What is an AI gateway?

Whether AI services are proprietary tools built in-house or deployed as third-party models accessed through the cloud, gateways provide a unified, lightweight layer that connects applications and AI models and enforces governance and security policies consistently across all AI tools in the ecosystem.

While traditional application programming interfaces (API) gateways enable data exchange between clients and backend services, AI gateways are engineered to address the unique challenges of AI workloads. They extend the capabilities of standard API gateways to include multi-model access and integration, intelligent AI workload routing, dynamic load balancing, token consumption tracking and rate limiting, security policy enforcement and more.

Enterprise AI workloads can, for example, require sophisticated AI infrastructures capable of supporting massive computational loads, especially for deep learning and large model training. Existing enterprise systems can struggle to provide the high bandwidth and low-latency access businesses need to manage production-scale AI models.

AI gateways help development teams more easily manage complex AI-driven architectures. They provide a unified entry point for all AI model interactions, using AI-based APIs to orchestrate the flow of data, instructions and policies between applications and AI systems. This feature enables teams to control how different models and AI workflows are used and accessed from a single pane of glass, instead of relying on a separate interface for each model.

As such, AI gateways can help streamline access to AI model ecosystems. They help reduce the friction that can accompany model integration, and create a centralized governance structure for enterprise-scale AI adoption.

AI gateways act as bridges between AI systems and end-user applications, centralizing the deployment and governance of AI models.

Imagine a customer support tool on an e-commerce platform. The tool uses a large language model (to respond to user queries), a sentiment analysis model (to determine users’ moods) and an image recognition model (to analyze any photo attachments users send during interactions). An API gateway would sit between the models and the platform to orchestrate and streamline backend task completion.

For example, when a user submits a purchase query with screenshot as proof of a purchase, the application forwards the message and photo to the AI gateway’s endpoint. The gateway will route the text portion to the LLM and the screenshot to the image recognition model for a response. It also sends the message to the sentiment analysis model to determine whether the user seems frustrated or angry.

Along the way, the AI gateway helps ensure that all requests are authenticated and that no sensitive or private data is revealed. Ultimately, the gateway merges the results from each model in a standardized format before the results are returned to the client.

AI gateways act as bridges between AI systems and applications, centralizing the governance of AI models and helping teams eliminate fragmented, inconsistent guardrail enforcement. To provide these features, however, AI gateways must perform a series of key functions.

They include:

AI gateways impose a unified, canonical API format to enable seamless integration between multiple AI models and the applications that use them. Essentially, gateways help simplify the integration of diverse models from various AI providers. Canonical definitions enable AI APIs to map to multiple vendors, so applications always work with a consistent API surface, regardless of which AI model or tool is deployed.

AI gateways create a central control plane that fields incoming application requests, automates protocol conversions and masks differences between model providers’ APIs so that developers don’t have to reformat queries manually. They centralize access controls, observability and compliance protocols, usage tracking and other model management practices.

Model management and orchestration refers to the systematic monitoring, coordination and deployment of multiple AI models coexisting in the same environment. These processes—which include end-to-end lifecycle management (including tasks such as versioning, deployment, rollback and updates), resource allocation, error management and scaling, among others—help ensure that models work together seamlessly as part of a unified AI system.

Gateways facilitate the smooth delivery and operation of AI models, so developers don’t have to worry about manual deployments or outdated models. AI gateways also serve as central access points that route data requests, manage authentication and enforce policies across models, data sources and applications.

For instance, gateways enable dynamic model selection wherein the gateway automatically selects and routes AI requests to the best model based on the use case or system conditions.

AI gateways continuously track the performance, usage and health of AI models and the AI-related traffic they handle, enabling real-time visibility. Gateways monitor metrics, such as request volume, response times, error rates and cost accumulation at granular levels (per user or per application, for example).

Because they serve as AI traffic hubs, gateways can unify monitoring across multiple AI models and services, providing a holistic view of system performance in a centralized location (often, a dashboard). They also help developers maintain detailed logs of each AI request and response—including input prompts, model outputs, duration and token usage counts—for faster troubleshooting, more thorough compliance audits and stronger accountability measures.

