As AI becomes a cornerstone of digital transformation, IT leaders are under pressure to deliver innovation at scale while maintaining control, security and cost efficiency. But as enterprises adopt technologies like LLMs to build AI-driven experiences, a new challenge is emerging: managing the sprawl of large language model (LLM) usage across the enterprise.Enterprise AI: From Experiments to AgentsGenerative AI (GenAI) has shifted from hype to reality. Organizations across industries — from financial services to manufacturing — are already embedding AI into core processes and customer-facing applications. Reference architectures for AI applications have evolved rapidly, from prompt engineering to Retrieval-Augmented Generation (RAG), and lately to agentic AI.The future of enterprise AI is agentic. AI agents are the most promising architecture to leverage AI in the enterprise. Enterprises are now building specialized agents to support the needs of their customers and employees.LLM Sprawl Is a New Enterprise RiskAI innovation is no longer confined to centralized IT teams. Business units, innovation labs and even citizen developers are spinning up their own AI projects. While this decentralized innovation is good for speed, it may create critical challenges for IT, which can be grouped under the umbrella of LLM sprawl:Who’s calling LLM APIs and how often?Are we exposing sensitive data to external models?How can we track LLM usage and manage costs?Without proper governance, enterprises risk overspending, data leakage and shadow AI projects that can undermine security and compliance standards. IT leaders need to ensure LLM usage is secured, cost-effective and governed.The AI Gateway: Centralized Governance and ControlJust as API gateways help protect the enterprise from inbound threats, an AI gateway helps protect outbound traffic to AI services. It becomes the strategic control point for how your enterprise accesses and manages LLMs.By integrating an AI gateway into an API gateway, you can gain:A single point of access for enterprise-wide use of LLMs and GenAI APIs.Governance and policy enforcement to control who uses what, how and when.Cost management and visibility through usage analytics and dashboards.This isn’t about slowing down innovation — it’s about giving IT leaders the tools to manage it responsibly.What To Look For in an AI GatewayThere are four areas you should focus on so that your AI gateway can address the enterprise AI governance challenges.1. Cost Management and OptimizationIf you have ever been worried about the costs of your enterprise AI projects due to uncontrolled calls to monetized LLM APIs, an AI gateway can help take this worry off your shoulders. Gateways are designed to give you back control by managing and possibly reducing costs:Rate and token limits help avoid budget overruns by throttling usage based on defined thresholds.Caching of frequent prompts can help address redundant calls, costs and response times.This directly supports your IT finance goals: manage spend, predict costs and optimize performance.2. Enterprise-Wide VisibilityWith more AI projects built throughout the enterprise, it can be hard to keep track of different AI initiatives and attribute the costs they incur.Built-in dashboards provide real-time insights:Track API calls by app, team or business unit.Understand usage patterns and spot anomalies.Attribute costs accurately to the right projects.This clarity helps justify AI investments and identify which initiatives deliver real business value.3. Developer Enablement With GuardrailsYou want your teams to build AI projects fast, but still in a controlled way. An AI gateway can empower teams to innovate while prioritizing security in the following ways:Offer LLM access through a shared enterprise account, keeping credentials protected.Provide a self-service portal where teams can discover and use approved AI models.Enforce your security and compliance policies with data masking, access control and audit trails.Rather than building governance from scratch for every team, you give them a secured platform to move fast.Use Cases: Where an AI Gateway Adds ValueEnterprises have a wide range of AI projects, from customer-facing applications to internal applications.Customer-facing applications use AI to help improve customer experience. AI chatbots are a new form of user interface, complementing or replacing graphical user interfaces (GUIs). For example:AI service agents that help reduce call-center load.Conversational travel agents that replace complex interfaces.Teams across the enterprise are integrating AI into internal workflows in ways including:HR bots for vacation booking and payroll questions.IT support agents for self-service troubleshooting.Whether the AI model is used in customer-facing or internal applications, an AI gateway can help you govern access, provide visibility and control the cost.Looking AheadAI agents should act intelligently, and for that reason, they need API-based access to AI models such as LLMs. But that’s not sufficient; they also need secured access to your enterprise APIs to fetch data, trigger workflows and act autonomously. Organizations must evolve from simply “using LLMs” to strategically managing access to LLMs and enterprise APIs.That’s where a tool like IBM webMethods Hybrid Integration comes in. It provides an integrated solution covering both:An AI gateway to manage an agent’s access to LLMs.An API gateway and Model Context Protocol (MCP) to manage an agent’s access to enterprise systems.Together, they form a foundation for secured, scalable AI and automation. You get control, consistency and visibility without slowing down your teams.What IT Leaders Should Do NowAI gateways are experiencing exponential growth – and for good reason. If you want your AI strategy to scale, you should govern access to LLMs like any other enterprise-critical capability with an AI gateway. To assess your AI gateway maturity, start asking yourself the following questions:Do you have visibility into the calls that your applications are making to AI models?Are you sure that all your applications are configured to use the enterprise AI account?Are you confident that your applications aren’t sending sensitive information out to external models?If the answer is anything less than a confident yes, it’s time to evaluate your need for an AI gateway.Explore IBM AI GatewayIBM’s webMethods Hybrid Integration and the AI gateway capabilities in IBM API Connect offer IT leaders the tools to address this challenge, enabling secured, governed and efficient AI integration across the enterprise.The AI-driven enterprise isn’t a vision of the future — it’s already here. As an IT leader, your role is to make sure it’s governed, scalable and built to last. IBM’s AI gateway helps you get there.Take the next step:Discover AI gateway for IBM API Connect.Request a free trial.Talk to an IBM expert about AI gateway.The post Taming LLM Sprawl: Why Enterprises Need an AI Gateway Now appeared first on The New Stack.