The rise of AI and LLMs in our world is revolutionizing the applications we’re building and the customer experiences we’re delivering. This is one of the pivotal moments in our industry where we cross over an intersection in our technology evolution to enter a new journey with a paradigm shift. Past intersections were the rise of mobile, the rise of the cloud, and the rise of microservices, among others. Some people may even say that AI is as revolutionary as the birth of the internet.
Involved as I am in the world of APIs in my role as CTO of Kong, I can’t help but notice that AI usage is driven by APIs as its backbone:
As such, the secular tailwinds of APIs are being strengthened by the adoption of AI. Today, more than 83% of Internet traffic is API traffic. Now that we have a new category of AI traffic, I wonder how much API traffic will grow in the coming years and how much of that traffic will be related to AI. The majority of our API traffic today is already driven by non-browser experiences like mobile and wearable devices, and AI is poised to take a big chunk of net-new API traffic in the world.
AI Gateways bring order, security, and efficiency to your LLM operations. Serving as the essential control point between your apps and AI models, they manage access, monitor usage, and ensure responsible AI deployment.
Today, more than ever, organizations face rising stakes in three critical areas:
In short, AI Gateways aren't optional—they're mission-critical for sustainable and safe AI adoption.
This graph shows the types of API traffic. How much API traffic will be captured by AI in the coming years?
To start using AI in our products, developers and organizations around the world need to develop new best practices and adopt new technologies to fundamentally establish how AI consumption is being governed and secured.
There are many areas that need to be addressed in this regard:
Open source models are rapidly improving and even surpassing some private models. Source: ARK Invest
As the adoption of AI increases in the organization, we want developers to rapidly iterate and build new capabilities without having to manage the specific cross-cutting concerns around AI usage. Therefore, to improve the productivity of the application teams and to securely and responsibly leverage AI in the organization, AI needs to become a service offered by the core platform in the organization, which is available for consumption from any product that may want to use it. By doing so, we can avoid reinventing the proverbial wheel when it comes to AI adoption across our teams.
An AI Gateway is a specialized middleware layer designed explicitly for managing and securing interactions with Large Language Models and other AI-powered services. It sits between your applications and AI models, handling essential functions such as request routing, authentication, authorization, rate limiting, and traffic monitoring.
To accelerate the adoption of AI in the organization with the right level of AI observability, security, and governance, organizations will start adopting the AI gateways to provide a distributed AI egress to any LLM and model that developers may want to use. By doing so, we have a centralized place to manage AI consumption across every team, whether the AI models are hosted in the cloud or self-hosted.
Traditional API Gateways provide routing, load balancing, and standard security features for general APIs. AI Gateways, however, come equipped with advanced capabilities tailored specifically to the unique complexities of LLMs:
The AI gateway operates in a similar way to a traditional API gateway: instead of acting as a reverse proxy for exposing our internal APIs to other clients, it is being deployed as an egress proxy for AI traffic generated by our applications. That traffic is being directed either inside or outside the organization depending on where the backend AI models are being hosted (in the cloud or self-hosted).
For business leaders, implementing an AI Gateway translates directly into measurable, strategic value.
AI Gateways help secure your AI assets by enforcing robust security policies, such as encryption in transit and at rest, granular access controls, and data masking. They enable compliance with strict regulatory frameworks including GDPR, HIPAA, and emerging AI-specific legislation.
AI costs can quickly spiral without proper management. With features like caching, request throttling, and token usage tracking, AI Gateways provide visibility and control, optimizing resource allocation and reducing unnecessary spend.
However, deploying an AI Gateway requires careful consideration of potential risks:
Some of the many cross-cutting concerns that need to be addressed with AI adoption.
As the adoption of AI increases in the organization, we need to accelerate our teams to be able to innovate without having to address the same cross-cutting concerns in every application. Without an AI gateway, we risk introducing complexity, fragmentation, security blindspots, shadow IT, and overall lower efficiency and higher costs.
In the same way that we adopt an API gateway to cater to API cross-cutting concerns, we will adopt an AI gateway to do the same for all AI consumption. To simplify the architecture, ideally, the AI gateway will be managed by the platform team and offered as an internal core service to every application that needs to use it. This has the benefit of providing a unified control plane to manage all AI consumption generated by the organization and therefore gives us the opportunity to more quickly implement security policies, observability collection, and potentially implement developer pipelines for automating the onboarding of different teams whenever they need to access AI in their applications.
AI gateway becomes a core platform service that every team can use.
Deploying an AI Gateway is more than a technical exercise—it requires organizational alignment and robust governance. An AI gateway will support multiple AI backends (like OpenAI, Mistral, LLaMA, Anthropic, etc.) but still will provide one API interface that the developers can use to access any AI model they need. We can now manage the security credentials of any AI backend from one place so that our applications don’t need to be updated whenever we rotate or revoke a credential to a third-party AI.
