Best Practices for Managing and Scaling Generative AI

By 2025, it is estimated that there will be 750 million apps using large language models (LLMs), and 50% of digital work will be automated through LLM-based software. Despite the technology’s rapid growth and relative accessibility, however, applications and services being built on top of LLMs cannot be deployed out of the box.

Concerns around privacy, compliance, and security have somewhat slowed mass adoption at the legacy enterprise level despite the opportunities many businesses perceive are possible with AI.  Companies must also be mindful of their data security, given that there is an unknown quality in how AI may attempt to access different data repositories. Moreover, integrating into existing IT infrastructures and scaling these applications will require planning and software that makes management at the API-level simple and transparent.

This is where AI Gateways can prove invaluable by providing production-grade AI infrastructure to both developers building these new LLM-driven use cases, and platform owners that are supporting them.

Understanding AI Gateways

An AI Gateway is a central hub for managing and orchestrating the deployment and operation of AI models and tools. It is an intermediary layer that facilitates secure, observable, efficient, and scalable interactions between AI services and their consumers. Core functionalities include prompt and model lifecycle management to provide scalable workflows and automation for scaling AI usage across every LLM provider, cloud or self-hosted. It provides access control to LLMs, which manages who can use the AI resources and authentication, ensuring that only authorized users can access sensitive data. Additionally, it provides monitoring and logging capabilities to track the usage and performance of AI models, allowing for detailed analytics and insights, including cost tracking. These features collectively ensure that AI operations are secure and compliant and optimized for peak performance and reliability.

10 Key features and why they are important

AI Gateways incorporate several critical features that enhance AI deployments’ productivity, security, performance, and manageability. Here are some key features to look out for:

1. Multi-LLM support across both cloud and self-hosted models through a unique interface that can be incorporated once by the developers and without changing code, which allows developers to use all the most popular LLM technologies at the flip of a switch.
2. The ability to semantically cache, secure, and route AI requests like semantic caching to ensure higher performance at a lower spend for LLM traffic. Also, semantically firewall AI usage is based on the essence of the meaning of the prompts, without having to specify explicit rules that don’t work well with AI traffic as they can be easily bypassed. Semantically route AI traffic to models better suited to respond to the incoming prompts as well.
3. Easily create RAG pipelines that can be applied on the fly to incoming prompts to ensure a low rate of hallucinations.
4. Advanced AI load balancing and routing across cloud and self-hosted models to ensure uptime, maximum performance, and lowest latency for AI-driven applications – resulting in better customer experiences. The ability to automatically implement A/B testing and canary releases across different fine-tuned models will ensure the ability to innovate continuously without downtime.
5. Access control and authentication are essential to ensure that only authorized users can interact with AI models and data. This often includes multi-factor authentication (MFA) and role-based access control (RBAC), which provides multiple layers of security and fine-grained permissions management, ensuring that users can only access the resources necessary for their roles. Also, the ability to inject and rotate, at runtime, the LLM credentials and the ability to create access tiers for the developers without sharing the LLM credentials themselves.
6. Monitoring and logging capabilities are another crucial aspect of AI Gateways. These provide real-time visibility into API usage and performance, allowing administrators to track access patterns, identify potential security threats, and understand how AI models are utilized and how much spend they incur. Detailed logs facilitate compliance with regulatory requirements by maintaining a comprehensive audit trail of all interactions with AI services.
7. LLM token rate limiting and quotas are implemented to prevent resource overuse and ensure fair distribution among users. By capping the number of requests – and prompt and response tokens within a request – a user or application can make within a specific time frame, AI Gateways protect the system from being overwhelmed by excessive traffic, which can lead to performance degradation or service outages. Quotas help manage resource allocation, ensuring critical applications have the necessary resources without being affected by less important or malicious usage.
8. Data encryption, both in transit and at rest. Encryption ensures that sensitive information processed by AI models is protected from interception and unauthorized access, maintaining data confidentiality and integrity. AI Gateways also support version control and lifecycle management of AI models, enabling organizations to track changes, deploy updates seamlessly, and roll back to previous versions if necessary. This ensures that AI models are always operating optimally and securely.
9. Increased developer productivity when building and releasing AI applications to production without having to build AI infrastructure from scratch every time.
10. Integrating AI Gateways allows for seamless connectivity between AI models and various business applications. Acting as a bridge enables real-time data processing and decision-making, enhancing operational efficiency. This ensures that AI tools can be easily incorporated into existing workflows, making deploying and managing AI technologies more effective and streamlined.

Best practices for deployment

Implementing AI Gateways effectively means implementing the infrastructure necessary to go to production with AI while adhering to several best practices to ensure optimal performance, security, and scalability. First, it is crucial to establish comprehensive access control and authentication mechanisms, such as multifactor authentication and role-based access control, to safeguard sensitive data and AI models.

Regular AI monitoring and logging should be implemented to provide visibility into API usage and detect potential security threats promptly. Additionally, deploying rate limiting and quotas helps manage resource allocation and prevent service disruptions caused by excessive traffic. Lastly, maintaining up-to-date encryption protocols for data in transit and at rest ensures data integrity and confidentiality, while version control and lifecycle management of AI models facilitate seamless updates and rollback capabilities. Following these best practices ensures that AI Gateways are deployed securely and efficiently, maximizing their potential and minimizing risks.

The future of AI Gateways is set to evolve rapidly, driven by advancements in AI technology and increasing demand for robust AI management solutions. The rise of edge computing will see them being deployed closer to data sources, reducing latency and improving real-time processing capabilities.

Another significant trend will be incorporating more sophisticated data analytics and visualization tools, providing deeper insights into AI operations, enabling more informed decision-making, and even more AI-driven semantic capabilities. These developments will ensure that AI Gateways continue to play a critical role in the secure and efficient deployment of AI technologies across various industries and in their ability to ensure the developers can productively deliver customer experiences quicker by leveraging production-ready AI infrastructure.

Marco Palladino, founder and CTO at Kong Inc.