Systems | Development | Analytics | API | Testing

The most effective way to adopt streaming machine learning (ML) is not by rebuilding your entire platform but by adding a single, high-value inference step to your existing data flow. This incremental approach allows you to transition from batch-based processing to real-time decision-making without the risk of a "big bang" migration, ensuring that your microservices architecture remains agile and responsive. What Is Streaming ML? ML in streaming is the practice of.

Most AI pilots fail to reach production not because the models don’t work, but because enterprises struggle with data governance. While pilot-phase AI projects demonstrate impressive results in controlled environments, they hit governance walls when moving to enterprise-scale deployments. This post examines why AI initiatives stall before production and provides a governance-focused approach for breaking the cycle.

When it comes to QA, AI-powered automated testing tools promise more speed, better coverage, and lower maintenance. But they don’t all solve the same problems, and their approach to solving problems can be fundamentally different. Some platforms lean heavily into autonomy. Others focus primarily on speed or aggressive self-healing. A smaller group applies AI in specific parts of the workflow while preserving test execution reliability and human control.

In the burgeoning intelligence economy, AI tokens are a metered utility, but enterprise profitability now hinges on a critical distinction: output tokens can cost up to 10x more than inputs, creating a new, invisible risk for cost overruns, particularly with Agentic AI. Learn how Kong AI Gateway and Konnect Metering & Billing provide the essential financial control plane to enforce directional guardrails, protect margins, and turn token consumption into realized revenue.

Federal agencies must perform a high-stakes balancing act: Modernize legacy systems, break down data silos, and deliver real-time citizen services—all while operating under strict security and compliance requirements with constrained budgets and staff. Today, we're announcing that Confluent Cloud for Government (CCG) is now available on the FedRAMP Marketplace, with FedRAMP Moderate authorization achieved through the competitive FedRAMP 20x Pilot program.

Apache Kafka powers massive, mission-critical data streams at enterprises worldwide. But in many organizations, those streams still behave like dumb pipes: raw JSON or bytes flowing between services, limited governance, weak contracts between teams, and data that’s hard to reuse for analytics or artificial intelligence (AI).