Table of Contents

The New Age of Solution Architecture

The landscape is shifting. Here's what's changing and why it matters.

The Ground Is Moving

The past decade had a stable playbook: pick a cloud, containerize, use managed databases, ship. That playbook isn't wrong, but it's no longer enough. Several forces are reshaping how systems are designed.

AI & LLM Integration

It's no longer a feature, it's an architectural concern.

• New building blocks in the stack — vector databases, RAG pipelines, embedding models, prompt management layers, and LLM gateways are now first-class components alongside your database and message queue
• Unpredictable cost models — LLM costs scale with tokens, not compute time. A chatbot that costs $50/month in testing can cost $5,000/month in production. Architects need to model AI costs as carefully as database scaling.
• Latency decisions — an LLM call takes 2-10 seconds. Fine for a chatbot, unacceptable for real-time processing. Where does inference happen? Synchronously? Async via queue? Pre-computed and cached? On-device?
• Failure and fallback — LLM APIs go down, hit rate limits, and produce nonsense. What happens when the AI is unavailable — degrade, cache, or fall back to rules?
• Data privacy — every prompt sent to an external LLM potentially contains user data. Some organizations self-host open-source models for compliance. That's a major architectural decision.
• Expanded build surface — real AI integration involves prompt pipelines, evaluation frameworks, guardrails, token budgets, model versioning, A/B testing, and AI-specific observability. It's a sub-architecture within your system.

Buy vs. Build — The Pendulum Is Swinging