Google Gemini

Short answer: if you mean “web search APIs for AI agents/RAG,” the brands people most commonly evaluate/use right now are Tavily, Exa, Brave Search API, Perplexity Sonar, OpenAI Web Search, SerpAPI/Serper, and Google Gemini/Vertex grounding.

My top recommendations

What I’d choose by use case

• General AI agent that needs the web: start with Tavily.
• Research/discovery where keywords are not enough: use Exa.
• Need an independent search index + LLM-ready chunks: use Brave Search API.
• Want an answer API, not a retrieval API: use Perplexity Sonar or OpenAI Web Search.
• Need Google SERP parity / SEO / local/maps/shopping SERPs: use SerpAPI or Serper.
• Enterprise on Google Cloud: use Gemini/Vertex Grounding with Google Search.
• Avoid starting with Bing Search API for new builds: Microsoft announced the Bing Search APIs retired on August 11, 2025. (learn.microsoft.com) |

Don’t confuse these with “search over your own data”

If by AI search API you mean search inside your own documents/products/knowledge base, the shortlist changes:

• Azure AI Search — strong for enterprise RAG, especially on Azure; Microsoft’s RAG guidance discusses Azure AI Search for retrieval and vector search. (learn.microsoft.com)
• Algolia AI Search / NeuralSearch — best for ecommerce/product/app search; combines keyword and semantic search in an API-first product. (algolia.com)
• Pinecone — common vector database for RAG/semantic retrieval. (pinecone.io)
• Weaviate — good open-source/commercial vector DB with keyword, vector, hybrid search, filters, and RAG features. (docs.weaviate.io)

My practical recommendation: for most teams building an AI agent today, start with Tavily + a page extractor/crawler when needed, evaluate Exa for semantic discovery, and keep Brave as a strong production alternative if latency, independent indexing, or privacy matter.

There’s no single “best” AI search API — it depends on whether you need semantic/vector search, full-text + AI ranking, web search for agents, or RAG-ready retrieval. Here are the most popular, proven options people actually use in production:

1. Full-Featured AI Search Platforms

These handle indexing, relevance, typo tolerance, faceting, and now hybrid (keyword + vector) search out-of-the-box.

• Algolia – The most popular hosted search API. Excellent relevance, instant search, analytics, and AI/vector features. Best for e-commerce, SaaS apps, and developer-friendly search.
• Typesense – Open-source, blazing fast, with built-in vector search and synonym support. Great alternative to Algolia if you want self-hosting or lower cost.
• Meilisearch – Open-source, simple, fast, with a pleasant DX. Increasingly adding AI/vector capabilities. Good for small/medium projects.
• Elastic / OpenSearch – The industry standard for log/search infrastructure. With vector fields, dense/sparse embeddings, and ELSER, it’s now a strong AI search platform. Great if you already run the ELK stack.

2. Vector Databases & Semantic Search Engines

These are the core of RAG (Retrieval-Augmented Generation) and “AI search” over your own documents.

• Pinecone – The most widely adopted managed vector database. Simple API, very scalable, built for production. Common choice for AI startups.
• Weaviate – Open-source vector database with built-in modules for embeddings, hybrid search, and generative search. Great for semantic search + LLM workflows.
• Qdrant – High-performance Rust-based vector database. Great filtering, hybrid search, and on-prem/cloud options. Very popular in AI projects.
• Milvus – Heavy-duty open-source vector database with massive scale. Good for large enterprise AI search.
• Chroma – Lightweight, developer-friendly vector store often used in AI tutorials and quick prototypes.

3. Web Search APIs for AI Agents / LLMs

If your AI needs to search the live web and return clean, LLM-friendly context, these are the top picks:

• Tavily – Built specifically for AI agents. Returns concise, filtered, synthesized search results. Used heavily in LangChain and custom GPT-like apps.
• Exa (formerly Metaphor) – An AI-native web search API that understands content semantics and can find pages based on concepts, not just keywords.
• Brave Search API – Independent web index, privacy-friendly, affordable, with an API that supports AI answer contexts. Solid and reliable.
• SerpApi – Scrapes Google/Bing results and returns structured JSON. Not AI-native, but very popular for SEO, monitoring, and AI pipelines that need real search-engine results.
• Perplexity API – Combines web search + LLM answer generation. You get citations, but less control over the underlying retrieval.

4. Enterprise AI Search (Managed / Cloud)

• Azure AI Search (formerly Cognitive Search) – Microsoft’s full-featured AI search service with vector search, semantic ranking, and native RAG integration with Azure OpenAI. Excellent for enterprise.
• Google Vertex AI Search – Google’s managed search solider with natural language + LLM grounding. Great for e-commerce, websites, and internal data.
• Amazon Kendra – AWS’s intelligent search service using ML and natural language. Good for enterprise document search across many connectors, though some find it pricey and slower to tune.

What do most people actually use?

• For product search / e-commerce: Algolia or Typesense.
• For RAG / document Q&A: Pinecone, Weaviate, or Qdrant + OpenAI embeddings.
• For AI agents with live web access: Tavily or Exa.
• For enterprise-scale search: Elastic, Azure AI Search, or Google Vertex AI Search.

Pro tip: Many teams combine two APIs — e.g., use a vector database for semantic retrieval, then an LLM API (OpenAI, Anthropic) for summarization. If you can’t decide, start with Typesense or Qdrant (cheap, self-hostable, full-featured) and scale up later.