Are We Choosing AI Architectures—or Following Trends?

Hi Product Hunt community! 👋

I’m Malik Dixon, a U.S. Army veteran, security-focused technologist, and solo founder with more than 25 years of experience across software development, AWS, DevOps, DevSecOps, UX, and AI workflow design.

While building AI systems, I kept seeing teams choose RAG, MCP, agents, and other architectures because they were popular—not because the added complexity was supported by evidence.

That inspired me to build TraceLogicAI.

TraceLogicAI runs the same task through five pipelines—Plain, RAG, MCP, Agent, and Security-aware—and compares their retrievals, citations, tool calls, execution traces, groundedness, latency, cost, and safety signals.

The goal isn’t to declare one architecture universally superior. It’s to help builders find the simplest approach that meets their requirements without unnecessary cost, risk, or operational complexity.

TraceLogicAI is bootstrapped, live, and currently free to explore as I prepare for its Product Hunt launch.

I’d value your perspective:

• How do you decide which AI architecture to use?

• Which evaluation metrics matter most to you?

• Have you discovered that an AI system you built was more complex than necessary?

• What would make this comparison useful in your workflow?

Try it here: https://tracelogicai.com/

I look forward to learning from your experiences and using your feedback to shape the product.