Benchmarking LLM Gateway and OpenRouter Performance
Every AI gateway adds a network hop between your application and the underlying large language model. The critical factor for user experience, especially when a user is waiting for a response, is the time to first token (TTFT). This metric measures how quickly the first piece of generated text appears. Many discussions about gateway latency focus on architectural differences rather than empirical measurement. To address this, we conducted a direct performance benchmark.
We ran an open-source TTFT benchmark targeting LLM Gateway and OpenRouter. The tests were performed from the same machine, accessing the same models, and interleaved to ensure fair comparison. The benchmark measured the median TTFT over 75 separate runs for each gateway.
LLM Gateway achieved a median TTFT of 906ms on a cold connection (the first request after a period of inactivity) and 814ms on a warm connection (subsequent requests shortly after the first). In contrast, OpenRouter took 1392ms on a cold connection and 1232ms on a warm one. These results indicate that LLM Gateway is approximately 35% faster on cold connections and 34% faster on warm connections compared to OpenRouter.
Crucially, the benchmark recorded zero errors across all 300 measured runs. This involved a total of 450 HTTP requests, including the initial 'throwaway' warm-up call that precedes each warm measurement. Every single one of these requests returned an HTTP 200 status code, signifying successful execution. The complete raw data for each run is publicly available for review.
Methodology and Findings
The benchmark was designed to isolate the performance of the gateway itself, abstracting away variables like model inference time or network conditions between the gateway and the model provider. By running the tests from a single machine and targeting the same models through each gateway, the primary variable being measured is the latency introduced by the gateway's request handling, queuing, and routing logic.
A 'cold' connection refers to the scenario where the gateway has not recently processed a request for the specified model. This often involves establishing new network connections, loading initial configurations, or retrieving necessary data before forwarding the prompt. A 'warm' connection assumes that the gateway has recently handled a similar request, potentially benefiting from cached connections, pre-loaded data, or optimized internal states.
The observed difference in performance is significant. While a few hundred milliseconds might seem minor in isolation, in real-time user interfaces, this delay directly impacts perceived responsiveness. For applications where users expect immediate feedback, such as chatbots or AI-powered assistants, a 30-50% reduction in initial response time can dramatically improve the user experience.
The absence of errors from LLM Gateway further strengthens its position as a reliable option. Consistent performance with zero errors across hundreds of requests suggests a robust and stable implementation. OpenRouter's performance, while slower, also appears to be reliable within the scope of this specific benchmark, though the data does not detail the error rate for OpenRouter, only that LLM Gateway had zero errors.
Vercel's Role and Limitations
It is important to note that Vercel, often considered in similar discussions for its serverless functions and infrastructure, was not directly included in this specific TTFT benchmark. Vercel's AI gateway offering typically involves deploying custom serverless functions that act as intermediaries. The performance of such a setup is highly dependent on the specific implementation, the chosen LLM provider, and the Vercel platform's cold start characteristics for the particular function.
Directly comparing a managed gateway service like LLM Gateway or OpenRouter with a self-deployed solution on Vercel requires a different benchmarking approach. A Vercel-based gateway's TTFT would be influenced by the cold start latency of the serverless function, the network latency to the LLM API, and the efficiency of the code within the function itself. Therefore, while Vercel is a significant player in deploying AI applications, its performance as a gateway is not directly comparable to the managed services tested here without a custom benchmark.
Implications for Developers
For developers building applications that rely on LLM integrations, the choice of gateway can have a tangible impact on user satisfaction and application performance. The data suggests that LLM Gateway offers a clear advantage in terms of speed for Time To First Token, which is often the most sensitive metric for interactive AI features. This speed advantage could translate into a more fluid and engaging user experience.
If an application's core value proposition involves rapid AI-driven responses, prioritizing a gateway with demonstrably lower TTFT, like LLM Gateway, becomes a strategic decision. Furthermore, the reliability demonstrated by LLM Gateway, with zero errors in the tested runs, is a critical factor for production systems where stability is paramount. Developers should consider their specific application's needs and user expectations when selecting an LLM gateway, weighing factors like latency, cost, features, and reliability.
The decision to use a managed gateway versus building a custom solution on a platform like Vercel also involves trade-offs. Managed services offer simplicity and potentially optimized performance out-of-the-box, as seen in the LLM Gateway results. Custom solutions provide flexibility and control but require engineering effort to achieve comparable or superior performance and reliability.