Table of Contents

What Is an AI Gateway?

3 min. read

Table of Contents

An AI gateway is the centralized control plane for security and AI governance across the enterprise. It sits between AI consumers, agents, copilots, LLM-powered applications, and AI pipelines and the models, tools, and services they interact with. Every AI operation flows through it: model calls, tool invocations, and agent actions.

Key Points

Discover AI Usage: Know exactly what AI is running across your enterprise, who's using it, and what it's costing you, down to every team, model, and agent.
Govern AI Interactions: Set the rules for what AI can access and do. Every interaction is inspected and filtered in real time, blocking data leakage, prompt attacks, and unsafe outputs before they land.
Secure Every Agent: Give every agent a verified identity and control what it can access before it touches your data, tools, or systems.

Why AI Gateways are Important for Enterprise Security

AI has moved from answering questions to taking action. The systems driving this shift, i.e., coding agents, enterprise agents, and copilots, now represent the bulk of AI activity in the enterprise.

Coding Agents

Over 90% of enterprise developers now use AI coding assistants daily, making it the #1 use case for AI today. Tools like Cursor, Claude Code, and GitHub Copilot are deployed to engineering teams with access to MCP servers, code repositories, build systems, internal APIs, and package registries.

These agents generate code, install dependencies, execute commands, and push changes, often with blanket approval from the developer who initially onboards the tool and never reviews its actions at each step. The supply chain runs through public marketplaces and MCP registries that have not been vetted by any security team.

Enterprise Agents

By autonomously managing multi-step tasks within corporate software, enterprise agents operate without human intervention. They leverage persistent memory and connect to MCP servers to orchestrate cross-platform operations, such as extracting data from Salesforce, modifying Jira tickets, and sending outgoing emails. While traditional security reviews inspect each tool independently, they often overlook the risks introduced when an agent seamlessly coordinates and acts across all of them.

Copilots

Microsoft 365 Copilot, Salesforce Einstein, Adobe Firefly, ServiceNow Now Assist— these copilots are now embedded across the enterprise SaaS stack. They operate with a human in the loop, but every interaction processes sensitive enterprise data, emails, documents, customer records, financials, and routes it to external models for inference.

The infrastructure supporting these systems was never designed for AI workloads. Without a central control plane, these problems compound as adoption scales.

Shadow AI Usage

No Visibility into AI Activity and Costs

Token-based pricing makes AI costs inherently unpredictable. Request costs vary with input length, output length, and model choice. Agents worsen this: multi-step reasoning, retries, and tool-calling loops can turn a single user interaction into dozens of model calls. AI has become the fastest-growing line item that nobody can explain to the board.

No Shared AI Infrastructure

The absence of a unified framework leads to fragmented adoption of AI. When twenty different teams each build independent stacks for cost monitoring, logging, retry logic, and provider integrations, the result is a dozen redundant versions of the same infrastructure. This creates a lack of visibility and consistency across the organization.

Unmanaged AI Interactions

Sensitive Data Leaving the Perimeter

Every request to an external model is data crossing the boundary. PII, proprietary code, internal documents, customer conversations, all flowing to third-party providers without inspection or redaction. And with agents now invoking tools and taking actions autonomously, the attack surface is bigger.

Provider Outages

Directly integrating with a single AI provider means that when they face an outage, your AI-powered features go down instantly. With no built-in fallbacks or graceful degradation, it results in complete downtime, leaving you to explain in an incident post-mortem why a third party's reliability issue became your customer's problem.

Zero Audit Trail

Regulatory scrutiny has shifted from whether enterprises are adopting AI to how they govern it, with strict demands for verifiable evidence. Compliance frameworks like the EU AI Act, sector-specific mandates in finance and healthcare, and internal audit protocols now require a granular paper trail.

This includes tracking exactly what data was sent to which model, the resulting outputs, subsequent decisions, and the authorizing entities. Currently, most organizations cannot answer these fundamental questions.

Unsanctioned Agents

Agent Sprawl

The rapid expansion of enterprise agents has created significant governance challenges. Currently, organizations lack reliable methods to verify agent identities or restrict system access to authorized applications. Without a defined security boundary, connections to internal systems via MCP remain unmonitored, leaving enterprises vulnerable when issues arise.

