In the rapidly expanding universe of artificial intelligence, large language models (LLMs) have evolved from experimental curiosities to mission-critical business applications. But as organizations increasingly deploy AI models from multiple providers like OpenAI, Anthropic, and Google Gemini, a new challenge arises: how to effectively orchestrate the flow of information to and from these diverse models.
Enter the LLM router: a sophisticated system that directs user queries to the most appropriate LLM from a pool of different models. Instead of always using the same model for everything, it matches the right tool to the right job. Think of them as smart traffic controllers for AI requests.
But how do LLM routers help organizations stay agile in a fast-changing AI landscape, improve business decisions, and optimize model performance for their customers?
To explore these questions, we spoke with two AI experts from Ontra: Jerry Khong, Machine Learning Engineer, and Yuxiang Liu, Senior Manager, Machine Learning. They explain the critical role LLM routers play in streamlining development, enhancing security, optimizing costs, and ensuring peak performance in a multi-LLM environment.
Q: What problems do LLM routers solve? And why are they becoming essential for modern AI applications?
Jerry Khong: LLM routers are incredibly helpful for enterprises that need to deploy and manage multiple LLM providers like OpenAI, Anthropic, or Google Gemini. They offer a centralized platform for robust security practices, giving you a holistic view of all your machine learning services using LLMs. They also allow you to monitor all incoming and outgoing traffic.
Additionally, LLM routers enable more granular budget and cost tracking. Unlike a basic OpenAI dashboard that simply shows total spend, a router lets you delve deeper, understanding the cost of individual projects, which can then inform business decisions. They also provide rate limiting, preventing abuse or excessive LLM usage that might exceed budget requirements.
Yuxiang Liu: LLM routers are crucial because they let developers leverage a variety of LLM models without having to build special versions for each. Developers simply set a specific field in their requests, and the LLM router handles the routing to any supported model, significantly streamlining the development process.
This flexibility is vital in today’s rapidly evolving AI ecosystem. When Ontra first started this LLM journey, OpenAI was the clear industry leader. However, in the past year, and even in recent months, many competitors have matched or surpassed OpenAI’s model performance. Therefore, it’s essential for us to offer our customers the best possible models for each operation, rather than being tied to OpenAI. This approach also avoids a significant increase in development effort when switching to these superior, state-of-the-art models.
Q: Are customers increasingly asking for the flexibility to route to different LLM models?
Yuxiang Liu: Yes, they are. Some customers need models in the EU region for data residency, while others are weighing options like OpenAI versus Azure OpenAI. Ultimately, our customers simply want the best performance for their work. It’s our responsibility to identify and provide those top-performing models, which sometimes means looking beyond OpenAI.
Q: How do LLM routers handle the trade-offs between cost, latency, and accuracy when routing requests to different models?
Jerry Khong: Regarding LLM costs, my perspective is that as time progresses, particularly since OpenAI released GPT-3, the cost for each new model iteration has decreased significantly. Therefore, the greater importance lies in the availability to quickly implement new models. For instance, if OpenAI, Anthropic, or Google release a new model next week, having the capability to seamlessly integrate and utilize it is, in my opinion, more crucial.
Yuxiang Liu: I agree. I view the role of the LLM router in terms of different layers. Decisions about trade-offs between cost, latency, and accuracy should ideally be made at a higher application level. Each application possesses a clearer understanding of its specific trade-off requirements. The LLM router, on the other hand, primarily facilitates the easy sending of applications to different models, regardless of whether the aim is to optimize for latency, accuracy, or cost.
Jerry Khong: LLM routers give developers the flexibility to make trade-offs between cost, latency, and accuracy for their applications. Each team can decide, “Okay, we want to use this model for this purpose,” rather than dynamically routing based on every user request.
Q: Why is it important to have multiple LLM providers?
Jerry Khong: I believe it’s critical to have access to multiple LLM providers for rapid iteration. This allows Ontra to be model-agnostic rather than being locked into a single provider. Given that AI is still in its early stages, having diverse LLM options gives us the best chance for success.
Q: Can LLM routers help mitigate issues like model bias or hallucinations?
