ACP offers a streamlined DevOps approach by seamlessly integration with popular tools, creating a “golden” path to production. It automates self-service management of Kubernetes clusters, ensuring consistent configuration and security policies in any environment. With comprehensive end-to-end observability , including real-time metrics and monitoring capabilities, ACP empowers enterprises to effortlessly build, deploy, and manage applications with automation and consistency across all environments.
Alauda Container Platform provides a unified distributed cloud stack to fully automate the management of diverse and dynamic infrastructures across public cloud providers, on-prem environments and edge locations. It extends Kubernetes and goes beyond container management to offer container native virtualization, container host OS, comprehensive networking and distributed storage capabilities.
Alauda Service Mesh is an enterprise ready microservices governance platform based on Istio. It helps developers manage the operational complexity introduced by modern application architectures. ASM provides end-to-end observability, mediation of communication across service components, advanced service deployment strategies, improved reliability and security, and ease of management for the service mesh infrastructure itself.
Alauda DevOps extends Kubernetes to offer an open DevOps toolchain integration and orchestration platform. It enables integrations of the tools developers love to use. It uses Kubernetes to orchestrate the tools running on top of the platform. Ultimately, it ties everything into an end-to-end DevOps process. The goal is to fully automate the DevOps best practices and deliver that as a service.
ACP aims to provide full-stack support for cloud native applications and offers most popular data services out of the box. It makes extensive use of the operators pattern to fully manage these components and deliver them as managed data services. In addition, ACP provides a cloud native machine learning platform which brings out the engineering best practices for doing MLOps and running machine learning workloads on top of Kubernetes.
