What is an AI gateway? Do you need another one?

Produced by | 51CTO Technology Stack (WeChat ID: blog51cto)

作者 | Liam Crilly

Compile | Words

From GitHub Copilot to Microsoft Office Copilot to ChatGPT and more, AI has shifted at the speed of light from "someday we'll get there" to "what's your AI strategy?"

As a result, organizations are rapidly embracing AI – creating an enhanced end-user experience, reduced operational costs, and a competitive advantage. New applications are emerging that are built around AI processes and workflows. Like most new applications and services, AI services, such as those provided by OpenAI or various cloud providers, are delivered and consumed through APIs.

So how do you interact with AI applications and large models? This is where AI gateways come in.

AI gateways are purpose-built systems for managing, securing, and observing surging AI traffic and application demand. As a result, they are quickly becoming an important product category. So what is an AI gateway?

1. What is an AI gateway: Quick definition

An AI gateway is a specialized device or solution designed to manage and simplify the interaction between applications and AI models, especially in the context of large language models (LLMs) and other AI services. The gateway acts as a central control point for AI traffic, providing a unified interface for applications to access various AI backends and models. AI gateways also allow operations and security teams to manage key areas such as security, governance, observability, and cost management.

Most AI gateways include the following sets of features:

1. Security and compliance

AI security is both the most important and the most important. AI applications may be used to process customer data or other forms of personally identifiable information and are often exposed to valuable proprietary company data. More and more third-party AI bots are trying to train on publicly available data without authorization.

The gateway handles authentication and zero trust, acting as a gatekeeper for AI services and API access.

In the face of these and other risks, AI gateways are becoming a new type of firewall. The AI gateway manages the security credentials of consumers and providers of AI services.

The gateway handles authentication and zero trust, acting as a gatekeeper for AI services and API access. It also provides an authorization layer to ensure that only approved users can access a specific service, or approve the use of a service based on defined policies. Policies may restrict usage based on geography, business unit, role, infrastructure provider, or infrastructure type.

For specific AI prompt management, the AI gateway can implement prompt security, verification, and template generation. This simplifies on-the-fly management by consolidating functionality in a single control plane that can be managed without the need to update the local development environment or different model systems or AI applications. This is essential for responsible and compliant AI use, as it prevents developers from building AI integrations around restricted topics or setting the wrong context in prompts.

In addition, AI gateways are used as firewalls or digital loss protection systems for AI data. A full-featured AI gateway protects against model poisoning, model theft, and other emerging cybersecurity threats to AI systems.

2. Load balancing and centralized consumption management

You may need an AI load balancer, even if you don't already have one. AI applications can be highly data-intensive and computationally dependent. Not managing traffic to AI applications can mean very expensive GPUs sitting idle, waiting for the resource-starved upstream part to finish the job. For consumer-facing products, the latency of AI applications is a killer – the longer you make someone wait for a chatbot response, the more likely they are to swipe left or right.

Then there's the issue of consumption. Today, most organizations are using multiple AI model-as-a-service offerings. These are mostly provided through cloud providers or other third-party services. The AI gateway provides a centralized platform for managing AI consumption across different teams and applications within an organization. This centralization is essential to maintain control over AI traffic and ensure that AI is used in a compliant and responsible manner.

AI gateways provide a centralized platform for managing AI consumption across teams and applications.

By providing a unified control plane and load balancer, AI gateways enable organizations to manage all AI consumption and observability collection. In AI, spending is different because it is measured in tokens rather than the volume of transactions or data.

However, a simple measurement of tokens is imprecise: some types of queries require more tokens to run the job, and the number of tokens required for the same prompt may change over time. In other words, suppose your standard application returns a variable amount of data for the same request. This is at the heart of AI nuances – consumption can be harder to predict and control.

3. Streamline developer workflows

Today, developers and platform operations teams are faced with a dizzying array of AI integrations and APIs to choose from. Cloud providers can simplify consumption through their APIs, but the design of the AI gateway allows for easy management of AIAPIs and a single integrated management point.

The AI gateway supports multiple AI services and provides a single API interface that developers can use to access any AI model they need. Endpoints may allow developers to access a variety of models offered by OpenAI, but also access to thousands of more finely tuned open-source models and tools included on Hugging Face. AI gateways can automatically enable teams that need access to AI services.

Yes, the spread of AI is one thing, and you don't want your developers to mess with it.

This unified API endpoint streamlines development workflows and speeds up the integration process. This, in turn, enables developers to focus on building AI applications rather than managing complex integrations.

Just as developers want to have a range of frameworks and open-source modules to choose from when developing software, AI developers increasingly want a wide range of models and AI services to choose from in order to tailor applications faster and more appropriately. Yes, the spread of AI is one thing, and you don't want your developers to mess with it.

4. Cost optimization, monitoring, and observability

AI gateways allow organizations to learn from their AI usage to manage and reduce costs. The gateway provides insight into the consumption quota for each model, enabling efficient resource allocation and cost control. This transparency allows users to effectively manage their AI resource usage, ensuring optimal utilization and preventing waste (e.g. paying for idle GPUs).

More advanced AI gateways can direct the right type of AI computing job to the most economical infrastructure by applying context to each job. For example, the most critical jobs that require scale and throughput may be directed to the highest capacity GPU cluster, while simpler inference jobs can be directed closer to the end user but less powerful GPUs.

The flip side of optimizing a coin is observability and monitoring. The AI gateway manages the observability of the AI from one place and can even send data to third-party log/metric collectors. This makes it easier to capture the entire AI traffic generated to further ensure data compliance and identify any anomalies in use. Some of them overlap with security, but most of them are specific to AI, as the anomalies of signal issues vary depending on the consumption pattern of AI.

For example, AI inference to an application in production may look similar to normal application traffic, but AI model training and tuning looks very abrupt, with a lot of traffic and dependent computational work that needs to be closely monitored to ensure that GPUs aren't wasted waiting in an inefficient data pipeline.

2. Bring order to the AI's Wild West

What's even more confusing is that many point products focus on one or two problems that more comprehensive AI gateways seek to solve. Some vendors also package API gateways as some AI-specific features and name them AI gateways.

There are a few open source projects that offer some of the features discussed above. For example, many machine learning operating platforms and services create unified API endpoints for AI consumption by development teams.

Bundling many different products together to get all the features will end up being a hassle to overcome and will be more expensive. Just as API management is focused on API gateways, AI management will also exhibit a bias towards comprehensive AI gateways.

The best will provide an effective way for everyone exposed to this powerful new technological paradigm to tame the AI "Wild West".

The right AI gateway will pave the way for enterprises to adopt AI and make deploying this powerful new technology more routine, secure, and cost-effective at any scale.

To learn more about AIGC, please visit:

51CTO AI.x社区

https://www.51cto.com/aigc/

Source: 51CTO Technology Stack