Unregulated Agent

All agents have the same unrestricted access to every model, MCP server, and internal system. There's no way to enforce who can use what. So, when something goes wrong, there's no trail and no boundary that could have prevented it.

How an AI Gateway Works

The gateway intercepts every AI request before it reaches a provider and every response before it reaches an application. This is where policy meets execution.

The following is the request lifecycle:

Request initiation: The application, agent, or orchestrator sends a request to the gateway through a universal API. One endpoint, regardless of which model or provider is called.
Input validation: Before anything reaches an external model, the gateway inspects the request. Is there PII that needs redaction? Does this violate an org-level policy? Is this a prompt injection attempt? Requests that fail checks are blocked or flagged.
Governance and routing: The gateway determines where to send the request based on the configured routing strategy, within permitted models and limits.
Provider execution: The request is transformed to the provider's format, sent, and the response is normalized back to a standard format. The application never deals with provider-specific quirks.
Output validation: The response is validated against safety policies, content filters, and schema requirements before being returned.
Logging and attribution: Every interaction is recorded: tokens consumed, cost incurred, latency, which user or team triggered it, and which guardrails fired. This powers both real-time dashboards and long-term audit trails.

A gateway is split into two planes:

Data plane: Runs within the infrastructure. This is where requests are processed. Sensitive data never leaves the network perimeter.
Control plane: Manages configuration, policies, and analytics. Can be SaaS-hosted, deployed on private cloud, or fully air-gapped, depending on the compliance requirements.

The data plane syncs policies from the control plane and operates independently at runtime even if connectivity to the control plane is interrupted, the AI operations continue unaffected.

How does the AI gateway help?

An AI gateway simplifies the various stages of the AI transformation journey for the multiple stakeholders involved across an organization.

Deploying and Securing AI Coding Tools (CTO / Platform Engineering)

For large-scale AI coding assistant deployments, an AI gateway provides the control layer needed to move from ad hoc adoption to managed enterprise use. It helps teams govern model access, manage usage and budgets, protect sensitive code and context, and give leaders clear visibility into adoption, risk, and impact.

Making Production AI Agents Reliable and Safe (CTO)

When organizations deploy customer-facing AI agents at scale, an AI gateway helps ensure those agents remain reliable, safe, and controlled. It supports provider failover to reduce outage risk, applies output guardrails before responses reach customers, and enforces policies for tool access, actions, and session-level spending. Instead of managing controls agent by agent, teams can apply consistent governance across the entire agent environment.

Centralized Agent Governance (CISO)

Enterprises often struggle with agent sprawl, managing a mix of in-house, SaaS, and experimental prototypes. An AI gateway addresses this by providing a unified registry to track every agent's identity, access levels, and real-time activities.

This centralized control plane allows organizations to set granular permissions for specific teams and tools, while ensuring every invocation is logged for auditability. Furthermore, administrators can push instant policy updates across the entire agent fleet without the need for redeployment.

Further reading: Securing and Governing AI Agents at Scale Through A Unified AI Gateway

Proving Compliance to Regulators and Auditors (CISO / Legal)

In healthcare, finance, and legal, every AI interaction is a compliance event. Patient data can't reach external models unredacted. Financial advice generated by AI needs a full audit trail. The gateway is the enforcement layer: PII is redacted before requests leave the perimeter, every interaction is logged for regulators, and org-level policies apply uniformly regardless of which team is making the call.

Gaining Visibility Into AI Operations (CTO)

By logging every enterprise AI interaction, the gateway provides comprehensive attribution across teams, models, agents, costs, and performance metrics. Real-time dashboards offer deep visibility into cost trends, usage patterns, and system performance, enabling teams to perform end-to-end tracing whenever issues occur. This creates a unified source of truth, allowing leadership to seamlessly access reports on AI adoption and impact without requiring manual, cross-team data consolidation.

Bringing AI Spend Under Governance (CIO)

The gateway provides full cost attribution by team, model, application, and use case, allowing organizations to know exactly where AI spend is going. Teams set budgets per team or project, and they are enforced automatically before overruns happen. Optimization happens at the infrastructure level: repeated queries are served from cache, simple tasks route to cheaper models, and expensive calls only go where they're needed.

