In recent years, AI has become a hot topic, and big models have become a key driver of business innovation and growth in a wide range of industries. With this comes the question, "How can organizations securely manage and deploy the challenges of AI applications?" AI infrastructure must be designed not only to support existing business needs, but also to be able to adapt to the rapid development of future technologies. Against this backdrop, the concept of the AI Gateway has emerged, which plays a critical role in the integration, management and optimization of AI applications.
In this article, we will explore the differences between AI gateways and API gateways, analyze why AI systems need specialized gateways, and suggest guidelines for choosing the right AI gateway for your organization.
AI infrastructure will be the new trend
In recent years.AI GatewayIt is quickly becoming the security barrier and load balancing layer between AI applications and external users and internal AI teams. With the increasing complexity of integrating and managingLarge Language Models (LLMs)The AI Gateway provides a centralized solution to these challenges, serving as a control center for AI workloads, advanced computer vision algorithms, and other machine learning techniques.
Despite the obvious need for an AI gateway, many of the companies currently offering these services don't refer to their products directly as "AI gateways", but rather as "AI gateways".AI Developer Portal、AI Firewall、AI Security、AI load balancing etc. are described under different names, but in reality all of these scenarios contain elements of an AI gateway.
In a nutshell, AI gateway is a brand new concept, and a new term that has been introduced by organizations with the rise of the big AI model for AI security calls, as well as for securing their own information and data. Of course, the AI gateway whose bottom layer is the API gateway.
the reason whyAI Gatewaytogether withAPI Gateway In comparison, we can see that the managementAPI is also an important part of the AI gateway, especially with large cloud service providers orOpenAI when interacting with external AI providers such as However, in order to better design AI application infrastructures, understanding theAPI Gateway cap (a poem)AI Gateway The distinction between is particularly important.
Key role played by the API gateway
API Gateway As an intermediary between client and back-end services, it can help developers and operations teams simplify the management and deployment of APIs, as well as provide security and load balancing features. an API gateway not only protects an organization's APIs, but also prevents malicious attackers from leveraging external APIs.
The following are the main features of the ****API gateway:
| functionality | corresponds English -ity, -ism, -ization |
|---|---|
| gestion | Define and implement a set of policies, standards, and processes to manage, monitor, and control the use and maintenance of APIs. |
| Request Routing | Intelligently forward requests to the right service, ensuring that the data reaches the right AI model for processing. |
| Certification and Authorization | Strict access control through mechanisms such as API keys, OAuth and JWT. |
| performance enhancement | Optimize response time and resource usage with rate limiting and caching. |
| Monitoring and Logging | Provides detailed insights into API usage, error rates, and system health. |
| Earnings management | Controls and manages API-based product services and determines rates for products and services. |
However, the API gateway plays a critical role in governance, security, and performance optimization in AI systems. But its limitations are exposed when dealing with AI-specific requirements.
Why AI systems need specialized gateways
Today, most organizations use AI results through third-party APIs, with common vendors includingOpenAI or large cloud providers. And organizations that build, tune, and host their own models typically use them through internal APIs.
AI Gateway The basic responsibility is to enable developers, AI data engineers, and operations teams to quickly call and connect AI APIs with theAPI Gateway works in a similar way.
However, there are significant differences between API gateways and AI gateways when it comes to handling AI applications. For example, the computational requirements of AI applications are very different from traditional applications and often require specific hardware to support. Each process of training an AI model, tuning the model, adding special data, and querying the model is important to theperformances、procrastinate maybebandwidths The requirements can all be different.
Additionally, the need for parallelism in deep learning or real-time inference may require a different approach to distributing AI workloads. For AI systems thatDepletion of resources, also requires special consideration, such as fortokencap (a poem)model efficiencyof understanding. AndAI Gateway It is also necessary to monitor incoming requests to detect possibleAbusee.g.Cue Injection maybeModel theft。
| comparison term | API Gateway | AI Gateway |
|---|---|---|
| Cost optimization | Managing the Cost of API Calls | Provides detailed AI model cost tracking and optimization tools |
| Model diversity | Managing a Single API | Manage multiple AI models and unify interface points |
| Model version control and deployment | Control over API updates | Streamline version updates, rollbacks and A/B testing of AI models |
| safety | Support for basic authentication and authorization | Supports fine-grained privilege control and input validation for AI models |
| observability | Monitoring Standard API Metrics | Monitor AI model-specific metrics such as inference time, bias detection, token usage, etc. |
It is clear that the AI gateway has a significant role to play in handling the AI-specificcomputational model cap (a poem)security needs It offers significant advantages, especially in terms of flexibility and expertise in dealing with the complexity of AI applications.
For example, the open source AI gateway A solution is provided to help organizations achieve simultaneous access to multiple large-scale language model scenarios.
APIPark AI GatewayIt greatly simplifies the process of invoking large language models, allowing users to quickly connect to multiple language models without writing code. The platform protects sensitive data and information while invoking AI models, enabling organizations to use AI technology more quickly and securely.
Currently, APIPark has access to a number of LLMs (Large Language Models), including OpenAI, Claude (Anthropic), Gemini, Wenxin Yiyin, Dark Side of the Moon, and Tongyi Qianqian, which can be quickly invoked by enterprises.
In addition.APIPark also supports organizations to build their own API open portals.The platform controls the calling privileges of APIs through a strict approval process. At the same time, the platform provides multi-dimensional API monitoring and analysis reports to help enterprises effectively track the use of AI models and their own API assets, ensuring that the openness and sharing of APIs, as well as the application of AI models, are more in line with the security and compliance requirements of enterprises.
How to choose an AI gateway?
Before applying an AI gateway, organizations need to carefully evaluate and select the right product. Here are a few key things to consider when choosing an AI gateway:
| Whether to support multiple models | Can the AI gateway easily handle diverse models from different vendors, both internal and external? |
|---|---|
| Advanced security and governance features | Does the gateway have security protocols designed for AI models? Can it detect potential abuse? |
| Cost management and optimization | Are granular usage and cost tracking tools available, as well as optimization strategies to control spending? |
| In-depth observability | Is it possible to track key AI model health metrics such as inference time, accuracy, drift and bias for proactive management? |
| Easy to integrate and expand | Does it integrate seamlessly into existing development and deployment processes? Can it scale to handle growing AI workloads? |
APIs and AI gateways will coexist
It's important to recognize that AI gateways are relatively new technologies, and they may evolve significantly in the future. In addition, AI gateways are not the "AI tool of all trades" that must be used in every scenario. Some AI applications may still work well with traditional API gateways. For example, if the application is primarily run through theOpenAI API Access to services that do not involve complex tuning or additional training may be similar to the needs of traditional applications. In this case, using an AI gateway may add additional cost and operational complexity, making it a bit of a "killjoy".
In fact, the deployment model for AI applications may contain bothAPI Gateway cap (a poem)AI Gateway, because their usage scenarios tend to coexist and complement each other. For example, someAPI Gateway Products have begun to increaseAI Gateway features, such as theKong；Some AI teams also useNGINX reverse proxy cap (a poem)Ingress Controller to provide governance, load balancing, and delivery of AI applications, while new gateway products have emerged, such as the aforementionedAPIPark AI Gateway。
In the future, AI gateways may exist in many forms, both as existingAPI Gateway part of the product, or may appear as a standalone solution. In effect, AI gateways are the natural evolution of API gateways in the age of AI, just as API gateways themselves evolved from reverse proxies. Understanding the difference between these two types of gateways can help organizations better understand their necessity and rationalize the choice or use of both when designing and deploying AI applications to ensure that they run optimally.