๐ง๐ต๐ถ๐ป๐ด๐ ๐ ๐๐ฒ๐ฎ๐ฟ๐ป๐ฒ๐ฑ ๐๐ฎ๐ฐ๐ธ๐ฒ๐ป๐ฑ ๐๐ฒ๐ณ๐ผ๐ฟ๐ฒ ๐๐ฒ๐ฐ๐ผ๐บ๐ถ๐ป๐ด ๐๐ป ๐๐ ๐๐ฎ๐๐ฒ๐๐ฎ๐ ๐๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฟ I'm building an AI API gateway. I thought integrating providers would be the hard part. I was wrong. The hard part is absorbing inconsistency and giving developers something stable to trust.
When building a multi-model gateway, you need to think like someone responsible for a production contract. This shift is where most complexity comes from. A unified AI API sounds like an interface design problem, but it turns into a systems problem.
You need to define a canonical internal request model, maintain a capability map for each provider and model, translate requests into provider-specific formats, and normalize responses and failures. Without this separation, abstraction leaks everywhere.
Routing is not just about model selection, but about constraints. Developers ask for a model that fits a constraint, like lower latency or better reasoning. You need a model registry that knows what each model can do, how expensive it is, and how it behaves under load.
Latency is not just about speed, but about unpredictability. What people remember is variance. A model that usually responds in one second but occasionally takes twelve feels unreliable.
Streaming is where the abstraction gets stress-tested. Many providers support streaming, but they do it differently. You need to build a stream normalization layer to hide these differences.
Error handling is one of the most human parts of the system. Raw upstream errors are often inconsistent or not actionable. You need to normalize errors to give developers a unified failure experience.
Observability is key to making everything else possible. You need to see what was requested, what was routed, and how long it spent in each stage. Without this, routing and failover are guesswork.
The target never stops moving. Providers update model versions, pricing changes, and context windows change. Architecture needs to assume motion.
What people call "aggregation" is often really a search for reliability. The hard part is not exposing more models, but standing between a messy provider ecosystem and a developer who wants their production system to behave predictably.
Source: https://dev.to/mundo_ghose_bb3af8bcb2bc3/what-building-a-multi-model-ai-gateway-taught-me-about-reliability-2373 Optional learning community: https://t.me/GyaanSetuAi