Build resilient AI systems that never go down — production-proven patterns for provider routing, fallback, and capacity planning.
Provider names in examples are anonymized as A/B/C — see Chapter 2.
Every team that ships an AI feature into production eventually meets the same wall: a single provider is not enough. A free-tier rate limit hits at the worst possible time. A region goes dark for forty minutes. A model deprecation lands without a usable replacement. The alert pages someone, and that someone starts typing the same patterns this book describes — only later, in a hurry, and without the safety net of a tested design.
This kit is the architecture you wish you had built first. 10 chapters on multi-provider routing, fallback chains, capacity planning, vector retrieval, latency optimization, and the monitoring you need to know when any of it is quietly failing. Every pattern was designed and run against real content-heavy production traffic — generalized, anonymized, and stripped of anything that ties to a specific stack.
This is not a comparison of commercial AI vendors. There are no provider recommendations, no pricing matrices that will be wrong by the time you read them. The kit treats providers as interchangeable units in a system. The system is what you own, and the system is what keeps your product up when any single provider does not.
One table per task type, one adapter per provider, one fallback chain. Change routing at runtime without a deploy. The schema, the walkthrough, the build order.
Rate limits at three levels (tokens/minute, requests/minute, tokens/day), round-robin across key pools, reactive vs proactive rate limiting, the tracker-estimate drift problem and how to fix it.
The mechanical path from "every call site has its own provider logic" to "every call site uses one router." Five steps, each shippable, none requiring a big-bang refactor.
Postgres vector extension setup, dimension choice and migration paths, realistic cache hit rates by task shape, and the specific failure mode that makes semantic caching backfire on conversational chat.
Pre-LLM parallelization, embedding reuse across operations, streaming protocol tradeoffs, fire-and-forget post-LLM work, latency budgets per phase.
One adapter file, the test pass before traffic hits it, what not to put in the adapter, and the removal path when a provider outlives its usefulness.
Hybrid vector + keyword search, when rerankers earn their cost, chunking strategy, schema for a production index, the relevance floor pattern that eliminates "the AI made stuff up" reports.
Seven tables of alternatives considered and why rejected — across routing config, provider selection, vector store, streaming, cache hit strategy, pre-LLM parallelization, and embedding model selection. The differentiator: you can read the rationale and decide whether your context warrants a different choice.
The five metrics per provider, the dashboard layout, the alert rules that page vs wait, structured logging, a quality-drift monitor that catches silent model swaps, capacity forecasting.
Chapter 2 (Architecture Overview) with the full diagram and schema is available as a free sample. Start here to gauge the density and level of the entire kit.
No. This is an architecture kit, not a starter template. You will find schemas, pseudocode, diagrams, and decision tables. Where something is pseudocode, the text says so. The patterns are language-agnostic — teams have implemented them in TypeScript, Python, and Go from the same material.
None specifically, and that is the point. The architecture is provider-agnostic: it treats providers as interchangeable units with common input/output shapes. Anywhere the book says "Provider A" or "Provider B", you substitute whichever vendor your team uses.
Yes. The architecture is OS-agnostic. Schema examples use PostgreSQL syntax because it is the most legible; teams have implemented the same patterns on MySQL, SQLite, and managed cloud databases. Pseudocode is plain enough to translate.
Libraries solve the function-call layer: one SDK, multiple providers behind it. This book solves the architecture above and below: when to route where, how to plan capacity, how to cache responses, how to observe quality drift. A library is a tool; this is how to build the system that uses the tool effectively.
No. Prompting is a different skill. This book is about the infrastructure that carries prompts to providers and responses back. A good prompt on a broken provider chain fails; the book makes the chain robust enough that your prompts get to do their job.
License is single-seat. Team license available on request — contact support.
Every pattern in this book shipped in real content-heavy AI production systems over multiple months. Where a pattern failed or got replaced, the book says so. The capacity math, the decision tables, and the failure modes come from operating the system in production, not from a whiteboard.
AI Multi-Provider Architecture Kit — €59 one-time, lifetime v1.x updates, 30-day refund.