Senior Penetration Tester at a wholesaler/distributor with 501-1,000 employees
Real User
Top 5
Aug 3, 2026
I am currently evaluating the implementation of LLM Gateway on our agency environment. I have extensive experience with LLM Gateway and Guardrails, and it has become my primary focus at work. We are using LLM Gateway for our agentic workflows, which encompasses most of our current operations. We use this gateway for LLM proxies, primarily leveraging the AI Guardrails functionalities. We are implementing a comprehensive set of controls and a full verification layer for our API calls. We hire vendors such as Anthropic or OpenAI and import their keys into LLM Gateway. When we make this technology available to employees who need to use Cloud code, they access it through the LLM Gateway API key. When you call Cloud code using any prompt, that API call instantly passes through our LLM Gateway with all our Guardrails applied. This is the current workflow we are using and applying across the entire company.
Senior Architect at a tech vendor with 10,001+ employees
MSP
Top 20
Jul 9, 2026
My main use case for LLM Gateway is building an agentic system, a harness wherein we can build multiple use cases that will be supported mainly for PDLC and SDLC automation. Whenever we want to use LLM Gateway for PDLC or SDLC automation, we want to create spec-to-code or code-to-spec, and we want to convert ideas into specifications. Whenever we want to create any documentation or any features or epics from converting from code to spec-to-code using LLM Gateway, we need to call multiple LLMs, and we want some intermediate gateway wherein it will handle our authentication, authorization, as well as the cost optimizations can be seen. For that purpose, we have used LLM Gateway. Some of the use cases need multi-modal calls wherein we can use multiple models at the same time, so LLM Gateway is a valuable gateway to work with.
Our main use case for LLM Gateway is that our company has partnerships with multiple LLM providers including OpenAI, Claude, and Gemini. LLM Gateway acts as an interface between all three providers. I would describe it as a router that functions as middleware between our application and the AI providers so that we do not need to give or share API keys to each team. Our team calls LLM Gateway from their application, and all the keys and routing configurations are present in LLM Gateway. Its responsibility is to connect with Claude, OpenAI, or Gemini based on the request we receive. We have an application in which users can ask anything. For example, if a user is asking a general question, we call LLM Gateway and pass the model name as ChatGPT. It internally uses ChatGPT itself. If the question is related to the application we created, it internally uses RAG and goes to Claude. LLM Gateway is responsible for redirecting the request based on context. LLM Gateway also has an additional feature where if one of the models is unavailable at a time, it automatically redirects the request to another model, so there is no downtime in the application. The automatic failover feature ensures that if one model is not available, LLM Gateway redirects the request to another model.
LLM Gateway efficiently connects businesses with advanced AI language models, enabling streamlined data processing and insightful analytics for improved decision-making. It optimizes data interactions to enhance overall enterprise intelligence.Designed for integration, LLM Gateway offers scalable solutions tailored to dynamic environments, ensuring robust performance. Its comprehensive suite of tools provides seamless AI integration, empowering users to leverage linguistic capabilities for...
I am currently evaluating the implementation of LLM Gateway on our agency environment. I have extensive experience with LLM Gateway and Guardrails, and it has become my primary focus at work. We are using LLM Gateway for our agentic workflows, which encompasses most of our current operations. We use this gateway for LLM proxies, primarily leveraging the AI Guardrails functionalities. We are implementing a comprehensive set of controls and a full verification layer for our API calls. We hire vendors such as Anthropic or OpenAI and import their keys into LLM Gateway. When we make this technology available to employees who need to use Cloud code, they access it through the LLM Gateway API key. When you call Cloud code using any prompt, that API call instantly passes through our LLM Gateway with all our Guardrails applied. This is the current workflow we are using and applying across the entire company.
My main use case for LLM Gateway is building an agentic system, a harness wherein we can build multiple use cases that will be supported mainly for PDLC and SDLC automation. Whenever we want to use LLM Gateway for PDLC or SDLC automation, we want to create spec-to-code or code-to-spec, and we want to convert ideas into specifications. Whenever we want to create any documentation or any features or epics from converting from code to spec-to-code using LLM Gateway, we need to call multiple LLMs, and we want some intermediate gateway wherein it will handle our authentication, authorization, as well as the cost optimizations can be seen. For that purpose, we have used LLM Gateway. Some of the use cases need multi-modal calls wherein we can use multiple models at the same time, so LLM Gateway is a valuable gateway to work with.
Our main use case for LLM Gateway is that our company has partnerships with multiple LLM providers including OpenAI, Claude, and Gemini. LLM Gateway acts as an interface between all three providers. I would describe it as a router that functions as middleware between our application and the AI providers so that we do not need to give or share API keys to each team. Our team calls LLM Gateway from their application, and all the keys and routing configurations are present in LLM Gateway. Its responsibility is to connect with Claude, OpenAI, or Gemini based on the request we receive. We have an application in which users can ask anything. For example, if a user is asking a general question, we call LLM Gateway and pass the model name as ChatGPT. It internally uses ChatGPT itself. If the question is related to the application we created, it internally uses RAG and goes to Claude. LLM Gateway is responsible for redirecting the request based on context. LLM Gateway also has an additional feature where if one of the models is unavailable at a time, it automatically redirects the request to another model, so there is no downtime in the application. The automatic failover feature ensures that if one model is not available, LLM Gateway redirects the request to another model.