RocksDB as the Memory Layer for an Enterprise AI Platform
Most conversations about AI infrastructure focus on models, vector databases, GPUs, and retrieval pipelines. Those components are important, but during the development of an enterprise AI-assisted platform, we ran into a different problem. We needed a place to store rapidly changing project context, generated artifacts, user modifications, intermediate reasoning results, and session-level state. The challenge was not storing terabytes of information. The challenge was managing thousands of small reads and writes while keeping latency low and deployment simple. Our first instinct was to use a traditional database. That is what most software teams do. Databases are familiar, reliable, and well understood. The problem was that our workload did not behave like a typical business application. Users were continuously generating API specifications, editing artifacts, creating mock services, updating configurations, and asking AI assistants to reason over information that changed every few seconds. The amount of state was not enormous, but the...
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