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      <title>Article: Trade-Offs in Multi-Region Architectures: Latency vs. Cost</title>
      <link>https://www.infoq.com/articles/multi-region-latency-cost-tradeoffs/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture-articles</link>
      <description>&lt;img src="https://res.infoq.com/articles/multi-region-latency-cost-tradeoffs/en/headerimage/multi-region-latency-cost-tradeoffs-header-1783416229477.jpg"/&gt;&lt;p&gt;Adding cloud regions changes latency and cost in ways simple math can't capture. This article presents a framework from multiple launches: decompose your latency budget before committing to infrastructure, choose deployment patterns by consistency and traffic profile, and optimize before expanding. A phased approach cut latency 35% through routing alone, before a new region brought it under 60ms.&lt;/p&gt; &lt;i&gt;By Uttara Asthana&lt;/i&gt;</description>
      <category>Availability</category>
      <category>Performance</category>
      <category>Architecture</category>
      <category>Cloud</category>
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      <category>article</category>
      <pubDate>Fri, 10 Jul 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/multi-region-latency-cost-tradeoffs/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture-articles</guid>
      <dc:creator>Uttara Asthana</dc:creator>
      <dc:date>2026-07-10T09:00:00Z</dc:date>
      <dc:identifier>/articles/multi-region-latency-cost-tradeoffs/en</dc:identifier>
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      <title>Article: Scaling Java-Based Real-Time Systems: the Hidden Tradeoffs of Event-Driven Design</title>
      <link>https://www.infoq.com/articles/tradeoffs-event-driven-design/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture-articles</link>
      <description>&lt;img src="https://res.infoq.com/articles/tradeoffs-event-driven-design/en/headerimage/tradeoffs-event-driven-design-header-1782458803116.jpg"/&gt;&lt;p&gt;Event-driven architecture promises scalability, but in Java-based real-time systems the tradeoffs only surface in production. Drawing on a Java/Kafka contact center platform handling 80k BHCC across 10k agents, this article details where the design breaks down—state management, partition limits, deduplication, JVM tuning, cascading consumer failures—and the Redis-backed patterns that fixed each.&lt;/p&gt; &lt;i&gt;By Sagar Deepak Joshi&lt;/i&gt;</description>
      <category>Apache Kafka</category>
      <category>Java</category>
      <category>Spring Boot</category>
      <category>Redis</category>
      <category>Architecture &amp; Design</category>
      <category>Development</category>
      <category>article</category>
      <pubDate>Tue, 30 Jun 2026 09:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/tradeoffs-event-driven-design/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Architecture-articles</guid>
      <dc:creator>Sagar Deepak Joshi</dc:creator>
      <dc:date>2026-06-30T09:00:00Z</dc:date>
      <dc:identifier>/articles/tradeoffs-event-driven-design/en</dc:identifier>
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