Production AI System Architecture

Most AI projects start with a model.

Production AI systems don’t.

They start with 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲.

A production-grade AI system needs much more than an LLM:

→ 𝗨𝗜 / 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 to interact with users
→ 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 to control access
→ 𝗟𝗼𝗮𝗱 𝗯𝗮𝗹𝗮𝗻𝗰𝗶𝗻𝗴 + 𝗔𝗣𝗜 𝗴𝗮𝘁𝗲𝘄𝗮𝘆 to handle traffic reliably
→ 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 + 𝗮𝗴𝗲𝗻𝘁𝘀 to manage workflows
→ 𝗧𝗼𝗼𝗹𝘀 + 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 to connect AI to the real world
→ 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 for conversations, knowledge, logs and state
→ 𝗖𝗹𝗼𝘂𝗱 𝗼𝗿 𝗼𝗻-𝗽𝗿𝗲𝗺 𝗺𝗼𝗱𝗲𝗹𝘀 depending on the requirements
→ 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 + 𝗾𝘂𝗲𝘂𝗲𝘀 to improve performance and resilience
→ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 to continuously measure quality
→ 𝗣𝗿𝗼𝗺𝗽𝘁 / 𝗺𝗼𝗱𝗲𝗹 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 to control changes and versions
→ 𝗟𝗟𝗠 + 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 to understand what happens in production
→ 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 + 𝗳𝗮𝗹𝗹𝗯𝗮𝗰𝗸𝘀 when things go wrong

The interesting part is that 𝗻𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲𝘀𝗲 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗶𝘀 𝘁𝗵𝗲 “𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺” 𝗯𝘆 𝗶𝘁𝘀𝗲𝗹𝗳.

The AI system is the set of relationships between them.

And there is no single correct stack.

You might use:

• APISIX or another API gateway
• PostgreSQL or another application database
• Elasticsearch or another log platform
• Redis or another cache / queue
• Keycloak or another identity provider
• Prometheus + Grafana or another observability stack
• cloud models or self-hosted/on-prem models

The tools change.

The architectural principles don't.

𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗮𝗯𝗼𝘂𝘁 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗮𝗻 𝗟𝗟𝗠 𝘁𝗼 𝗮𝗻𝘀𝘄𝗲𝗿 𝗮 𝗽𝗿𝗼𝗺𝗽𝘁.

It's about building a system that remains reliable, observable, secure and cost-effective 𝗮𝗳𝘁𝗲𝗿 𝘁𝗵𝗲 𝗱𝗲𝗺𝗼 𝗲𝗻𝗱𝘀.

I put together this architecture diagram to make those relationships easier to reason about.

What would you add or change in this architecture?

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