Systems | Development | Analytics | API | Testing

At Koyeb, we run a serverless platform for deploying production-grade applications on high-performance infrastructure—GPUs, CPUs, and accelerators. You push code or containers; we handle everything from build to global deployment, running workloads in secure, lightweight virtual machines on bare-metal servers around the world. One of the core metrics we track is time to HTTP 200: the time between a successful deployment (excluding build) and the moment the app is ready to accept traffic.

Today, we're announcing the Koyeb MCP Server in public beta to let you interact with your Koyeb resources in natural language. Using the Koyeb MCP Server, LLMs and agents can easily discover and leverage Koyeb primitives to: All of this using your favorite AI assistants like Claude, Cursor, Windsurf or any other applications that support the Model Context Protocol.

Amazon API Gateway and AWS Lambda are widely used for deploying and running scalable APIs or applications in the cloud. While they offer powerful capabilities for deploying and scaling APIs, designing the API or maintaining visibility into performance and reliability can be challenging without the right tools in place.

Amazon Web Services (AWS) had a well-documented failure of the S3 service in its US East region this week. The knock-on effects of the S3 outage took out many of Amazon’s other services and caused serious and long-lasting outages for many high-profile services including Quora, Trello, GitLab, and many others. Overall Amazon provides an outstanding set of services, and as anyone knows these things happen, the key is to architect your solution so that it can tolerate these kinds of failures.

In today's competitive landscape, data warehouses and data lakes are the essential platforms for business intelligence, analytics, and AI. While immensely powerful, these systems were traditionally designed for batch data processing, often leading to insights based on data that is hours or even days old. The primary challenge has always been the complexity of bridging the gap between real-time data streams, typically flowing through Kafka, and these analytical systems.

Confluent has established itself as a leader in event streaming, providing not only a robust platform but also a rich portfolio of pre-built connectors. These connectors act as bridges, effortlessly channeling data between a multitude of systems, from databases and applications to cloud services. This extensive portfolio empowers users to weave together their data landscapes with remarkable ease and flexibility.

In the last decade, AWS has redefined how businesses build data pipelines. Its ETL toolset isn’t just about moving datasets, it’s about orchestrating security, compliance, scale, and efficiency. Whether you're migrating legacy data systems or building modern ELT workflows, AWS offers a robust, versatile stack of services to meet virtually any requirement.

In case you missed it, we recently made a big announcement around beta GCP support for Kong’s Dedicated Cloud Gateways (DCGWs). There’s a lot of good stuff in there, but TL;DR DCGWs now support all three of the major cloud service providers (CSPs): AWS, Azure, and GCP at a 99.95% SLA with support for over 25 regions around the globe. Being the first API management vendor to support managed gateway deployments with all three CSPs has a lot of folks excited, for obvious reasons.