Petabyte-Scale AI Memory with Serverless Java

Wed, 15 Apr 2026 07:00:01 +0000

The current wave of Generative AI innovation is built on a paradox. While models are becoming more powerful and efficient, the infrastructure required to operate them at scale is becoming increasingly wasteful. Enterprises are investing millions into compute, storage, and energy, yet a significant portion of these resources remains idle. Modern vector databases, graph systems, and caching layers are based on a monolithic architecture run as always-on clusters, consuming CPU and RAM regardless of whether data is actively accessed. Studies and industry analyses consistently show that up to 80% of compute resources in such systems are effectively wasted on idle workloads.

Vector Embedding in Java Applications: Gen-AI Functions Directly from the Database

Tue, 06 May 2025 11:30:01 +0000

Generative AI (GenAI) has rapidly advanced in recent years and is increasingly finding its way into practical software development. What was once considered a research topic is now becoming a tool for everyday developers — including in the Java space. Vector embeddings play a key role here: they enable functions like semantic search, text classification, or recommendation systems, opening up entirely new application possibilities.

But how can these technologies be integrated into existing Java applications without the need for complex frameworks or external cloud services?

VectorEmbedding on JAVAPRO International

Petabyte-Scale AI Memory with Serverless Java

Vector Embedding in Java Applications: Gen-AI Functions Directly from the Database