Discover how YugabyteDB Resource Governance helps organizations safely consolidate more databases on shared infrastructure without sacrificing predictable performance. Plus, learn how fair CPU sharing and workload controls prevent noisy neighbors, improve resource utilization, and provide the isolation needed to confidently run multi-tenant workloads at scale.
After a decade of building a reliable distributed data architecture, the natural progression for Yugabyte was to begin supporting AI agents. Yugabyte is now a Silver Member of the Agentic AI Foundation (AAIF), the Linux Foundation’s home for the open standards that agentic AI runs on. This blog provides background on the AAIF’s work and shares why we joined the Foundation.
With Meko, your project context lives in a datapack any MCP-connected client can read. This allows you to switch tools without losing context, share useful information with your team while keeping selected data private, and capture not just what decision was reached, but why. All this is built on a PostgreSQL-compatible distributed database that scales as you grow. This blog details what each Meko component does, why it’s important, and the steps you need to take to try it for yourself!
During major live sporting events, peak traffic reaches unprecedented levels, and customers expect a flawless in-the-moment experience. The right data infrastructure separates the platforms that win from the ones that fail. This blog uses the FIFA World Cup as an example and explores why successful platforms are building multi-agent systems on a unified data infrastructure to handle seasonal and event-driven traffic spikes.
With YugabyteDB at the core, customer records, vector embeddings, policies, and audit logs live together in a single distributed data layer. There is one source of truth and one consistent answer. This blog shares how to build a fast, explainable copilot on YugabyteDB, combining RAG, CAG, and vLLM in a single distributed Postgres-compatible foundation.
AI models write vanilla PostgreSQL. If your database is distributed, providing the AI model with a YugabyteDB skill file closes the gap and ensures it writes code that works for your application. In this blog, we break down the benchmarking results across 17 model configurations. We conducted 350+ evaluations across Claude 4.5, 4.6, and 4.7, Gemini 3.1 Pro, GPT-5.x, Composer 2, Codex CLI – through Claude Code CLI, Cursor, and Codex. Discover what moved the score, what regressed, and the one structural finding that changes how every team should think about delivering context to an AI coding tool.
