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.
Meko is the agent-native data infrastructure that enables multi-agent systems to learn together, building collective memory and shared knowledge that compounds across the entire system. Meko solves the data infrastructure headaches developers and engineering teams face when building and shipping production multi-agent AI applications. Find out more.
Discover a use case for PG advisory lock that protects against write skew and provides a solution for efficient deletes that meet retention requirements. This data modeling keeps the write path predictable and gives practical control of retention cleanup without partition-management overhead.
Cloud migration failures stem largely from misaligned strategies and outdated database architectures. Distributed SQL databases offer a path forward, combining the familiarity of SQL with horizontal scalability, strong consistency, and multi-region resilience. Discover how engineering leaders can turn cloud migrations into a strategic success by modernizing database architecture, planning comprehensively, and investing in skills and automation.
Retail and e-commerce platforms are among the most demanding distributed systems in production. Learn why traditional retail and e-commerce database architecture fails at scale and how a distributed database for retail supports real-time inventory systems, AI-powered product search, and scalable infrastructure across a multi-region database.
A common temporal join pattern in analytics used to require extra tweaks in distributed Postgres-compatible systems, but is no longer required due to full support for native PostgreSQL Parallel Query (PQ). Achieve latency gains that scale better by keeping your original schema and query and allowing YSQL to execute a Parallel Index Scan directly.
Your AI isn’t broken, it’s just trained on a database that isn’t distributed. This blog covers the design of our new distributed SQL benchmark, examines what breaks and why, and offers a handy open-source fix you can drop in today.
YugabyteDB Anywhere’s embedded Kubernetes operator allows declarative, Kubernetes-native infrastructure-as-code management of multi-cloud topologies leveraging popular service meshes. Discover how the latest YugabyteDB Anywhere features simplify the deployment of YugabyteDB universes across multiple clouds with Kubernetes.
