Open source LLMs continue to compete with proprietary models on performance benchmarks for natural language tasks like text generation, code completion, and reasoning. Despite having fewer resources than closed models, these open LLMs offer cutting-edge AI without the high costs and restrictions of proprietary models. However, running these open-source models in production and at scale remains a challenge.

About 100 years ago, Austrian economist, Joseph Schumpeter introduced the idea of “creative destruction.” It describes the process by which new innovations and technologies replace and make outdated ones obsolete. It’s an ongoing process that constantly reshapes the economy emphasizing innovation-driven economic development and growth, leading to the dismantling or transformation of old industries and economic structures, and the creation of new ones.

Many organizations struggle with managing API gateways across multiple cloud environments. In this blog post, we'll explore how Kong Mesh can solve these challenges and enable seamless cross-cloud connectivity.

The focus of this blog is to provide step-by-step instructions for deploying and configuring Kong Mesh with Kong Konnect on an AWS ECS instance so that anyone will be able to get pre-production installation of Kong Mesh standing up on their own.

Imagine you're an IT administrator managing sprawling hybrid cloud workloads and infrastructure with multiple service catalogs, each requiring meticulous attention to detail and manual analytic and administrative effort. Your team is constantly juggling between different tools, struggling with infrastructure observability and management productivity, facing challenges optimizing costs and gaining actionable insights.

AWS offers its own out-of-the-box API gateways. For many AWS teams, AWS API Gateways are a natural place to start when beginning their API management and API gateway journeys. However, as API practices and programs begin to scale, many organizations begin to look beyond AWS due to major limitations, feature gaps, performance issues, and cost-effectiveness challenges.

The need for automation testing is growing rapidly, and it’s no longer just limited to functional testing. Today, organizations also require cross-browser testing, cross-platform testing, responsive testing, visual regression testing, and accessibility testing, in addition to end-to-end and API testing. While open-source tools can perform these tests, they don’t provide the infrastructure to run the automation scripts.

The healthcare sector is undergoing a remarkable wave of innovation, especially due to the integration of cloud computing in healthcare. From solidifying security to enhancing predictability, and improving performance, this transformation is touching every facet of the world while also supporting business transformation. The implementation of cloud computing in the healthcare sector has fundamentally transformed how medical data is generated, accessed, stored, and exchanged.

We’re on a mission to simplify application deployment for developers and businesses worldwide, whether they're AI-driven models, full stack applications, APIs, or databases. Our next-generation serverless platform significantly accelerates your deployments and improves efficiency, enabling you to build more with less spend. 2024 was a major year for us, packed with crucial serverless milestones.