Inference Infrastructure

Once a system has users, inference becomes an infrastructure problem: latency budgets, throughput, routing between providers, caching, fallbacks, and cost attribution. This guide explains each of those concerns, the trade-offs between hosted and self-hosted serving, and the operational metrics that tell you the system is healthy.

What you will be able to do

• Set and defend a latency and cost budget per request type.
• Route between models and providers with safe fallbacks.
• Cache at the right layer without serving stale answers.
• Instrument inference so failures are diagnosable.

Inside the guide

1. 01

   Anatomy of a request

   Where time and money go between the user and the model.

2. 02

   Serving options

   Hosted APIs, dedicated capacity, and self-hosting compared.

3. 03

   Routing and fallbacks

   Model selection, degradation paths, and provider outages.

4. 04

   Caching

   Prompt caches, semantic caches, and result caches.

5. 05

   Observability and cost

   Traces, metrics, and attribution per feature and customer.

From the guide

A latency budget is a design input, not a report. Decide what the user can wait for, subtract the network, and what remains is the model and retrieval budget you are designing against.

Who it is for

• Platform and infrastructure engineers supporting AI products.
• Teams moving from a single provider to a routed, multi-model setup.