pages, threatening the income of news outlets, authors and other content creators. In response, several businesses and individuals have filed lawsuits against AI companies, which could affect search APIs and their ability to gather information from the internet.
Despite this fierce backlash, more tech companies are buying into an AI-driven digital world. This past year, Anthropic launched a web search API feature to equip its chatbot Claude with real-time data; Microsoft released collaborative AI agents that can work together to complete tasks; and Google introduced an AI mode that replaces lists of blue links with AI-generated responses to queries. While fears over AI bots taking over the internet are very real, search APIs and the AI tools they power can challenge traditional search and established tech titans.
“Search APIs are crucial because there’s a perennial need for real-time web data,” Sathyanarayana said. “It’s very important to have options beyond Google and Microsoft to preserve choice and keep the Web open.”
Indeed, search APIs will only become more essential as companies shift away from static search to a more dynamic experience where AI agents, chatbots, virtual assistants and other tools bring relevant information straight to the user. According to Bright Data’s Lenchner, more companies will likely rely on search APIs to provide these products with the real-time data needed to address user queries. In short, search APIs are here to stay.
“What you’re seeing today with all these new companies, or even mature companies, that are working on these products, is that they don’t really want to keep collecting everything anymore and indexing it,” Lenchner said. “They just want to call a search API that someone built that can do it for them, get that snippet of information and push it into their product towards the eyes of the users.”
Frequently Asked Questions
How do LLMs search?
When answering user queries, LLMs connect to real-time data from the web by using a search API — an interface that directly interacts with a search engine. An LLM sends a user query to the search API, which then delivers the query to a search engine, processes the search engine’s results and shares the final result with the LLM.
What makes Exa different from Google?
Exa focuses on search tools for AI applications. In particular, its web search API taps into search engines, equipping AI agents and other products with real-time information. Meanwhile, Google’s main offering is its search engine, which does the work of crawling the internet and indexing web pages. Search APIs like Exa’s can then connect to a search engine like Google and share relevant content with AI applications.
Can LLMs use normal search engines effectively?
LLMs can use traditional search engines through techniques like retrieval augmented generation (RAG), which enables LLMs to collect information from authoritative data sources. However, search APIs may be more efficient for simply gathering real-time data, and they can be more cost-effective than implementing a RAG system.
