Infra in Practice: Stack, Prompts and Use Cases
A layer that picks the cheapest capable model for each request, saving cost and latency. This guide pulls together everything on Onexial tagged infra — 8 connected nodes across definitions, workflows, tool stacks, comparisons, prompts and applied use cases — and orders it the way you would actually learn it: vocabulary first, then process, then tooling, then execution. Every item below links to a full node with its own examples and connections, so you can go as deep as you need without losing the map.
Core concepts behind Infra
Before wiring anything together, the vocabulary has to be precise. These 4 definitions cover the terms that show up in almost every Infra discussion — each one links to a full entry with an example and its own connections inside the graph.
AI Router
A layer that picks the cheapest capable model for each request, saving cost and latency.
Quantization
Shrinking a model by lowering weight precision.
Inference
Running a trained model to produce outputs.
Model Routing
Sending each request to the cheapest model that can handle it.
Trade-offs and comparisons
Most Infra decisions are trade-offs rather than right answers. These 4 comparisons break down the real differences, when each option wins, and the recommendation for the common case.
Vercel vs Cloudflare Workers for AI Apps
Two edge platforms, two philosophies: DX-first vs infra-first. Both ship AI apps well.
Groq vs Together AI
Fast open-model inference: throughput vs breadth.
Chroma vs Qdrant vs Pinecone
Open-source local vs managed cloud vector databases.
Cloudflare Workers vs Vercel Functions
Two edge runtimes for AI apps.