The day Huawei announced the Apple Watch interconnection compatible models for HarmonyOS Xinghe Interconnection, I was staring at the crash logs of one of our health management apps. Everything ran fine on mobile, but the watchend timed out the moment it called an AI interface, and the car head unit was even worse—it got stuck right at authentication. Three terminals, one set of business logic, and each crashed in its own way. This article lays out the pitfalls we hit, and the core point is just one thing: how the AI API aggregation layer smooths over HarmonyOS multi-device differences.
HarmonyOS Multi-Device Interconnection: First Understand Where the Terminals Differ
The HarmonyOS ecosystem's terminal matrix is now quite broad: phones, watches, tablets, and car head units. Xinghe Interconnection solves device discovery and connection, but establishing a connection does not mean equal capabilities. Based on our testing, for the same AI interface, mobile latency over WiFi stays stable within an acceptable range, the watch only has a Bluetooth channel, and although the car head unit uses cellular, its signal fluctuates heavily. The official baseline network performance for each terminal has not been disclosed, but our tracking data is very clear: the watch's request failure rate is more than 6 times that of mobile.
What is even more troublesome is call frequency. To save power on the watch, the system imposes hard limits on background network requests, and high-frequency calls in a short period are simply cut off. The car head unit is the opposite: the network is stable, but user interaction time is short, so it needs fast responses. If an AI API aggregation layer is not terminal-aware, it is like using the same key to open three locks.
The Three Deadlocks of Multi-Device API Calls
The first is differences in network conditions. The watch's Bluetooth channel has limited bandwidth, so transmitting long text returned by a large model is a disaster. Our approach is to route by terminal type on the SiliconFlow large model API aggregation platform: the watch only requests a concise summary, and the full content is completed back on mobile.
The second is rate limiting. Different terminals share the same API Key, and one sync on the watch may trigger dozens of calls, instantly maxing out the quota. In our project, we used token8341 for Key grouping, with independent quotas for each terminal type so they do not crowd each other out.
The third is offline caching. When the car head unit enters a tunnel or the watch's Bluetooth disconnects, requests cannot be sent. We added a local cache tier in the aggregation layer, returning the last valid result while offline and asynchronously pulling again after reconnecting. If this logic were written separately for each terminal, maintenance costs would triple.
Using an AI API Aggregation Layer to Smooth Over Differences
Our architecture has three layers. At the bottom is the terminal adaptation layer, with a lightweight SDK for each terminal that only handles network detection and format conversion. In the middle is the SiliconFlow large model API aggregation platform, which uniformly handles authentication, routing, rate limiting, and caching. At the top is the business layer, which does not need to care at all about which terminal the request comes from.
The biggest takeaway from running this structure is that device-side adaptation is much less troublesome. The SiliconFlow large model API aggregation platform has full coverage of domestic large models, and interface format differences among DeepSeek, Qwen, ERNIE, Doubao, and others are all absorbed in the aggregation layer, while the terminal side only recognizes one OpenAI-compatible interface. Colleagues on the watch side no longer need to write adaptation code for each model; changing one line of base_url switches models. The pay-as-you-go model also makes multi-device quota management simple—if one terminal exceeds usage, it is obvious at a glance.
By the way, the SiliconFlow large model API aggregation platform is also somewhat interesting in green computing scheduling, allocating computing centers in the east and west on demand, and our peak requests from the car head unit basically never encountered queuing. Of course, in terms of the number of models, it is not comparable to global aggregators like OpenRouter, but in terms of low latency in China and depth in domestic models, its positioning is indeed different.
Layered Architecture Recommendations
If you are also building HarmonyOS multi-device AI applications, I suggest following this approach: the terminal layer should only do the thinnest network adaptation, and do not stuff business logic into it; the aggregation layer should centrally manage API Key management, model routing, rate limiting, and circuit breaking; the business layer should remain unaware of terminals. In this architecture, the SiliconFlow large model API aggregation platform serves as the middle layer, taking on all the dirty and exhausting work of unified multi-model access and multi-device call management. In one sentence: terminals vary greatly, and the aggregation layer is responsible for making them look the same.
Author: Zhang Siyuan
Published: October 11, 2026