May 2026 Summaries

Machine learning (ML) and artificial intelligence (AI) systems rely on complex data infrastructures that must accommodate large datasets and intricate inference paths, often leading to challenges in latency, scalability, and cost management. The growing complexity of ML workloads necessitates databases that can handle training, online feature serving, and vector retrieval, each with distinct requirements and bottlenecks. Aerospike, PostgreSQL with pgvector, Apache Cassandra, Milvus, Weaviate, Qdrant, Vespa, Elasticsearch, ClickHouse, and Neo4j are highlighted as prominent databases, each excelling in different aspects of ML and AI architecture, such as low-latency operations, vector search, and hybrid search capabilities. The choice of database impacts not only performance and cost but also staff workload, as systems with predictable latency and comprehensive capabilities reduce the need for overprovisioning and integration complexity. Balancing specialized systems with general-purpose solutions, such as Aerospike's Hybrid Memory Architecture, can streamline the ML infrastructure by consolidating workloads while minimizing duplication and operational overhead.

Redis, a popular in-memory data store, excels in providing low-latency reads and simple data models, making it a favored choice for teams needing speed without complexity. However, its architectural design, characterized by single-threaded processing and reliance on RAM for data storage, presents significant challenges as datasets grow and conditions change in production environments. These limitations lead to increased infrastructure costs, engineering time, and an inconsistent user experience, especially under high-load conditions or when datasets exceed available memory. Redis' performance degrades when it hits memory ceilings, experiences failover events, or faces unpredictable latency in multi-step processes, prompting many organizations to consider alternatives like Aerospike. Aerospike offers a more scalable solution by decoupling performance from memory constraints and utilizing SSDs, thus reducing the need for workarounds and enabling efficient capacity planning. As highlighted in benchmarks, Aerospike provides higher throughput and lower latency, offering a cost-effective and reliable alternative for systems requiring predictable performance at scale.

Aerospike's integration with Datadog enhances operational visibility for Site Reliability Engineering (SRE) and DevOps teams by embedding its database monitoring into existing observability platforms, thus avoiding the need for separate systems. This integration features a flexible monitoring stack, with the Aerospike Prometheus Exporter (APE) as its foundation, allowing metrics to be routed into various platforms including Datadog, Grafana, and OpenTelemetry-compatible pipelines. The native Datadog integration offers eight pre-built dashboards that provide insights into cluster health, namespace capacity, node performance, and more, with a focus on latency histograms for detailed diagnostics. Setting up the integration involves installing the Datadog agent and Aerospike Enterprise check on each node, or alternatively using the Prometheus Exporter in OpenTelemetry mode to send metrics directly to Datadog Cloud. This initiative is part of Aerospike's broader strategy to make its database a well-instrumented, observable system within diverse tech stacks.