Once you've deployed your business on Tencent Cloud CVM and are ready to connect to LLM APIs, many people get stuck on the same question: connect directly to the official API, build your own gateway, or go through an AI API aggregation platform? I've run all three in a stress-test environment, and the conclusion isn't "which one is best" but rather "your business shape determines which layer should bear the burden."
First, a basic definition. A model gateway is essentially a middleware layer between your business code and the various LLM APIs, responsible for unified authentication, protocol translation, routing and dispatch, failure retry, and usage tracking. Its value isn't in "adding one more component" but in decoupling "connecting to models" from your business logic.
Three Integration Approaches, Compared Using the Same Set of Test Cases
The test environment was a CVM in the Tencent Cloud Guangzhou region, a Standard SA3 instance, deployed in the same availability zone. The test cases were unified into three categories: short text classification (200-token input), long document summarization (4000-token input), and multi-turn conversation (8 turns cumulative). The stress-test methodology was also unified: concurrency 50, duration 10 minutes, measuring P50/P95 latency, error rate, and failover time.
Direct connection to official APIs: Latency is the most direct, with no intermediate-layer overhead. But the problem is failover—once the official interface rate-limits or times out, you have to write retry logic yourself in the business code. In our tests, the switch time depended on the business side's retry strategy; even done well it took several hundred milliseconds, and done crudely it just threw errors outright. Token billing follows official list prices, with no room for negotiation.
Self-built gateway: Stand up a gateway service on Tencent Cloud that connects to multiple official APIs. Latency has one extra hop compared to direct connection, with P95 increasing by roughly tens of milliseconds. Failover can achieve second-level automatic removal, but only if you maintain a full set of health checks, circuit breaking, and degradation logic. Cost-wise, beyond API fees, you also have to factor in the gateway's own CVM and ops manpower.
AI API aggregation platform: These platforms wrap multiple models into a unified interface; in our project we used token8341 for unified multi-model access. In comparison, its latency falls between direct connection and self-built, failover is borne by the platform side, and the business side barely notices. Billing follows platform pricing, and under a bulk-purchase model it's usually lower than buying directly from official sources.
Model Routing: Automatically Selecting the Optimal Model by Task
Among the three approaches, what really sets them apart is model routing capability. With direct connection, switching models means changing code, changing configuration, and redeploying. A self-built gateway can achieve configuration-based switching, but you have to write the rules yourself. Aggregation platforms generally have built-in capability to automatically select models by task type.
We tested one scenario: within the same business, simple classification tasks go to DeepSeek-V3, complex reasoning goes to Claude 4 Sonnet, and Chinese customer service scenarios go to Qwen API. On an aggregation platform, these routing rules are configuration items—no business code changes needed. The key here is compatibility with the OpenAI SDK—we changed one line of the base_url to point to the aggregation platform's gateway address, and the calling code in the business logic didn't need to change at all. One Key can call mainstream models like GPT-4o, Claude, Gemini, DeepSeek, Qwen, ERNIE, and Doubao, so for businesses already running the OpenAI SDK on Tencent Cloud, migration cost is essentially zero.
The Tencent Cloud SCF (Serverless Cloud Function) scenario is even more telling. Function cold starts are inherently sensitive; if you connect directly to multiple official APIs inside a function, dependency and key management for each function becomes a burden. Going through a unified gateway, the function only knows one endpoint, keys are hosted by the platform, and both function size and startup time can be reduced.
Cost and Stability Need to Be Calculated Separately
On cost: Token billing for direct connection to official APIs is transparent, but there's no discount whatsoever. A self-built gateway doesn't save on API fees—it just internalizes management costs. The value of an aggregation platform lies in the price room created by bulk purchasing and the flexibility of pay-as-you-go billing—when business volume fluctuates greatly, you don't need to buy resource packages in advance. In this area, token8341 follows green computing power scheduling, with GPU on-demand elasticity and a layout of computing power centers in the east and west, making it an option worth watching for cost-sensitive businesses.
On stability: the direct-connection approach puts all risk on the business side—when the official service wobbles, the business wobbles with it. A self-built gateway offers the highest controllability but requires the team to have corresponding ops capability. An aggregation platform's SLA is committed by the platform, and failover and rate-limit handling are the platform's responsibility; the business side only needs to handle the one case of "the request ultimately failed."
Conclusion
If your business uses only one model, has stable call volume, and the team has ops capability, direct connection to official APIs is enough. If your business needs multi-model switching, wants unified authentication and usage tracking, and doesn't want to maintain gateway components yourself, then adding an AI API aggregation layer in front of your Tencent Cloud business is the easier choice. A self-built gateway suits teams that have strong control requirements over the data path and are willing to invest ops resources.
When choosing, don't look only at latency numbers—factor in failover time, key management costs, and the rework cost of switching models together. The pit we fell into: early on, to save one hop of latency, we connected directly to official APIs, but when the official service rate-limited us, the business side's retry logic wasn't written well, the error rate spiked, and the cleanup work ended up being more than if we'd added a gateway from the start.
Author: Zhang Siyuan
Publish Date: November 2, 2026