The Distributed SQL Blog

A PostgreSQL Database for Every Agent

A PostgreSQL Database for Every Agent

Newly released YugabyteDB 2026.1 and YugabyteDB AMP (Agentic Multitenant Postgres) provide true serverless, scale-to-zero PostgreSQL, where every agent gets its own real, isolated database starting at a fraction of the cost of a core. Discover how YugabyteDB AMP packs hundreds of bursty, idle-prone agent workloads onto shared distributed infrastructure, how Resource Governance keeps costs predictable across a fleet, and how the same platform scales from prototype to global active-active deployment with no rewrite. We also share what else is new in 2026.1!

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Get Started with Meko: Agent Memory with Built-in Discernment

Get Started with Meko: Agent Memory with Built-in Discernment

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!

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The Beautiful Game: Winning at Scale with a Multi-Agent Strategy

The Beautiful Game: Winning at Scale with a Multi-Agent Strategy

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.

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Benchmarking AI Coding Agents for Distributed SQL: What We Learned

Benchmarking AI Coding Agents for Distributed SQL: What We Learned

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.

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10 Reasons Cloud Migrations Fail and Key Strategies to Increase Success

10 Reasons Cloud Migrations Fail and Key Strategies to Increase Success

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.

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