Qubrid AI Infrastructure

TL;DR

This is a pivot toward the unsexy side of AI—focusing on latency, cost, and routing rather than just chasing the latest LLM benchmarks.

Who is this actually for?

Engineering leads and platform architects who need to move past the 'OpenAI wrapper' phase into production-grade systems with predictable margins.

The Good

• Prioritizes inference economics, which is the only way to build a sustainable AI business without burning through VC cash.
• Supports specialized, smaller models that are significantly faster and cheaper than over-engineered general-purpose models.

The Catch (Potential Downsides)

Managing hybrid local/cloud orchestration adds massive technical debt and another layer of potential failure to your stack. This is likely overkill for simple MVPs or solo hackers.