What Is an AI Gateway?

Unlike a standard API gateway that manages general web traffic, an AI gateway understands AI-specific concepts like prompts and semantic intent. For IT security teams and network architects, this level of centralization is foundational for AI security. AI adoption has outpaced most organizations' ability to govern it, and an AI gateway restores that control without creating bottlenecks for your teams.

The growth of AI workloads and the control gap

Zscaler's ThreatLabz 2026 AI Security Report analyzed nearly one trillion AI transactions across roughly 9,000 organizations. The number of applications generating AI traffic nearly quadrupled to more than 3,400, while transaction volume grew 83% year-over-year. Data transfers surged 93%, to 18,033 terabytes.

ThreatLabz red team testing revealed critical flaws in 100% of enterprise AI systems tested, with a median time to first critical failure of 16 minutes. Your organization likely carries this same exposure if AI traffic moves without a centralized gateway in place.

Why standard API gateways fall short

Traditional API gateways, such as popular tools like Kong or Apigee, were never equipped to grasp the nuances of generative AI. They do not recognize "prompt token" syntax, nor can they route requests across multiple models. Developers often compensate by embedding private API keys directly in client code; however, these create blind spots that your security team can't see or audit. 

The primary function of an AI gateway is to act as a reverse proxy to mitigate this issue. The AI gateway sits between your client application code and each model API it calls. To do so, your developers will need to replace one base URL in your client application with the gateway endpoint's base URL, providing unified access, failover capabilities, and unified observability across all providers.

AI gateway vs. API gateway: Key differences

While traditional API gateways excel at managing standard web traffic and RESTful endpoints, they lack the context needed to process generative AI workloads. An AI gateway fills this gap by understanding machine-learning concepts like tokens, prompts, and model-specific protocols.

An AI gateway provides a wide array of technical features as a control point. Intelligent routing and semantic caching are among the most important features and affect how fast your app runs, whether it functions reliably, and overall operating cost.

Intelligent routing, load balancing, and failover

If a model API goes down, your AI gateway will automatically route traffic from that service to an alternate location or backup service. The user will not even know there was a failure. Automatic failover is among the clearest reliability benefits an AI gateway offers.

Both load balancing and intelligent routing help keep your application available. When a model reaches its maximum requests-per-second threshold, the gateway directs excess traffic to another available endpoint rather than returning errors to users. This feature, known as load balancing, is especially critical during high-volume events, such as product launches or seasonal peaks, when a single saturated model API could take your entire AI application offline.

In addition to providing redundancy and scalability, the AI gateway provides a common interface for communicating with multiple providers. The common model API approach refers to sending a standardized request to the gateway. The gateway then converts that request into the specific form required by each provider (OpenAI, Anthropic, Cohere). Your developers write integration code once and then gain access to all providers supported by your organization.

AI gateway for agentic workflows and MCP

As AI deployments transition from single-turn chat interfaces to autonomous agentic workflows, the AI gateway serves as the critical security and orchestration anchor. It manages the rapid, multi-step loops of prompts and tool-calling actions that autonomous agents execute in real time. By integrating with the Model Context Protocol (MCP), the gateway controls how agents access external data sources and developer tools, ensuring that agent actions remain strictly within enterprise compliance boundaries.

Performance optimization through semantic caching

Semantic caching returns stored answers for prompts that mean the same thing, regardless of phrasing. For example, if the gateway recognizes that "summarize this contract" and "provide summary of this agreement" are essentially asking the same thing, it returns the cached answer for either prompt.

Semantic caching has two advantages. First, it speeds up response time by eliminating the need for model execution on previously requested data. Second, it decreases provider costs associated with tokens for processing each query. For organizations running high-volume enterprise applications, these cost savings can add up rapidly.

Feeding uninspected data to a public LLM endpoint comes with major compliance risk. As soon as a prompt leaves your network, you have no control over where that data ends up, and “we did not know” won’t hold up as an acceptable response to regulators.

Data loss prevention (DLP) and PII redaction

The magnitude of this risk is already evident. Enterprise data transferred to AI applications has increased by 93%, reaching 18,033 terabytes in 2025, according to the ThreatLabz 2026 AI Security Report.

ChatGPT alone triggered 410 million DLP policy violations. These violations involved attempts to upload Social Security numbers, source code, medical records, and other sensitive data through a single AI application. 

An AI gateway addresses this vulnerability by providing an active checkpoint on each prompt. The gateway scans every prompt for personally identifiable information (PII) and strips it before the request reaches the model provider. Additionally, the gateway blocks intellectual property uploads, including source code, financial data, and contract language pasted into a chat window. Your data governance policy will now be enforced in transit, not after an event has occurred.

