As businesses rush to adopt artificial intelligence, the costs are beginning to pile up. A 2026 survey from SaaS platform Zylo found that companies of all sizes are investing more in AI annually, with the largest organizations funneling nearly $5 million into AI-native applications. Meanwhile, a Gartner report projects global AI spending to exceed $2.5 trillion by the end of 2026. With many teams paying for multiple AI model subscriptions, leaders are turning to a new technique to keep their expenses in check: AI routing.
What Is AI Routing?
AI routing is the practice of selecting the cheapest AI model that can finish a given task. This technique is common in AI gateways, or middleware layers that monitor all interactions between large language models and applications. AI gateways connect an organization to various AI models, enabling them to route tasks to different models. By embracing AI routing, businesses can then avoid using the same model for every task and reduce expenses.
AI routing refers to assigning a task to the model that can complete it at the lowest price. It’s a simple yet effective strategy that could be a game-changer for organizations. Let’s take a closer look at what AI routing is, why it gives companies a competitive edge and how it could ultimately upend the way the AI industry does business.
What Is AI Routing?
An AI router is a software layer located between an application and multiple AI model providers that decides which model to assign a given task. Cost is the main metric these routers consider, but they can also be trained to factor in predicted performance, latency and other requirements. Teams can then choose different models for different situations, instead of using the same model every time. So, developers could deploy a lightweight model for a basic task and a heavyweight model for a complex problem, managing resources more efficiently.
While AI routers can be standalone software, they often are components of AI gateways, which is a middleware layer that acts as the control center regulating all interactions between large language models (LLMs) and applications. It processes routing requests, monitors data traffic, authorizes actions taken by AI agents and enforces governance policies and security best practices, among other functions that make it a vital part of modern tech stacks.
Why Companies Use AI Gateways
AI gateways alleviate many of the headaches that can come with deploying multiple AI models. For one, they save money. A traditional setup may require a business to pay a monthly subscription for each model it wants to deploy, tacking on costs that can quickly strain an IT department’s resources. An AI gateway serves as a single access point for various models, so teams can just pay for the gateway’s infrastructure, swap models for different tasks and avoid dedicating a chunk of their budget to a list of subscriptions.
Standardization is another major advantage of AI gateways. Imagine learning to work with open-source models from Europe’s AI startup Mistral, but then having to adjust to an entirely new set of procedures when using models from OpenAI and Anthropic. An AI gateway applies the same rules and protocols that shape interactions between all LLMs, creating a more uniform experience no matter which model a team chooses for a given task.
Perhaps the most lasting benefit of AI gateways, though, is the added peace of mind they provide. Cybersecurity incidents show how dangerous advanced models can be when they’re allowed to roam with few safeguards, even prompting industry leaders to call for a slowdown of AI development. By acting as a central filter for all LLM activity, AI gateways make it much easier to track data traffic, enforce policies across a slate of models and take proactive measures when any model behaves suspiciously.
How AI Routing Differs From Traditional API Gateways
If AI routers and gateways sound similar to application programming interfaces (APIs), that’s because they are. An API is a software mechanism that lets applications communicate with each other to exchange data and features. It oversees data requests, authorization procedures, rate limits and caching practices.
In essence, AI gateways play the same role for AI workloads. While they manage operational and security needs, they specifically handle LLM requests, tokens and more advanced cyber attacks like prompt injections. AI routers must also process more complex, multimodal data from written, audio and visual inputs. AI gateways are then specialized APIs tailored to the particular needs of generative AI models.
Key Features of AI Routing
AI gateways and routers can vary in their designs and capabilities, but these are the main features that form the backbone of every gateway:
Standardization: AI gateways establish a common API format for all models and applications. This way, teams don’t need to switch between different keys, APIs or software development kits.
Model Management: AI gateways decide which models to deploy for specific tasks. They also collect data on how users apply models and how many tokens each model uses to find opportunities to cut unnecessary spending.
Performance Monitoring: After deploying models, AI gateways monitor factors like error rates and latency. They can then identify issues early on and implement fixes as needed to improve routing.
Data Integrations: AI gateways can compile data from apps, cloud platforms, databases and other sources. They work with a range of APIs and third-party tools as well, so models can connect to various software and AI systems.
Security Protocols: An AI gateway creates a single access point for monitoring model activity and can restrict access to authorized users. Gateways secure data with authentication methods, encryption procedures and API keys.
How AI Routing Could Impact the AI Industry
Businesses are no longer willing to maximize their AI usage because of rising costs that continue to strain their budgets. Instead, they are pivoting to alternatives, such as investing in model-routing services or even subscribing to cheaper Chinese models. Although these decisions rein in spending and keep AI accessible for organizations, they threaten the very revenue model AI titans depend on to stay afloat.
Major players in the AI industry have built their long-term outlooks on the belief that customers will continue regularly using their most advanced products. But AI routing directly undermines this assumption, deploying simpler models for easier tasks and reserving more expensive models for bigger challenges. As a result, teams are less likely to subscribe to higher-end models when routing lets them use cheaper ones to get the same work done.
This shift not only damages AI leaders’ short-term revenue streams, but it also affects their prospects for going public. For instance, Anthropic’s IPO filing has already revealed $80.7 billion in net losses in 2026 so far, forcing the company to admit that it “may not achieve or sustain profitability in the future.” Losing consistent revenue from its premium offerings would almost certainly prevent Anthropic from ever breaking even.
After all, reduced revenue tied to AI gateways and routers would only add to the current pressures AI companies face, including finite resources, data center moratoriums and widespread political pushback. If tech leaders can’t convince investors that AI will generate profits in the years ahead, further investment in the technology will be harder to justify and the specter of an AI bubble may gain traction once again.
Frequently Asked Questions
What is an AI gateway?
An AI gateway is a middleware layer that oversees large language models, regulating how they interact with each other and applications. While they manage data traffic, authorization protocols and security policies, AI gateways have gained attention for their role in AI routing. These mechanisms connect to a range of model providers and route tasks to different models based on cost, latency and other metrics, helping companies get the most out of models.
Do I need an AI gateway if I only use one AI model?
No, AI gateways are ideal for teams that want access to multiple AI models for different tasks. That said, an AI gateway can help a business scale its operations if it eventually needs more than one model to handle its AI workloads.
Do AI gateways work with AI agents?
Yes, AI gateways can handle autonomous AI agents, but they are better suited for managing large language models. An agent gateway is best for teams looking to monitor the complex workflows of multiple AI agents.
