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LLM Gateways: All You Need to Know

Updated: Jun 23

As organizations increasingly rely on Large Language Models (LLMs) for various applications, managing access, security, and monitoring has become crucial. This post explores the significance of LLM gateways, their impact on logging, security compliance, and the centralization of access from LLM applications to the models themselves.

Introduction to LLM Gateways

One of the primary challenges in deploying LLMs in production is the need to access multiple models from different providers, this design pattern is often referred to as "Multi LLM". As organizations transition from GPT-3.5 to GPT-4 or explore alternatives like Anthropic, maintaining direct access to each LLM provider becomes cumbersome.

As the figure above shows, each vendor may have slightly different API, so when you want to change between models, you may need to deploy a new version of the code. You'll need to know their API and develop expertise in each one. This is, of course, difficult as there are many vendors, including open-source options. Ideally, you should address this by unifying the API access, allowing seamless switching between models without modifying the application code. This is exactly what LLM gateways do, here's an example to how it's done with LLMstudio:

from llmstudio import LLM
model = LLM("anthropic/claude-2.1")"What are Large Language Models?")
AI developer trying to adjust the code keep up with the changes with AI APIs

Monitoring and Measuring LLM Applications

Besides just unifying AI access, making your code more generic and customizable to changes in the AI engine is crucial. Very often, you can think of AI application code as a set of wrapper functions around the engine and AI model that you can replace. Basically, the same LangChain code can be used to build a chatbot (like in this example), and then you can change the model by configuration to make your chatbot smarter or faster. This should be as easy as flipping a switch.

To know which models to choose, you will need to run optimizations, and for that, effective monitoring is essential. LLM gateways play a crucial role in this aspect by providing tools to log and monitor interactions with the models. This includes tracking latency, token usage, and response times, enabling organizations to optimize their applications based on performance metrics.

LLM Gateways in Action: Proxies and LiteLLM

Proxies are a core component of LLM gateways, acting as intermediaries between clients and LLM providers. They enable organizations to route requests to different models and handle failover scenarios seamlessly. For example, LiteLLM, a Y-Combinator-funded project, simplifies LLM API calls by providing a unified interface to interact with over 100 LLMs. This approach reduces the need for code changes and supports dynamic switching between models.

Open Source Solutions and Custom Integrations

LLMstudio, another open-source project contributed by TensorOps, exemplifies how organizations can leverage LLM gateways. It provides a UI for integrating and testing different LLMs, supporting custom extensions and enabling comprehensive logging and monitoring. This flexibility allows organizations to evaluate various models and prompts, ensuring the best fit for their specific use cases.

Scalable LLM Gateway Deployments

So far, I have only mentioned the benefits of introducing centralization to the LLM APIs, but such an approach can also have downsides. To handle the increased traffic and prevent bottlenecks, LLM gateways must be scalable. Containerized proxies can be deployed in Kubernetes clusters, enabling horizontal scaling based on demand. This ensures that the gateway can manage large volumes of requests without compromising performance or reliability. The diagram below shows the general architecture of a scalable LLM proxy on top of K8s.

Adding Extra Security, Privacy, and Compliance

After centralizing access to LLMs and adding logging and monitoring, LLM gateways can enhance security by centralizing access control, secret management, and logging. They also facilitate compliance with data privacy regulations by allowing organizations to mask sensitive information before sending requests to LLM providers. Some solutions, like, use small internal LLMs or neural networks to identify and redact Personally Identifiable Information (PII), ensuring that sensitive data remains within the organization.

Opaque Prompts makes sure PII doesn't slip out


LLM gateways are essential for managing access, security, and monitoring of LLM applications in production. By centralizing access, enabling seamless switching between models, and providing comprehensive logging and monitoring tools, they ensure that organizations can leverage the full potential of LLMs while maintaining control over costs and compliance. As the use of LLMs continues to grow, the role of LLM gateways will become increasingly important in ensuring efficient and secure deployment. You can hear more about it in the Webinar that we ran with Qwak:


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