If you’re reading this, there is a near 100% likelihood you have tried an AI application and are working in a job where you have experimented with AI applications. From GitHub Copilot to Microsoft Office Copilot to ChatGPT and others, AI has moved at light speed from “We’ll get there someday” to “What’s your AI strategy?”
As a result, organizations are rapidly embracing AI — creating enhanced end-user experiences, reduced operating costs and competitive advantages. Entire new classes of applications are emerging, built around AI processes and workflows. Like most new applications and services, AI services, such as those offered by OpenAI or various cloud providers, are delivered and consumed via API.
AI gateways are purpose-built systems to manage, secure and observe the surging flow of AI traffic and application demand. As such, they’re quickly becoming an important product category. So what is an AI gateway? And do you need one yet?
What Is an AI Gateway: A Quick Definition
An AI gateway is a specialized appliance or solution designed to manage and streamline the interactions between applications and AI models, particularly in the context of large language models (LLMs) and other AI services. The gateway acts as a central point of control for AI traffic, providing a unified interface for applications to access various AI backends and models. An AI gateway also allows operations and security teams to manage critical areas such as security, governance, observability and cost management.
Most AI gateways cover the following sets of functionalities:
Security and Compliance
AI security is both paramount and table stakes. AI applications might be used to process customer data or other forms of personal identifiable information, and are often exposed to valuable proprietary company data. Increasingly, third-party AI bots are attempting to train on publicly exposed data without seeking authorization.
The gateway handles both authentication and zero trust, serving as the gatekeeper for AI services and API access.
Against these and other risks, AI gateways are becoming something of a new type of firewall. AI gateways manage the security credentials for both consumers and providers of AI services.
The gateway handles both authentication and zero trust, serving as the gatekeeper for AI services and API access. It also provides an authorization layer to make sure that only approved users can access specific services or that services are approved to be consumed according to defined policies. Policies might restrict use based on geography, business unit, role, infrastructure provider or type of infrastructure.
For specific AI prompt management, AI gateways can implement prompt security, validation and template generation. This simplifies prompt management by consolidating capabilities in a single control plane that can be managed without requiring updates on local development environments or on different model systems or AI applications. This is essential for responsible and compliant AI usage, as it prevents developers from building AI integrations around restricted topics or setting the wrong context in the prompts.
In addition, AI gateways are used as the equivalent of a firewall or digital loss-protection system for AI data. A full-featured AI gateway can prevent model poisoning, model theft and other nascent cybersecurity threats to AI systems.
Load Balancing and Centralized Consumption Management
You might need an AI load balancer even if you don’t have one yet. AI applications can be highly data intensive and compute dependent. Not managing the flow of AI applications can mean that very expensive GPUs sit idle while they wait for an under-resourced upstream part of their pipeline to complete a job. For consumer-facing offerings, latency on AI apps is a killer — the longer you make someone wait for a chatbot response, the more likely they have swiped left or right.
Then there is the issue of consumption. Most organizations today are using multiple AI model-as-a-service offerings. Those are mostly delivered via their cloud provider or another third-party service. AI gateways provide a centralized platform for managing AI consumption across different teams and applications within an organization. This centralization is crucial for maintaining control over AI traffic and ensuring that AI is used in a compliant and responsible manner.
AI gateways provide a centralized platform for managing AI consumption across different teams and applications.
By offering a unified control plane and load balancer, AI gateways enable organizations to manage all AI consumption and observability collection. In AI, consumption is different because it is measured in tokens rather than transactions or volume of data.
However, simple measurement of tokens is imprecise: Some types of queries require more tokens to run a job and the number of tokens required for the same prompt may vary over time. In other words, imagine if your standard application returned a variable amount of data for the same request. This is core to the nuance of AI — consumption can be harder to predict and control.
Streamlining Developer Workflows
Developers and platform operations teams today confront a dizzying array of AI integrations and APIs to choose from. Cloud providers can streamline consumption via their APIs, but the AI gateway is designed to allow for easy curation of AI APIs and a single management point for integrations.
AI gateways support multiple AI services and provide a single API interface that developers can use to access any AI model they need. An endpoint might allow developers to access the various models offered by OpenAI, but also all the thousands of more finely tuned open source models and tools housed on Hugging Face. AI gateways can automate the onboarding of teams that need access to AI services.
Yes, AI sprawl is a thing, and you don’t want your developers messing with it.
This uniform API endpoint streamlines the development workflow and speeds up the integration process. That, in turn, allows developers to focus on building AI applications rather than managing complex integrations.
Just as developers want a palette of frameworks and open source modules to choose from in developing software, AI developers increasingly want a wide selection of models and AI services to allow them to customize applications more quickly and appropriately. Yes, AI sprawl is a thing, and you don’t want your developers messing with it.
Cost Optimization, Monitoring and Observability
AI gateways allow organizations to learn from their AI usage to manage and reduce costs. The gateway can provide insights into consumed quotas for each model, enabling efficient resource allocation and cost control. This transparency allows users to manage their AI resource usage effectively, ensuring optimal utilization and preventing waste (such as paying for idle GPUs).
More advanced AI gateways can direct the right types of AI compute jobs to the most economical infrastructure by applying context to each job. For example, the most critical jobs requiring massive scale and throughput might be directed to the highest capacity GPU clusters, while more simple inference jobs can be directed to GPUs that are closer to the end user but less powerful.
The other side of the optimization coin is observability and monitoring. AI gateways manage AI observability from one place and can even send data to third-party log/metrics collectors. This makes it easier to capture the entirety of AI traffic being generated to further ensure data compliance and identify any anomalies in usage. Some of this overlaps with security, but much of it is AI-specific because the consumption patterns of AI are different, and the anomalies that signal issues are also different.
For example, AI inferencing on an application in production might look similar to normal application traffic, but AI model training and tuning would look extremely bursty, with massive flows and dependent compute jobs that mandate close monitoring to ensure GPUs are not wasted waiting in an inefficient data pipeline.
Bringing Order to AI’s Wild, Wild West
To make matters slightly more confusing, many point products focus on one or two of the problems that more comprehensive AI gateways seek to solve. Some vendors are also wrapping API gateways with some AI-specific functionality and terming them AI gateways.
There are open source projects that deliver some of the capabilities discussed above. Numerous machine learning operations platforms and services create unified API endpoints for AI consumption by development teams, for example.
Stapling together a number of different products to gain all the functionality will ultimately become an insurmountable hassle and will be more expensive. Just as API management became centralized on API gateways, so too will AI management demonstrate bias toward comprehensive AI gateways.
The best ones will provide an effective way to tame the AI “Wild West” for everyone who touches this powerful new technology paradigm. The best AI gateways will smooth the path to enterprise AI adoption, and make deploying this powerful new technology more routine, safe and economical at any scale.
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It streamlines interactions between applications and AI models, and provides a way to manage security, governance, observability and cost management.