Jerry Khong: The LLM router itself doesn’t mitigate bias or hallucinations. However, it enables you to compare how different LLMs, such as Google Gemini versus OpenAI’s GPT-5, perform on specific tasks. While the router isn’t performing the mitigation directly, the ability to easily switch, change, and test models to evaluate their performance, is what indirectly helps in preventing hallucinations. In terms of model bias, I don’t think the router directly addresses that.
Yuxiang Liu: Exactly. It’s more about enabling you to address hallucinations and optimize model performance. Think of the router as a switch. For example, if an ML service or application notices that OpenAI isn’t performing well for certain queries, it can easily switch to Gemini just by changing the model name. Without the router, you’d have to develop an entirely new communication method for Gemini, wasting valuable development time.
Q: That makes sense. So, it’s about the application making a decision based on observed performance, like if ChatGPT isn’t performing well with a certain type of prompt, then routing it to a different LLM model.
Yuxiang Liu: Right, precisely. The application identifies performance degradation or other issues and can then easily choose to route those requests to a different model. The LLM router facilitates this seamlessly, sparing developers the complex task of figuring out direct communication with each individual model.
Jerry Khong: Yes, exactly. Our project lifecycle involves a constant feedback loop where we continuously evaluate how well the LLM is performing a given task. For example, we used the LLM router to help us evaluate which tasks GPT-5 performed better and only switched those tasks to GPT-5. The LLM router provides the flexibility to continuously change and adapt to new LLM models and approaches. This empowers our developers to iterate faster, which in turn helps our customers by delivering more features and better performance.
Q: What are the potential security and privacy considerations when using an LLM router, especially with sensitive data?
Yuxiang Liu: Concentrating all LLM requests through a single LLM router allows us to easily inspect all requests for potential security issues, such as prompt injection attacks or other vulnerabilities. By channeling all requests through one router, we can effectively scan both incoming requests and their responses for any problems, and then promptly reject or drop problematic requests. This is a far more efficient and secure method than having every application directly call LLMs like OpenAI or Gemini, which would make scanning and infrastructure setup for security much more difficult.
Jerry Khong: Think of it as guardrails. This centralization provides a granular view of everything happening, preventing the chaos of hundreds of unstandardized interactions. With the LLM router, we can see every incoming prompt, flag sensitive information like personally identifiable information (PII), or identify any malicious intent.
Q: So, it’s almost like air traffic control, but for LLM requests? A centralized system monitoring everything for safety, rather than disparate systems trying to coordinate?
Jerry Khong: Exactly. It’s about moving from a decentralized to a centralized approach.
Yuxiang Liu: Yes, centralizing all LLM traffic into one location provides better control and monitoring. It’s a fitting analogy.
Q: How do you ensure customers receive the best LLM performance for their specific tasks?
Jerry Khong: We aim to remove the burden from them of constantly deciding which LLM to use. My thought here is that some LLM router discussions suggest giving customers direct control, but I believe we should actually remove that complexity from them. They should trust us to make the best LLM choices on their behalf.
Yuxiang Liu: While some companies let users select models, we are generally against that approach. We believe we differentiate ourselves by having both ML experts and legal professionals within our company who rigorously evaluate models to determine the best fit for each part of the operational process. This ensures our customers receive optimal performance for their needs.
Jerry Khong: Precisely. When we evaluate an LLM’s performance on a task, we’re extremely thorough, ensuring it meets all requirements. We don’t want to give customers excessive control, as they might not have the right framework for evaluating prompt performance. We continuously iterate and test, so we essentially handle that for them. This approach democratizes AI, making it easier for customers who may lack extensive experience with prompts or understanding LLM behavior. We manage it all, allowing them to experiment and access LLMs in the most effective way possible.
Q: Things are evolving so quickly with AI models that constant evaluation is challenging. Having someone make that process more efficient and secure will significantly benefit customers.
Jerry Khong: Absolutely. It’s about taking care of it for them so they can access LLMs in the best possible way.
Q: And Yuxiang, to your point, our differentiation from competitors includes not just added security, but also the involvement of legal experts in evaluating performance, correct?
Yuxiang Liu: Exactly. It’s the human-in-the-loop aspect.
