Partner POV | AI Gateways vs. API Gateways: What's the Difference?

This article was written by Liam Crilly, Senior Director of Product Management at F5, and contributed by our partner, F5.

The new hotness in AI infrastructure is the AI gateway. These systems are emerging as the critical buffer, a security and load balancing layer between AI applications and external users as well as internal AI modeling teams. The urgency for AI gateways is clear.

As large language models (LLMs), advanced computer vision algorithms, and other machine learning techniques become integral parts of applications, the challenges of their integration and management intensify. AI gateways provide a novel solution to these complexities, providing a centralized point of control for AI workloads.

To make matters more confusing, many AI gateway providers don't call themselves AI gateways. They may describe themselves as an AI developer portal, AI firewall, AI security, or AI load balancing — all of which contain elements of AI gateways.

Not surprisingly, AI gateways are frequently compared to API gateways. Managing APIs is a critical part of AI gateways, which are almost always designed to interact with external AI providers such as large clouds or OpenAI. (in fact, some companies that claim they have AI gateway offerings are actually built on API gateways and only add a few plugins tuned for AI).

However, it's critical to understand the differences between API gateways and AI gateways in order to properly design AI application infrastructure that can handle the requirements of modern application design and deployment. 

The Still Necessary Role of API Gateways

API gateways act as intermediaries between clients and backend services. They allow application developers, security teams and DevOps or Platform Ops teams to reduce the complexities of managing and deploying APIs in front of applications. API gateways also act as security and load-balancing layers for both protecting an organization's APIs and for protecting an organization from bad actors looking to exploit external APIs that the organization consumes.

The key functions of API gateways include:

• Governance: Defining and applying a set of policies, standards and processes to manage, monitor and control the usage, development and maintenance of APIs.
• Request routing: Intelligently directing requests to appropriate services, ensuring data reaches the correct AI model for processing.
• Authentication and authorization: Enforcing strict access controls through mechanisms like API keys, OAuth and JSON Web Tokens (JWTs).
• Performance enhancement: Optimizing response times and resource usage through rate limiting (preventing overuse) and caching (storing frequently used responses).
• Monitoring and logging: Offering detailed insights into API usage, error rates and overall system health, which are crucial for troubleshooting and optimization.
• Monetization: Providing monetization controls and management of API-based products and services, and determining who should be charged, and how much, for consumption of products and product capabilities delivered via API.

AI Systems Need Specialized Gateways

Most organizations today consume AI outputs via a third-party API, either from OpenAI, Hugging Face or one of the cloud hyperscalers. Enterprises that actually build, tune and host their own models also consume them via internal APIs. The AI gateway's fundamental job is to make it easy for application developers, AI data engineers and operational teams to quickly call up and connect AI APIs to their applications. This works in a similar way to API gateways.

That said, there are critical differences between API and AI gateways. For example, the computing requirements of AI applications are very different from computing requirements of traditional applications. Different hardware is required. Training AI models, tuning AI models, adding additional specialized data to them and querying AI models each might have a different performance, latency or bandwidth requirement.

The inherent parallelism of deep learning or real-time response requirements of inferencing may call for different ways to distribute AI workloads. Measuring how much an AI system is consuming can also require a specialized understanding of tokens and model efficiency.

AI gateways are also expected to monitor inbound prompts for signs of abuse such as prompt injection or model theft. In short, while API gateways are indispensable for traditional applications, they may fall short when handling AI-specific traffic patterns and requirements such as:

• Cost optimization: AI model usage can incur significant expenses. AI gateways provide detailed metrics and cost-tracking tools, enabling informed cost-management decisions.
• Model diversity: AI applications often use multiple models from different providers, each with its own interface and protocols. AI gateways offer a unified interaction point, simplifying development.
• Model versioning and deployment: AI models evolve rapidly. AI gateways streamline updates, rollbacks and A/B testing of different model versions.
• Security considerations: AI models, due to their potentially sensitive nature, demand specialized security protocols. AI gateways support fine-grained authorization, input validation and encryption tailored to AI workloads.
• Observability: Monitoring standard API metrics is insufficient for AI. AI gateways track model-specific metrics like inference time, bias detection, token usage and concept drift, providing the insights necessary for proactive maintenance.
• Load balancing: AI load balancing is more complicated than traditional load balancing because AI has a wider variety of computing jobs — inference and training, internal and external with many permutations. GPUs used for AI computing are extremely expensive, so ensuring that parallel programming pipelines are well-balanced and synchronized is paramount.

API and AI Gateways Will Co-Exist

To be clear, AI gateways are relatively new entrants and will likely evolve considerably over the near term. They also are not AI magic dust that must be applied in every instance. Some AI applications will work perfectly well with traditional API gateways.

For example, if an application is largely consuming from the OpenAI API and is not engaging in extensive tuning or additional training, then their application might have requirements very similar to traditional applications. In that case, paying the extra bit for an AI gateway and adding additional operational complexity might be overkill.

In reality, deployment patterns for AI applications may well contain both API and AI gateways because the two use cases will often coexist and even complement one another.

We are already seeing AI gateway functionality added to existing API gateway products. We also see AI teams deploying NGINX reverse proxies and ingress controllers to provide some governance, load balancing and delivery of AI applications (both training and inference).

In the future, AI gateways will come in many shapes and sizes within existing API gateway products and as standalone kits. In reality, the AI gateway is the logical evolution of the API gateway for the new AI era, just as API gateways evolved from reverse proxies.

Knowing the difference between these two types of gateways clarifies why they are both necessary and how they should be used, even if they live side by side as related or dependent applications or microservices.