What’s more, AI gateways can integrate with observability tools (such as OpenTelemetry) and security orchestration, automation and response platforms to automate alerting and incident detection workflows when problems occur.  

Data integration involves extracting, transforming and loading data from a range of data sources (such as databases, cloud platforms, applications and other systems) into centralized data warehouses or lakes to standardize formats and remove silos.

With AI gateways, developers can connect data sources and merge them into unified pipelines for predictive analytics and business intelligence. Gateways make it possible to prepare and feed both structured and unstructured data into AI models, preprocessing incoming requests and normalizing data formats for more accurate model training and inference generation.

They can also use machine learning (ML) capabilities to optimize data flow, detect anomalies and adapt the pipeline to changing data patterns.

AI tools can create considerable security and compliance risks. AI gateways help counteract those risks by providing centralized access controls and automated security policies for all data traffic between users, applications and AI models.

Using tools such as API keys, AI gateways tightly manage who can access what data or AI model by restricting access based on user profiles and network activity; and all AI-related traffic must pass through the gateway.

They enforce strong encryption protocols for data both in transit and at rest, minimizing the risk of unauthorized access and misuse. AI gateways also monitor network activity in real time, using features such as deep packet inspection and anomaly detection to identify and block malicious activity.

AI gateways also comprise several functions that help businesses maintain compliance with regulatory standards. Gateways can scrub personally identifiable information (PII) and confidential data before it reaches models or leaves the organization. And with rule-based filtering and content evaluation, gateways help ensure that only appropriate data is processed by AI models.

Inference in AI and ML is the ability of trained AI models to recognize patterns and draw conclusions from information that they haven’t seen before. Serving is the process of deploying trained AI models and exposing them (using AI APIs and other interfaces), so they can process requests for inference in a production environment.

AI gateways use model-aware routing to direct inference requests to the appropriate model instance. This capability enables both real-time and batch inference and helps models prioritize tasks based on criticality.

To facilitate scalable serving, gateways offer customizable load balancing tailored to AI workloads, which can be especially useful for latency-sensitive or high-throughput applications. They also handle incremental rollouts of new model versions, mapping fine-tuned models to underlying services for easier updates and rollbacks.

These features help developers provide low-latency, reliable AI outputs for a litany of app functions, from chatbots to decision support.

API gateways and AI gateways are both middleware layers that manage traffic between clients and backend services, but they differ significantly in their purpose, capabilities and the types of workloads they handle.

Traditional API gateways are management tools that serve as a single entry point for managing and securing traditional API traffic. They enable vital cross-cutting capabilities such as traffic management, logging, security enforcement and versioning, making APIs easier to manage and scale.

API gateways route data requests and handle all authentication, authorization, rate limiting, caching, load balancing, prompt management and basic security processes for standard web or microservice APIs. They also abstract away service integration responsibilities, so developers can expose APIs and microservices without having to manage the underlying network or security infrastructure.

AI gateways are, essentially, specialized API gateways for AI models and services. They manage AI request flows and orchestrate AI service interactions (such as request retries and model fallbacks). They provide a control layer designed specifically for AI workloads and interactions with LLMs, generative AI (gen AI), AI agents and other AI systems.

Beyond basic routing and security functions, AI gateways offer advanced features—such as semantic inspection of prompts and responses, multimodal traffic handling (text, voice, images), dynamic policy adjustments and cost management services, and data masking (for privacy compliance).

Many modern computing environments use both API and AI gateways. However, unlike API gateways, AI gateways are purpose-built to address the unique data management, security, observability and cost control needs of AI-driven applications, workflows and environments.

Deployment models refer to the various ways AI gateways manage AI models and services across different infrastructure setups. They affect where AI gateways run and how they handle traffic routing, security, scaling and governance for AI workloads.

Examples of deployment models include:

Relying on AI tools and services comes with some significant risks.

AI tools rely heavily on APIs to access data from external sources, deploy workflows, and interact with applications and services. And each API integration presents a potential entry point for attackers. Because they don't always follow predictable API usage patterns, AI-based functions can inadvertently expose proprietary or sensitive data and significantly expand the attack surface.

