Several cloud providers offer AI-native or vector database services designed to handle high-dimensional data like embeddings, which are crucial for machine learning and similarity search tasks. These services are optimized for fast indexing and querying of vectors, making them essential for applications like recommendation systems, semantic search, and LLM-based workflows. Major cloud platforms like AWS, Azure, and Google Cloud provide managed solutions, while specialized providers like Pinecone offer dedicated vector databases. Below, I’ll outline specific examples and their use cases.
AWS offers multiple services tailored for vector operations. Amazon OpenSearch Service supports k-nearest neighbors (k-NN) search, enabling vector similarity queries alongside traditional text search. Developers can index vectors and combine keyword matches with semantic search, useful for hybrid retrieval-augmented generation (RAG) applications. For PostgreSQL users, Amazon Aurora supports the pgvector extension, allowing relational databases to store and query vectors using SQL. AWS also integrates vector capabilities into its AI services: Bedrock’s Titan embeddings can be stored in services like OpenSearch for LLM context retrieval. These tools are tightly integrated with AWS ecosystems, simplifying deployment for teams already using AWS infrastructure. Developers can start with OpenSearch’s managed clusters or extend Aurora databases with pgvector, depending on their existing stack.
Microsoft Azure provides Azure Cognitive Search, which added vector search support in 2023. It allows indexing vectors alongside text, images, or other data, enabling hybrid search scenarios. For example, a retail app could combine product descriptions (text) with image embeddings (vectors) to power multi-modal recommendations. Azure’s AI services, like Azure OpenAI, generate embeddings that can be stored and queried directly within Cognitive Search. Developers can use REST APIs or SDKs to manage indexes, making it accessible for Python or .NET environments. Azure’s approach emphasizes integration with its broader AI toolkit, streamlining workflows for teams building RAG pipelines or recommendation systems. For smaller projects, Cosmos DB offers limited vector search capabilities via integration with LangChain, though it’s less optimized than dedicated solutions.
Google Cloud and Pinecone round out the ecosystem. Google’s Vertex AI Vector Search (formerly Matching Engine) is a managed service for large-scale similarity searches, supporting billions of vectors. It’s designed for integration with Google’s ML pipelines, allowing embeddings from models like TensorFlow or Vertex AI to be indexed and queried with low latency. Firestore also added vector search in 2023, enabling real-time updates and queries for applications like chatbots. Meanwhile, Pinecone stands out as a fully managed, standalone vector database. It offers high performance for dynamic datasets, such as real-time updates in fraud detection systems, and supports metadata filtering for complex queries. Pinecone’s API-first approach makes it framework-agnostic, appealing to teams using diverse ML tools. While cloud providers bundle vector search within broader ecosystems, Pinecone focuses solely on optimizing vector operations, often providing better scalability for specialized use cases.