AI Gateway vs. API Gateway

An AI gateway isn't an API gateway with a plugin. It's purpose-built for traffic that is streaming, token-priced, non-deterministic, and requires content-level inspection. Organizations that try to retrofit API gateways for AI workloads end up rebuilding the logic themselves, which brings them right back to the fragmentation problem.

	API Gateway	AI Gateway
Designed for	Deterministic, stateless microservices	Non-deterministic AI workloads (models, agents, tools)
Connection model	Short-lived request-response	Long-lived, streaming (SSE)
Pricing model	Request or compute-based	Token-based (varies per request based on input/output length)
Routing logic	URL paths, headers, service discovery	Model capability, cost per token, latency, provider health
Security model	HTTP-level (auth, payload schema, IP rules)	Content-level (prompt injection, PII detection, output safety)
Observability	Request count, latency, error rates	Token usage, cost attribution, model performance, guardrail verdicts
Failure handling	Circuit breakers, retries to the same service	Cross-provider failover, model-aware fallback chains

AI Gateway vs. LLM Gateway

An LLM gateway governs model API calls. It handles routing, retries, cost tracking, and caching for the interactions between applications and language model providers. For a straightforward setup where an application calls a model and returns the response, an LLM gateway works.

Modern AI systems are no longer that simple; they function as agents that reason across multiple steps, invoke tools through MCP, access databases, execute code, send messages, and make decisions that trigger real actions.

An AI gateway is the evolution of the LLM gateway, governing the entire lifecycle of an AI operation rather than isolated API calls.

AI Gateway vs. MCP Gateway

An MCP gateway governs access to tools and resources for AI agents using the Model Context Protocol. It manages authentication, permissions, and observability for the connections between agents and the external tools they invoke: databases, APIs, code execution environments, and internal systems. It answers the question: "Which agent is allowed to access which tool, and what did it do?"

That's a critical layer, but it's only one dimension of the problem. An MCP gateway has no visibility into the model calls that triggered the tool invocation. It doesn't manage routing across providers. It doesn't enforce cost budgets. It doesn't validate model outputs for safety or compliance. It governs the tool side, but leaves the model side and the overall agent behavior ungoverned.

An AI gateway serves as a unified layer, consolidating the functionalities of:

an LLM gateway (handling model routing, cost management, and reliability)
an MCP gateway (managing tool access, permissions, and logging)
an agent gateway (governing agent-level behavior, boundaries, and multi-step orchestration).

Rather than deploying three separate tools to manage distinct phases of an AI interaction, an AI gateway integrates them into a single, continuous workflow governed by a centralized control plane.

AI Gateway to Enable Enterprise AI

As enterprise AI adoption expands from copilots and AI applications to autonomous agents, the AI security gap has significantly widened. These agents act as highly privileged insiders, executing a large volume of automated decisions across internal and external systems.

Teams need a centralized control plane to manage and protect autonomous AI agents, with the low latency required for agent-to-agent communication. Its architecture ensures that AI governance never comes at the expense of developer speed, allowing enterprises to accelerate AI innovation with confidence.

Today, the critical challenge for enterprises is no longer about adopting AI, but whether their infrastructure can successfully govern these technologies at the rapid pace that development teams expect to deploy them.

AI Gateway FAQs

An API gateway is designed for deterministic, stateless microservices using HTTP request-response patterns. An AI gateway is purpose-built for non-deterministic AI workloads, handling streaming connections, token-based pricing, content-level security (prompt injection, PII detection), and cross-provider failover that API gateways cannot support.

Yes. Even with a single provider, an AI gateway provides critical capabilities: data loss prevention (PII redaction), cost governance, audit logging for compliance, output validation, and protection against prompt injection, none of which single-provider integration offers out of the box.

An AI gateway verifies the identity of every agent, enforces per-agent permissions on which tools and data they can access, logs all tool invocations and actions, and applies runtime guardrails on agent behavior, ensuring only approved, authorized agents operate within the enterprise.

Modern AI gateways are designed for low-latency, high-throughput operation. The data plane runs within the infrastructure (VPC or Kubernetes cluster), minimizing network hops. The governance and security checks add negligible overhead compared to model inference time.

AI gateways support compliance with the EU AI Act, HIPAA, SOX, and sector-specific mandates in healthcare, finance, and legal. They provide full audit trails of every AI interaction, what was sent, what was returned, what decisions were made, and who authorized them, which regulators increasingly demand.