The development of the AI mega-model is driving growth across industries, with "a projected average annual growth rate of 37.3% over the next decade, and global enterprises are expected to spend $800 billion on AI by 2027," according to a report on the subject. This rapid growth is prompting many organizations to make strategic shifts, and technology teams need to adapt the way they build, run, scale, and secure AI applications, networks, and infrastructure.
In order to help enterprises better meet the challenges of AI innovation needs, the concept of AI gateway based on API gateway solutions has been proposed, aiming to help enterprises control and manage application traffic anytime, anywhere, and provide better security for enterprise AI calls.
What is an AI Gateway?
The rise of GenAI (Generative AI) has brought new security threats to organizations and created a need for AI service management solutions.AI gateways can help technology teams have more control over controlling, securing and running AI models and APIs at scale. Overall, an AI gateway is similar to a traditional API gateway, acting as a unified endpoint that connects AI infrastructure and services, providing comprehensive control, security, and visibility into AI traffic to ensure stable interactions between applications and models.
AI gateways play different roles in different teams:
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development team: Accelerate application building by reducing friction, boilerplate code, and errors across multiple LLM (Large Language Model) APIs.
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Security and governance team: Protect applications by restricting access, ensuring secure use of AI models, and providing control, auditing, and visibility.
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Infrastructure team: Help AI applications scale by enabling advanced integration modes and leveraging cloud-native capabilities for high-traffic, zero-downtime connectivity.
How the AI Gateway Works
The AI Gateway can be configured as an add-on endpoint to an existing gateway agent or as a dedicated gateway agent endpoint in the AI infrastructure, depending on the needs of the organization. Developers can interact with endpoints exposed by AI gateways, while internal platform, gateway and security teams can manage policies using modern principles and declarative configuration.
AI gateways play a crucial role for organizations that integrate AI models (e.g., Large Language Models, LLM) into internal systems or business applications. Unlike traditional API gateways that mainly deal with authentication, access management, traffic management, and logging, AI gateways provide management and control functions specifically for AI models and services.
The core features of the AI Gateway include:
| functionality | descriptive |
|---|---|
| Multi-AI model provider integration | Supports integration with multiple AI model providers for flexible selection and switching of AI models. |
| unified endpoint | Providing a unified access endpoint for AI services and models simplifies integration efforts. |
| Application configuration and deployment | Supports configuration and rapid deployment of AI applications to meet the needs of different scenarios. |
| Security and Access Management | Ensure application and model security with support for sophisticated rights management and access control. |
| AI Workload Observability | Provides monitoring and observability of AI workloads for easy tracking and optimization of system performance. |
The Future of AI Gateways
As organizations integrate more AI models and services into their systems or applications, AI gateways must constantly adapt to the new challenges posed by changes in AI applications and models. In order to expand AI applications, the future development of AI gateways will gradually meet the following scenarios:
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model failover: As the use of AI models increases, AI gateways must be able to seamlessly switch between different system and model providers to ensure application reliability and resilience.
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semantic cache: By implementing semantic caching, similar requests can be handled efficiently, reducing calls to the LLM API, improving response time, and reducing operational costs.
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Retrieval Augmentation Generation (RAG): The AI gateway should be able to retrieve data from a specific data store to augment the inputs prior to the model request and enhance the quality and relevance of the AI output.
For teams integrating AI models into on-premises systems, the AI Gateway is a key tool for ensuring control, security, and observability of generative AI APIs.
Open source AI gateway project
It is an enterprise-grade AI gateway that provides organizations with a solution to address the need for secure calls to AI big model APIs.APIPark It greatly simplifies the process of invoking large language models, allows users to quickly connect to multiple language models without having to write code, and protects sensitive data and information while invoking AI models, enabling organizations to use AI technology more quickly and securely.
Currently.APIParkThe LLMs (Large Language Models) have been accessed by OpenAI, Claude (Anthropic), Gemini, Wenxin Yiyin, Dark Side of the Moon, and Tongyi Thousand Questions, which can be quickly invoked by enterprises.
In addition, APIPark supports enterprises to build their own API open platform, control API calling privileges through the approval process, and provide monitoring and analyzing tools to help enterprises track API usage and ensure safe and compliant sharing and use of AI models.