Multi-Provider AI in Production: Lessons from Gateway Architecture

Multi-provider AI is attractive because it gives teams choice. One provider may lead on reasoning. Another may be faster for short responses. Another may offer a better open-source catalog. Another may be required by procurement. But multi-provider AI becomes messy if every application service integrates every provider directly.

Adapters are the boundary

Provider adapters should hide provider-specific details from the rest of the product. They map public model slugs to upstream model IDs, translate request bodies, read streaming events, parse usage fields, and normalize errors. When a provider changes an API detail, the adapter is the first place to update.

Shared schemas prevent drift

A gateway should validate incoming requests against a shared schema. That schema becomes the contract between client, dashboard, gateway, and tests. Without it, each layer starts making slightly different assumptions about fields, streaming options, tools, and response shape.

Route policy should be explicit

Production teams need more than a list of models. They need policy: allowed providers, blocked providers, preferred providers, fallback behavior, BYOK preference, and cost caps. These controls make the system usable in different environments without changing product code.

Operational boundaries protect other systems

A gateway should be deployed so it can be updated without disturbing unrelated projects. The compose project name, ports, env files, and restart scope should be explicit. When Sylica deploys, it should rebuild Sylica gateway and dashboard containers, not touch other containers on the instance.