AI应用正当时_详解AI应用开发新范式_84页_16mb

Despite exponential growth in data and AI workloads, enterprises must prioritize cost control and resource optimization when selecting platforms. OSS (Open Source Software) and pay-as-you-go models, along with fine-grained cost management tools, will gain prominence to ensure sustained operations.

AI-native applications, driven by Agentic AI, use data as a focal point and integrate toolchains, evolving from monolithic to cloud-native and finally AI-native architectures with microservices and specialized components like vector databases and GPU services tailored for AI tasks. By integrating AIOps, observability standards (like Open Telemetry), and enterprise-grade security protocols such as MCP (Model Context Protocol), applications can run more stably, securely, and efficiently.

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