Introduction and Best Practices to AI Gateways

Artificial Intelligence (AI) has fundamentally reshaped how consumers and organizations interact. The rapid rise of Generative AI (GenAI) is driving automation, boosting productivity and transforming industries—all while demanding a sharp focus on customer experience.

Adopting GenAI platforms, such as large language models (LLMs), offers significant advantages: faster application development, quicker product iterations, and enhanced employee productivity, all leading to improved customer experiences and new revenue opportunities through operational efficiency and performance.

However, as AI adoption scales, organizations must address challenges related to AI workloads, including sustainability, safety, and often-hidden resource costs. Implementing robust guardrails is crucial to ensuring the performance, reliability, and security of applications as they integrate with AI models.

In this guide, we share the best practices for managing GenAI APIs through AI Gateways. You’ll learn: 

• Definition of an AI Gateway and its key features 
• Best practices for building safe and performant AI workloads
• Examples of common challenges solved by AI Gateways
• Future trends in AI Gateways