To scale a vector database effectively on AWS, it’s important to architect for both horizontal and vertical scalability. Use sharding or partitioning strategies when dealing with very large datasets—most modern vector databases, including Zilliz Cloud, support distributed storage and compute. Running the database in a VPC ensures better network performance and isolation, and selecting the right instance types (such as compute-optimized or Graviton instances) helps maximize performance for embedding-heavy workloads.
Load balancing and replication are also important. For high-availability use cases, make sure your deployment spans multiple availability zones and includes replicas to handle failover scenarios. You can automate scaling by integrating with AWS services like Auto Scaling Groups or using Kubernetes with EKS for containerized deployments. Monitoring tools like CloudWatch and Prometheus can help you track CPU, memory, and latency metrics to inform scaling decisions in real time.
Security-wise, follow AWS’s best practices: enable encryption at rest and in transit, apply least-privilege principles using IAM roles and policies, and use security groups or network ACLs to restrict access. If your use case involves sensitive data, ensure compliance with relevant standards like HIPAA or SOC 2. For third-party solutions like Zilliz Cloud, confirm that the service runs within your preferred AWS region, supports private connectivity (like VPC peering), and offers audit logging. By combining these security and scaling practices, developers can run reliable, secure, and performant vector search infrastructure on AWS.