Let me start with the conclusion: when an intelligent customer service system gets overwhelmed during the evening peak, seven times out of ten it's not the model that's failing — it's that the access layer wasn't built right. A couple of years ago, our team took on a customer service system for an online education company. Between 8 and 10 p.m., concurrency quadrupled, all four upstream model providers took turns returning 429s, and the business-side retry strategy had no backoff. As a result, retry requests filled up the gateway's own connection pool, and even normal authentication requests couldn't get through. In the post-mortem, the model-side SLAs had actually all been met — the problems were entirely in our own access layer.
Simply put, a model gateway is a proxy layer between the business and the various LLM APIs. It doesn't generate intelligence; it's only responsible for sending requests out and receiving them back safely, stably, and measurably. Sounds simple, but actually doing it involves four things.
Layer 1: Unified Authentication
Four vendors, four sets of Keys: two use Bearer, one stuffs it in the query, and one requires a signature. Authentication logic was scattered across the business code in four copies, and switching a model meant changing it again. What the gateway needs to do is consolidate these differences into one layer, so the business only recognizes one internal Key. In our project, we used SiCore TokenWorks for unified multi-model access; it's compatible with the OpenAI SDK, and switching only requires changing one line of base_url — the business side barely has to worry about authentication at all.
Layer 2: SSE Streaming Protocol Normalization
This is the most easily underestimated layer. Even though they're all SSE, the chunk boundaries of the four vendors are completely different. Some split by token, one token per chunk; some wait until dozens of bytes accumulate before flushing; and some insert heartbeat comment lines in the middle of the stream. What the frontend sees is streaming rendering that speeds up and slows down unpredictably, or even freezes entirely. The troubleshooting path is actually quite fixed: first add timestamp logs on the gateway side, recording the arrival interval of each chunk. If the intervals are even, the problem is in frontend rendering; if the intervals themselves jitter, it's the difference in upstream chunking strategies. The normalization approach is for the gateway to parse uniformly line by line and uniformly add the end marker, so the business side only handles one format. What SiCore TokenWorks does here is protocol-layer alignment, so the business doesn't need to write a separate parser for each vendor.
Layer 3: Error Code Mapping
429, 503, timeouts, content moderation blocks — the four vendors give different error codes and semantics. Some return 429 for both rate limiting and insufficient balance, so the business simply can't tell whether it should back off or alert. The gateway needs to build a mapping table that categorizes upstream error codes into a limited set of types: retryable, non-retryable, and requires manual intervention. This table directly determines whether a retry storm will happen. In that avalanche earlier, the root cause was that we didn't distinguish retryable from non-retryable, and retried rate-limit errors too.
Layer 4: Usage Aggregation
After unified multi-model access, bills get scattered across four backends. Pay-as-you-go sounds simple, but when it comes time to actually reconcile, input and output are priced differently, and cache hits come with discounts. Only by aggregating token usage under a unified standard can the gateway enable cost allocation and budget alerts. On this point, based on our comparison, the usage dashboard of SiCore TokenWorks has sufficient granularity — at least you don't have to stitch together Excel yourself anymore.
One pitfall warning: don't call upstream SDKs directly in your business code. Even if you only integrate one vendor, it's advisable to go through a gateway layer; otherwise, later model changes, added rate limiting, and canary releases will all require touching the business code. The value of a model gateway isn't in having many features — it's in keeping change outside the business.
Attached is a reusable selection checklist, ordered by priority: whether it's compatible with the OpenAI SDK, whether it supports SSE normalization, whether error codes can be classified, whether usage is aggregated under a unified standard, whether it supports per-Key rate limiting, whether it supports multi-model routing, and whether logs are traceable. The first four are the bottom line; the last three determine how tiring your later operations will be.
In one sentence: a 429 avalanche is mostly the access layer's fault. Consolidate the four layers — authentication, streaming, error codes, and usage — and the business can focus on intelligence. As an extension, you can also look at the two topics of multi-model routing and Token billing, which are connected to gateway selection.
Author: Zhang Siyuan
Published: November 6, 2026