AI Gateway

Artificial intelligence has become a hot topic over the past couple of years. It’s transforming the enterprise technology landscape. Building on previous technological revolutions—from word processors that fundamentally changed document creation and workflow, CRM systems that transformed how organizations manage customer relationships, and accounting software that revolutionized financial operations—AI is introducing a new dimension of change. While these earlier innovations each transformed specific business functions, AI’s impact is uniquely pervasive across the entrerprise.

The challenges of implementing AI and Gen AI

As organizations move from experimental AI projects to production deployments, they encounter complex challenges that traditional networking infrastructure wasn’t designed to address. Understanding these challenges is crucial for building effective, secure, and governable AI systems.

Most of the AI service providers like OpenAI, Cohere, Azure and AWS offer their services such as LLMs and other AI services through APIs. At a technical level, using an LLM is similar to making any other API call—you send a request with your text and receive the model’s response. This consumption-based nature of LLMs creates unique challenges for cost management and resource optimization. Organizations struggle with attributing costs across different teams and projects, especially as usage patterns vary significantly. This requires sophisticated systems for tracking and controlling resource consumption through token-weighted controls. Semantic caching presents a particular opportunity for optimization, as many similar queries can be served from cache rather than making redundant API calls. Organizations must also implement intelligent model selection strategies, choosing the most cost-effective model for each use case while maintaining quality standards.

When employees interact with LLMs, they might include sensitive company or customer information in their prompts as the context to the LLM. Similarly, the LLMs might inadvertently include sensitive information in their responses to the users. This creates a significant security challenge: how do you prevent unauthorized data exposure while still allowing productive AI use? Similar to how web application firewalls (WAFs) protect traditional web applications, AI systems require specialized input validation and output constraints to ensure data security and compliance.

Organizations must also tackle the practical challenges of managing access to AI providers securely and efficiently. Unlike traditional APIs, AI services require dynamic access control that can adapt to varying usage patterns and security requirements. Traditional networking tools like routers and firewalls fall short, as they often lack provisions to manage credentials or semantically understand data in transit properly.

AI Gateway the solution

AI gateways are specialized tools that sit between applications and AI services and provide comprehensive solutions for these challenges. An AI gateway serves as a control plane for managing AI operations, providing organization-wide semantic request routing, intelligent caching, cost optimization, and AI provider failover capabilities. By handling these concerns at the infrastructure level rather than the application level, AI gateways enable organizations to implement consistent policies and optimizations across all their AI workloads, while reducing complexity for application developers.

An AI gateway is a specialized API gateway that can semantically understand requests and responses to handle and manage AI interactions. This understanding enables capabilities that traditional networking solutions cannot provide. It serves as an intelligent intermediary for AI traffic, providing sophisticated management capabilities that enhance applications’ interactions with AI services. This intelligence is crucial for several reasons.

First, the gateway provides sophisticated credentials and data management that traditional networking components like firewalls cannot deliver. It securely handles API keys, prevents sensitive data exposure, and ensures compliance with regulatory requirements—challenges that become increasingly complex as organizations scale their AI usage. Moreover, the AI gateway makes the processing of these concerns consistent across all infrastructure, including across multiple LLM instances.

Second, it implements AI-specific performance optimizations. The gateway can intelligently load balance requests based on their semantic content, cache similar queries to reduce costs and latency, and manage sophisticated failover between different AI providers when needed.

Third, it provides comprehensive visibility and control over LLM operations. Organizations can monitor usage patterns, implement cost controls based on token consumption, and enforce guardrails that prevent harmful or noncompliant LLM interactions. These capabilities prove essential as organizations scale from initial AI experiments to production deployments.