2 posts tagged with "Platform Engineering"

Introduction​

Enterprises are discovering they can run powerful AI models on their own infrastructure—but building production AI infrastructure is significantly harder than application deployment.

This post breaks down the six interconnected systems required, why specialized small models outperform foundation models for enterprise use cases, how emerging AI agents are changing the economics, and the engineering trade-offs at every layer.

Introduction​

You want the simplicity of "push code, get a live URL"—the developer experience Vercel pioneered—but with full control over your deployment, infrastructure, and compliance. This guide shows you how to build that experience on your own AWS infrastructure using AstroPulse and open-source tools: kpack, cert-manager, external-dns, and nginx-ingress.

You'll build a production-grade platform that delivers Git-push deployments with automatic TLS certificates, preview URLs, and complete observability—all running on infrastructure you own and control. Unlike hosted PaaS platforms, you'll be building on Kubernetes with full deployment control. That means you can run any workload: microservices (with or without public endpoints), stateful databases, WebSockets, long-running background jobs, AI/ML model training and serving, or traditional web applications in any language. You get the simple developer experience with complete architectural control.

How operations work: The infrastructure industry is moving toward an agentic era—AI agents autonomously handling complex workflows (MCP, A2A, multi-agent orchestration). We're heading toward infrastructure that self-configures, self-heals, and self-optimizes. We're not there yet, but Nova AI brings you AI-assisted operations today with human-in-the-loop. Day 1 (this guide): You build the platform. Day 2 (ongoing): Nova analyzes issues, diagnoses problems, recommends fixes—you approve. As AI matures, more becomes autonomous.