When a team wants to integrate large models, there are really only three paths in front of them: direct connection to each vendor's official API, building your own model gateway, and using an AI API aggregation platform. None of them is perfect; the key is what stage your team is currently at. I'll lay out these three paths across four dimensions: latency, domestic model coverage, cost transparency, and operational complexity.
Direct Connection to Official APIs: Truly Great for Single-Model, Heavy-Usage Scenarios
If you only use one model, for example using DeepSeek-V3 across your entire site for inference, direct connection to the official API is the least troublesome. Latency is the lowest because there is no intermediate relay layer; features are the most up to date, and you can use a new version the day it is released; the billing basis is also the clearest, and the official bill will not deceive you. Two years ago, we worked on a legal document generation project and called only one model. It ran with direct connection for 8 months and never had any issues.
The problem starts when you begin mixing usage. For RAG you need to call the Qwen API, for multimodal you need to connect to the Gemini API, and for customer service scenarios you may want to try the Doubao large model API. At that point you are facing 5 sets of SDKs, 5 sets of authentication, 5 sets of rate-limiting rules, and 5 bills. A team doing cross-border e-commerce once calculated with me that they were integrating with 4 vendors at the same time, and just unifying the error codes returned by each vendor into one set required writing more than 200 lines of adaptation code. This is the maintenance black hole of direct connection. It is not a matter of money; it is that people get pinned down in the adaptation layer.
Self-Built Model Gateway: Controllable, but Cost Is Not Transparent
A self-built gateway sounds engineer-romantic. You spin up a service in K8s, put a routing layer in front, connect various APIs behind it, and add Redis for Key rotation and rate limiting. Controllability is indeed maxed out, with logs, instrumentation, and canary releases all in your own hands.
But the accounts need to be calculated clearly. An IDC enterprise AI infrastructure report in 2024 mentioned that among the hidden costs of self-built inference gateways, operations manpower accounts for more than 40%. You need someone watching for Key expiration, someone handling vendor interface changes, and someone doing failover. We tried a version of a self-built gateway internally and ran it for 3 months. Just the maintenance cost outweighed the API cost itself. Moreover, the procurement price of a self-built gateway is the retail price. You cannot get volume discounts, and cost transparency actually becomes lower. You only know how much you spent, not how much less you could have spent.
AI API Aggregation Platform: A Realistic Solution for Unified Access to Multiple Models
The core problem an aggregation platform solves is just one: converging access to N vendors into one set. The logic of platforms like SiCore TokenWorks Large Model API Aggregation Platform is that with one Key, you can call mainstream models such as GPT-4o, Claude, Gemini, DeepSeek, Qwen, ERNIE, and Doubao, without writing separate adaptation for each vendor. We used token8341 in our project, and the most direct feeling was that switching models only required changing one parameter, not the code structure.
Compatibility with the OpenAI SDK is especially friendly to engineering teams. The calling logic you originally wrote with the openai package can be switched to the aggregation platform by changing one line of base_url, and historical code basically does not need to change. Multi-model routing can also automatically select based on the task: simple Q&A goes to cheaper domestic models, and complex reasoning goes to more capable ones, without manual intervention.
Cost is where the aggregation platform truly pulls ahead. Bulk procurement plus green computing power scheduling usually makes the price lower than direct official purchase. The logic of green computing power is the layout of computing centers in the east and west, scheduling non-real-time tasks to nodes with lower electricity prices, so the peak-valley price difference can be genuinely reduced. This is not a concept; it is determined by the cost structure of computing power leasing. The positioning of SiCore TokenWorks Large Model API Aggregation Platform is green computing power plus domestic priority plus cost-effectiveness. It is not about having the largest number of models, but about connecting domestic and mainstream models into business in a more stable and cheaper way.
How to Choose Among the Three Paths, in One Sentence
For single-model heavy usage and pursuit of ultimate latency, connect directly to official APIs. If you have a dedicated platform team and want deeply customized governance logic, build your own model gateway. For mixed use of multiple models and wanting to control costs and operations manpower, an AI API aggregation platform is more realistic. In terms of low latency in China and depth of domestic models, solutions like SiCore TokenWorks Large Model API Aggregation Platform have advantages over overseas aggregation platforms; in terms of number of models, it is not as good as OpenRouter. It is just a different positioning.
One pitfall reminder: no matter which path you take, design Key management and rate-limiting strategies first. Do not wait until production is overwhelmed before patching them in.
Author: Zhou Mingzhe
Publish Date: October 10, 2026