In fact, a single compromised or misconfigured API endpoint can grant access to multiple backend systems and sensitive datasets, enabling cybercriminals to move laterally within the architecture and escalate their privileges. 

Furthermore, most AI tools run on LLMs (OpenAI’s GPT models or Anthropic’s Claude models, for example), so they can inherit vulnerabilities from the LLM provider. If an attacker embeds malicious instructions into prompts or trusted data sources (such as configuration files, documentation or support tickets), the tool might unknowingly execute harmful actions when it processes the prompt.

AI gateways help development teams address these risks and challenges. They enable:

• Simplified AI traffic management. Centralized AI traffic management reduces the complexity of handling individual AI model connections, simplifying data routing, policy enforcement and usage monitoring.

• Improved efficiency and scalability. By automating resource management, load balancing and performance optimization processes, AI gateways can minimize downtime and accelerate the deployment and scaling of AI-based applications.

• Enhanced security. AI gateways implement robust security features—such as credential management and role-based access control (RBAC)—to protect data, increase visibility and ensure responsible AI usage. They provide a cohesive monitoring, auditing, anomaly detection and traceability apparatus wherein AI model usage can be tracked until the model is decommissioned.

• Faster innovation. AI gateways use ML to learn from new tasks and policies, enabling them to adapt to new environments and evolve over time. They also provide unified access to diverse AI services. This access helps developers innovate and deploy new AI apps faster.

• DevOps integration. AI gateways often integrate with continuous integration/continuous delivery (CI/CD) pipelines, providing detailed telemetry data that helps DevOps teams automate software rollbacks and remediation workflows. Gateways also automatically distribute traffic across AI model instances so that models can handle dynamic workloads without creating scaling delays.

AI gateways are themselves a newer technology and developers are finding new ways to maximize their effectiveness.

For instance, to support latency-sensitive and data-localized workloads (like those used for autonomous vehicles and healthcare devices), developers are increasingly choosing to deploy AI gateways at the edge of the network. Edge deployments rely on edge-optimized and lightweight AI tools that enable local inference generation, helping teams offload cloud services to edge servers while maintaining system responsiveness.

Semantic caching is improving AI gateways by reducing latency, cutting costs and scaling capacity in LLM-powered applications. Unlike traditional caching, which only reuses exact previous responses, semantic caching tools use embedded vectors to understand the meaning behind queries. Embedded vectors help AI gateways recognize and reuse responses for semantically similar questions (even if they’re phrased differently), helping them avoid redundant calls to LLM APIs and deliver faster responses.

Model failover is also helping teams maximize the benefits of AI gateways. Model failover configurations create redundancy so that, even if one model is down or running slowly, the gateway can continue to effectively route AI requests.

If the primary AI model becomes unavailable or returns errors, the AI gateway can use failover mechanisms to automatically switch traffic to a backup or secondary model. This process helps ensure that an issue with one model doesn’t disrupt the end user experience.  

With retrieval-augmented generation (RAG), AI gateways provide an orchestration layer that helps connect LLMs to current, external information sources. Instead of relying solely on the LLM’s fixed training data, RAG enables the model to first retrieve relevant context from external knowledge bases, documents and databases, and then augment the LLM prompt with this data before generating a response. As such, RAG-enabled AI gateways help models bridge the gap between static training data and dynamic knowledge and generate more accurate and relevant responses.

Furthermore, AI gateways can help mitigate the risks associated with deploying agentic AI tools.

AI agents use LLMs, natural language processing (NLP) and ML to autonomously design their workflows, perform tasks and execute processes on behalf of users and other systems. They enable human-in-the-loop development practices, where agents work alongside DevOps engineers and teams to help human beings achieve goals faster. However, agentic AI can also contribute to “shadow AI,” through unsanctioned and potentially harmful actions on the part of the agent, and significantly expand the attack surface for cybercriminals.

AI gateways can enforce security protocols, data privacy restrictions and regulatory compliance across complex, distributed deployments, and they help control API access, authentication and authorization processes for AI agents. And because AI gateways make agentic AI more observable, they also help businesses mitigate the shadow AI issues and runaway costs that agentic AI deployment can create.