Large language models (LLMs) are becoming a larger part of our lives on the internet on an almost daily basis. AI systems are connecting diverse and complex systems, and in 2025, the name of the game is agentic swarms.

Connecting multiple agents or models together has unlocked some critical and powerful functionality, but it has also introduced a much greater need to govern these connections. For this reason, the AI gateway — a connective tissue between AI agents, their data, and the services connected to them — has become very important.

Today, we’re going to dive into what an AI gateway is and what it can actually do for the average developer. We’ll look at how this differs from the API gateway of yesteryear, and look at some examples in the market of good AI gateway products.

What is an AI Gateway?

An AI gateway is essentially a middleware layer that acts as a central gatekeeper between AI models, services, and data systems. In essence, this allows services to connect to agents with a traffic controller in the middle, governing interactions and ensuring that they are secure, efficient, reliable, and routed to the correct methods and models.

This is a crucial development in the space of AI development. While early LLM systems were largely isolated from one another, more complex interactions have become common, utilizing multiple agents and workflows to do complex work. Without this connective layer, these connections would have to be ad hoc and individual. With an AI gateway, they can be managed at scale, allowing for better system utility.

What Can an AI Gateway Do?

Roughly, an AI gateway governs all aspects of how AI systems interact with users and with one another. This includes:

• Integration: AI gateways offer a more straightforward integration pathway for multiple AI models and services. Often, they provide a unified API endpoint for diverse models from different providers, allowing agent-to-agent communication to be much more efficient and structured.
• Security: Like their API gateway cousins, AI gateways often enforce security policies, manage access control, and ensure data privacy across multi-agent connections.
• Lifecycle management: AI gateways can handle the deployment, versioning, scaling, and sunsetting of both internal models as well as externally-connected systems, allowing you to use diverse models without having to worry about updates, maintenance, and other long-term issues.
• Routing and performance: Gateways of this nature can help route AI requests to their proper models, but can also handle multi-agent requests that might have significant overhead through intelligent performance tuning by providing caching, retrieval-augmented generation (RAG) features, and more. This can significantly reduce costs and improve response times, especially at scale.
• Monitoring and analytics: Finally, AI gateways often provide at least a basic visibility layer into your AI system, connecting performance metrics and tracking systems to allow full-connection visibility and observation.

How is this Different from an API Gateway?

For keen observers, this might not seem all that different from the API gateway, and in many ways, you’re right. An API gateway serves much the same functions we’ve detailed herein: the routing of HTTP requests, enforcing authentication and authorization, applying rate limits, and so forth. Where these systems differ is in their specialization.

It’s easy to forget in the age of the LLM just how specialized these systems are. A single LLM might have particular prompt methods, differences in application and format, and fundamental differences in ethos. These can multiply on top of each other, resulting in the need to mediate services in an LLM ecosystem in a way that doesn’t often exist in the LLM space.

The easiest way to envision this is to define what each gateway is in its core. An API gateway is largely deterministic and stateless — it’s built to work with clearly defined contracts and handle predictable, structured traffic from prebuilt and predefined clients. AI gateways, on the other hand, must mediate a lot more context and chaos. They interpret natural language prompts, map them to API calls, orchestrate multi-agent workflows, and maintain conversational states and task-specific memory banks.

API gateways handle structured requests from systems. AI gateways handle unstructured or goal-based input from agents and turn that into structured API interactions. While they may overlap in certain areas — like logging or access control — they ultimately serve different purposes and operate at various layers of abstraction. The API gateway is about managing known traffic efficiently. The AI gateway is about enabling agents to reason, plan, and act on APIs.

Example AI Gateways

To see what this looks like in practice, let’s look at some great examples of AI gateways in the marketplace.

Helicone

Helicone is an open-source gateway solution for OpenAI and other LLM systems. Its core offering is to add observability across the LLM stack, adding logging, metric analysis, caching, and more. The idea is that you can reduce costs through tracking at every stage and iterating on prompt versions, thereby increasing performance and reducing overall costs. Helicone is easy to use, with a quick drop-in via proxy URL.

PromptLayer

PromptLayer is a logging and observability tool that acts as a middleware layer to wrap OpenAI requests. This allows for a variety of more complex interactions, including debugging and analytics. Additional features, such as regressions, allow you to test the full flow of requests throughout all of your agents, identifying chokepoints and places where you can improve your data flow.

Portkey

Portkey offers itself as a middleware platform, a solution that provides everything from logging to prompt management. It supports a wide array of LLM models, including both enterprise models like OpenAI and Anthropic, as well as open-source models that might be more specifically tuned to a given business use case. PortKey offers a true platform solution, with a dashboard connecting all the functions across the product stack.

Solo.io

Solo.io is a cloud-native API gateway and service mesh solution that offers what it refers to as an “omnidirectional API gateway” through its Gloo platform. This platform includes the Gloo Gateway, the Gloo Mesh, and the Gloo Network, offering microservice control, security policy implementation, and performance monitoring across a diverse range of languages and stacks.

Lunar.dev

Lunar.dev is a developer-focused platform based around API observability and testing, offering to make it easier to understand, monitor, and validate how your APIs behave in their actual production environments. It provides tooling to capture live traffic with third-party AI APIs, analyze that traffic and its response patterns, and then detect changes in schema or behavior through automatically generated testing.

Final Thoughts on Emerging AI Gateways

AI isn’t going anywhere, and as it gets more complex and baked in throughout the software landscape, these sorts of solutions are going to become ever more important. AI gateways present an excellent solution for optimizing and controlling your stack at scale, and in the age of more demands around optimization and control, this is a blessing for most LLM adopters.

Did we miss any shining examples of AI gateways? Let us know in the comments below!