Prompt injection defense and guardrails

Sensitive data is not the only target of attack traversing this layer. Malicious actors are increasingly targeting the model itself via prompt injection. Hidden instructions are embedded within seemingly innocuous text, which override the system’s instructions and cause the model to behave differently.

Your AI gateway sanitizes every prompt before it reaches the model. The gateway will then strip all forms of prompt injection patterns and flag suspicious instruction sequences. Additionally, the AI gateway will validate that the model's output matches expected behavior before sending it back to your user.

Inline input/output validation is critical because prompt injection attacks manifest at machine speed. One malicious prompt can compromise a model session in seconds. Manual reviews cannot possibly keep pace with these types of attacks. Continuous inline validation of inputs and outputs at the gateway is what keeps pace with machine-speed attacks.

The cost of scaling AI can escalate far higher and more unexpectedly than traditional infrastructure costs. If nobody puts limits in place, your one experimental project can rack up thousands of dollars in model spend overnight. An AI gateway gives you the financial guardrails to prevent that.

Token-based rate limiting and quota enforcement

There's a layer of ambiguity with AI expenditure that makes gateways even more useful. Consider the economics of conventional API gateways, which limit traffic by request count (a metric that doesn't translate to generative AI). Depending on the prompt and the model, one request can consume a handful of tokens or hundreds of thousands. Your AI gateway enforces a defined quota by applying rate limits based on actual token consumption.

This lends you far more accurate control over spend while allowing you to set monthly financial quotas per application or department. For instance, if your marketing team's AI agent project reaches its quota, the gateway can throttle or pause requests before the project turns into a runaway bill. You set the ceiling in advance. 

Unified cost attribution and L7 metrics

Your organization probably uses more than one model provider, and each one prices tokens differently. Your AI gateway calculates spend across every provider in one dashboard so you always know your total AI cost without reconciling separate billing platforms.

The gateway also collects Layer 7 metrics on every request, which include:

• Time to first token (TTFT) tells you how responsive a model feels to your users
• Total tokens processed tells you exactly what you're paying for
• Error rates per model tell you which provider is underperforming and costing you retries

Metrics like these help you gauge your AI investment as a measurable line item. The visibility makes it possible to charge back AI costs to the business units that are generating them. Your finance team will have the same level of accountability for the costs generated by your department leaders as it already does for cloud infrastructure costs. And that combination is what will make your AI gateway a true FinOps control point and a security layer.

Your gateway deployment approach dictates how much control you retain and how much operational work is required. While there's no universally accepted answer, there are trade-offs between options.

Self-hosted versus managed implementations

Open source, self-hosted gateways like LiteLLM or open source Kong provide full ownership of your data path. Every prompt and response stays within the infrastructure you control, which is crucial if your industry’s regulatory environment demands strict data residency. But that level of control comes at a cost. Your DevOps team now owns patching, scaling, and uptime for the gateway itself, on top of everything else on their plate.

Fully managed solutions, such as Azure API Management or Cloudflare AI Gateway, shift the operational burden onto the provider. You get a proven gateway more quickly and with less internal maintenance. However, the trade-off is relinquishing control over exactly where your data transits and how quickly you can configure custom enforcement logic. Your decision between self-hosted and managed implementations should weigh your compliance requirements against your team's capacity to run additional infrastructure.

Secure network architecture and zero trust integration

However you deploy your gateway, integrate it with your zero trust network access (ZTNA) deployment, rather than implementing it in isolation. The ZTNA integration will need to be defined by core guiding principles for securing the AI gateway.

• Close all incoming listener ports on the gateway: A secure AI gateway does not accept open inbound connections. Instead, it uses cryptographic client identity proofs so that each connection establishes endpoint identity before communicating.
• Define all possible network pathways available for application access via strict allow lists: Design your entire environment so all outgoing queries from your AI must go through your governed gateway proxy, without exception or side channel.

At Zscaler, we build these same protections directly into the Zscaler Zero Trust Exchange™ platform, and the Zscaler AI Guard inspects every prompt and response inline, effectively blocking attack efforts like prompt injections, jailbreaks, and policy violations before they reach a model. 

• Zscaler AI Protect adds asset management, so you see every sanctioned and shadow AI app running on your network, backed by secure access controls and prompt-level data loss prevention.
• Zscaler AI Access Graph broadens visibility further. It maps how identities, applications, and data connect across your AI pipeline in real time, giving your security and FinOps teams the same centralized checkpoint a dedicated AI gateway provides.
• Zscaler treats AI traffic the same way it treats every other connection on your network. Nothing gets inspected out of band, and nothing bypasses policy. That consistency lets your company adopt AI at full speed while keeping compliance and security under one domain. Learn more about AI security with Zscaler.