Then it can implement prompt security, validation, and template generation so that the prompts themselves can be managed from one control plane and changed without having to update the client applications. Prompts are at the core of what we ask AI to do, and being able to control what prompts our applications are allowed to generate is essential for responsible and compliant adoption of AI. We wouldn’t want developers to build an AI integration around restricted topics (political, for example), or to mistakenly set the wrong context in the prompts which can then be later exploited by a malicious user.
Because different teams will most likely use different AI services, the AI gateway can offer a standardized interface to consume multiple models, simplifying the implementation of AI across different models and even switching between one another.
Identify key stakeholders across IT, security, compliance, and business units. Establish a dedicated AI Gateway Ops team responsible for ongoing management, security, and compliance monitoring.
The AI gateway could also implement security overlays like AuthN/Z, rate-limiting, and full API lifecycle governance to further manage how AI is being accessed internally by the teams. At the end of the day, AI traffic is API traffic.
Develop clear policies governing data handling, prompt engineering guidelines, logging, and auditing. Leverage existing frameworks such as Microsoft's AI Governance Framework or build bespoke policies tailored to your organizational needs.
AI observability can be managed from one place and even sent to third-party log/metrics collectors. And since it’s being configured in one place, we can easily capture the entirety of AI traffic being generated to further make sure the data is compliant, and that there are no anomalies in the usage.
Implement real-time dashboards to monitor usage metrics, detect anomalies, and ensure compliance. Integrate gateway logs and alerts with Security Information and Event Management (SIEM) platforms to automate compliance workflows.
Last but not least, as we all know AI models are quite expensive to run, being able to leverage the AI gateway will allow the organization to learn from its AI usage to implement cost-reduction initiatives and optimizations.
Artificial Intelligence driven by recent developments in GenAI and LLM technologies is a once-in-a-decade technology revolution posed to disrupt the industry and forever change the application experiences that every organization is building for their customers.
APIs have always been driven by new use cases (think of the mobile revolution, microservices, and so on) and AI further increases the amount of API traffic in the world: either APIs that our applications use to communicate with the AI models themselves, or APIs that the AI models use to interact with the world.
AI consumption is hard. There are serious data compliance ramifications when using models that can be shared across different applications, and perhaps even shared across different organizations (think of fully managed cloud models hosted in a shared environment). In addition, AI traffic needs to be secured, governed, and observed like we already do for all other types of API traffic coming inside or outside the organization. It turns out there is a great deal of infrastructure we need to put in place to use AI in production.
With an AI gateway, we can offer an out-of-the-box solution for developers to use AI in a quick and self-service way, without asking them to build AI infrastructure and all the cross-cutting capabilities that it requires. By adopting an AI gateway, the organization has the peace of mind that they have full control of the AI traffic being generated by every team and every application, and they finally have the right abstraction in place to ensure that AI is being used in a compliant and responsible way.
Marco Palladino,
CTO at Kong
An AI Gateway is a specialized middleware layer that manages and secures interactions between your applications and AI models (like Large Language Models, or LLMs). It handles vital tasks such as authentication, authorization, routing, rate limiting, data masking, and prompt management—all under a single control plane.
While both serve as proxies to manage traffic, AI Gateways include advanced capabilities specific to AI-driven workloads. These capabilities include prompt engineering controls, detailed token analytics, and session-based conversational context, which are essential for the secure, efficient, and compliant delivery of AI services.
Organizations face critical challenges in security, compliance, and cost management when adopting AI. An AI Gateway provides centralized governance, helps prevent data leaks or compliance violations, and enables cost controls by monitoring token usage and optimizing AI spend across various LLMs.
AI Gateways implement robust security policies like data masking, encryption, and access controls. They allow organizations to track and manage all AI traffic in one place, ensuring alignment with regulations (e.g., GDPR, HIPAA) and offering fine-grained control over how AI is accessed and utilized.
Yes. AI Gateways provide visibility into token usage and allow for optimized resource allocation. Cost-saving features like request throttling, caching, and usage analytics help organizations rein in runaway LLM expenditures, ensuring a more predictable AI budget.
AI Gateways serve as a single control plane for managing how AI is consumed across an organization. They streamline oversight of model usage, enforce best practices like prompt validation, and offer centralized visibility into AI traffic—making it easier to establish and maintain governance policies.
AI Gateways allow developers to connect to and switch between multiple AI providers or self-hosted LLMs through one unified interface. This simplifies integration, reduces development overhead, and ensures your teams can leverage the best AI model for each use case.
Common challenges include creating a single point of failure, risking vendor lock-in, or over-relying on prompt filtering to combat LLM hallucinations. Mitigate these risks by implementing redundancy, adopting open standards or multi-cloud strategies, and complementing gateway filters with model fine-tuning.
An AI Gateway inspects and validates prompts before they reach the model, helping to prevent prompt injection attacks or inappropriate content. Organizations can centrally manage and update prompts without having to modify individual client applications.
Kong’s AI Gateway is designed for multi-LLM adoption, cost efficiency, and enterprise-grade governance. Visit the Kong AI Gateway product page to learn about its features, request a demo, or explore documentation to see how it can seamlessly integrate into your existing infrastructure.
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