Cloud giants race to embed vector search at scale

In a groundbreaking move, AWS DynamoDB has integrated native vector search capabilities directly into its platform, allowing customers to perform real-time similarity queries on operational data without the complexity of managing separate vector databases or embedding pipelines. This serverless solution supports trillion-scale vectors with single-digit millisecond latency and over 99% recall, while offering flexible configuration options such as multiple distance functions (Euclidean, Cosine, Dot product) and attribute filters, empowering advanced AI applications like semantic retrieval, recommendation engines, and anomaly detection within existing DynamoDB workloads.

MongoDB Atlas has taken native vector search integration a step further by embedding auto-embedding capabilities powered by Voyage AI directly into its vector search index configuration. This innovation eliminates the traditional complexity of maintaining separate embedding services, vector stores, and synchronization layers, as documents are automatically re-embedded in real time upon any data changes, ensuring semantic search accuracy remains high and search quality does not degrade over time.

In August 2026, Google Cloud unveiled a breakthrough in vector search scalability with AlloyDB's new four-level hierarchical tree index, which dramatically reduces search complexity from O(N1/3) to O(N1/4). This architectural innovation enables efficient querying across billion-scale vector datasets, marking a significant leap in real-time AI application performance. By integrating advanced vector similarity search within a transactional PostgreSQL-compatible managed database, AlloyDB uniquely positions itself as a versatile platform that bridges traditional database reliability with cutting-edge AI workload demands.

Complementing its indexing advancements, AlloyDB incorporates sophisticated memory optimization techniques such as balanced tree configurations and condensed sampling sets. These enhancements allow the system to build and train large-scale indexes efficiently within existing memory constraints, all while preserving high recall accuracy. This careful balance of resource management and search precision underscores Google Cloud’s commitment to delivering scalable, performant vector search solutions tailored for the complexities of real-time AI workloads.

AWS has developed a specialized ecosystem of vector search solutions that integrate seamlessly with existing data stores such as OpenSearch, S3, DynamoDB, and Neptune, enabling agentic AI workloads without the need for data duplication or migration. This approach supports diverse workloads including hybrid search—which combines lexical and semantic methods—and large-scale retrieval, facilitating real-time, context-aware, and multimodal search experiences that enhance intelligent agent capabilities directly where the data resides.

By embedding vector search capabilities directly into established AWS services like Amazon OpenSearch Service, Amazon S3, and Amazon DynamoDB, AWS optimizes for workload-specific factors such as latency, cost, and access patterns. This co-location strategy eliminates cross-service data hops, significantly reducing latency and operational costs while leveraging the scalability and performance of existing databases, thereby allowing applications to run faster without the overhead of data synchronization or movement.

CORTO’s pioneering use of Amazon Aurora PostgreSQL with the pgvector extension to power billion-scale legal semantic search exemplifies a strategic choice to leverage existing PostgreSQL expertise rather than adopting specialized vector databases. Managing an immense dataset of 7.6 billion vectors within a 46 TB cluster, CORTO achieves sub-second query performance and cost-effective scaling, demonstrating that embedding vector search directly into a relational database can meet the demanding latency and scale requirements of real-time AI applications. This integration also allows for seamless combination of relational filters and similarity search in a single query, simplifying operations and accelerating time to production while maintaining a stringent 99.99% SLA.

CORTO’s multi-tenant architecture, designed to securely serve over 10,000 law firms, balances near real-time ingestion of critical legal documents with batch processing of less urgent data through a sophisticated layering of Integration, Data, and AI components. Utilizing Amazon Textract for tiered text extraction and Amazon SQS for priority-based ingestion, the system ensures logical per-firm isolation that upholds compliance mandates without sacrificing scalability. This architectural approach enables CORTO to deliver a secure, compliant, and highly scalable semantic search service tailored to the complex needs of the legal industry.