Systems | Development | Analytics | API | Testing

This is the second post in a series about reasons to attend API Summit 2025. Check out the previous post here. To unlock Kubernetes’ full potential, many enterprises are relying on three key building blocks available in Kong Konnect today: Together, these components extend Kubernetes from being just a container orchestration platform. They lay the foundation for Kubernetes to support the exposure, governance, and operation of APIs — and the AI workflows that increasingly rely on those APIs.

As artificial intelligence (AI) matures from experimentation into production use cases, the symbiotic relationship between data and AI becomes increasingly clear. To deliver real business impact—smarter automation, better customer experiences, and massive cost takeout—AI use cases are only as powerful as the data they’re running on.

We’re proud to announce that Confluent has been named MongoDB’s 2025 Global Tech Partner of the Year. This award highlights the strength of our partnership and joint go-to-market execution, helping enterprises build the next generation of intelligent, event-driven artificial intelligence (AI) applications.

With recent performance improvements to BigQuery, users can expect gains in scalability, reliability, and usability across BigQuery and BigQuery ML.

Modern application development demands speed, reliability, and reproducibility. But between third-party APIs, unstable backend services, and inconsistent test data, API development often struggles to achieve dependable environments for testing and iteration.

Multiplayer MCP Server streams full stack session data into your IDE. Give AI tools complete context—frontend, backend, annotations—for accurate fixes.

Ephemeral test environments have surfaced as a solution to DevOps teams’ growing challenges. Dealing with spiraling cloud costs and infrastructure maintenance is only getting more complex. Development teams find themselves competing for limited or stale environments while datasets grow larger. As a result, development velocity suffers. Application teams need realistic data for effective testing.

We put two of the most talked-about models head-to-head in a real-world RAG scenario, and the results might surprise you. Hemraj Bedassee , Delivery Excellence Practitioner,

As leaders across industries, we’ve all experienced the frustration of legacy BI tools—spending weeks building dashboards that end up unused, or struggling with rigid filters that block true exploration and insight. Calling this “data-driven” is no longer acceptable! Today, the pace of AI innovation has raised expectations. Customers and end users now demand instant, contextual, and explainable intelligence, seamlessly embedded into their daily workflows.