Yes, vector search can be implemented on the cloud. Cloud platforms provide the infrastructure and services necessary to deploy, scale, and manage vector search systems efficiently. By leveraging cloud-based databases, compute resources, and managed machine learning tools, developers can build vector search solutions that handle large datasets, perform real-time queries, and adapt to changing workloads. The cloud’s flexibility allows teams to avoid upfront hardware costs while accessing distributed systems optimized for high-dimensional data operations.
One common approach is using cloud-native databases or search services that support vector indexing. For example, Amazon OpenSearch Service offers the k-Nearest Neighbors (k-NN) plugin, which enables similarity search for vectors stored in OpenSearch indexes. Similarly, Google Cloud’s Vertex AI Vector Search (formerly known as Matching Engine) provides a fully managed service for building vector search applications. These tools abstract the complexity of managing infrastructure, allowing developers to focus on embedding generation and query logic. Cloud storage services like AWS S3 or Google Cloud Storage can also store raw vectors or embeddings, while serverless compute options (e.g., AWS Lambda) process queries or updates without manual scaling.
Another advantage is the integration of cloud-based machine learning services to generate embeddings. For instance, Azure Cognitive Services provides pre-trained models for text, image, or audio embeddings, which can be deployed via APIs. Developers can combine these services with vector databases like Pinecone (which offers a cloud-hosted version) or Milvus (deployable on Kubernetes in the cloud) to create end-to-end workflows. Challenges like latency or cost can be mitigated by selecting regions close to users, optimizing index structures, or using auto-scaling to adjust resources based on traffic. Overall, the cloud simplifies the operational overhead of vector search while enabling robust, scalable implementations.