What is an AI gateway?

An AI gateway is a middleware layer that sits between applications and large language model (LLM) providers, handling routing, authentication, token-based rate limiting, cost tracking, and prompt/response security for AI traffic.

Key aspects of an AI gateway:

• Multi-provider model routing and failover
• Token-level rate limiting and budget enforcement
• Semantic caching for similar prompts
• Prompt injection detection and PII masking
• Unified observability across LLM providers

Below, we explore how AI gateways differ from API gateways, where LLM traffic creates new governance risks, and what enterprises need beyond either gateway alone.

How is an AI gateway different from an API gateway?

An API gateway manages HTTP traffic between clients and backend services. An AI gateway manages LLM traffic between applications and model providers. The core difference is what each gateway understands.

API gateways work on requests. They authenticate callers, rate-limit by request count, route based on URL paths, and cache exact-match responses. They handle the cross-cutting concerns of REST and gRPC traffic: authentication, authorization, load balancing, and protocol transformation.

AI gateways work in tokens. They track how many tokens each request consumes, route prompts to the best model based on cost or latency, fail over to a different provider when one goes down, and cache responses based on semantic similarity, not exact string matches. According to Valuates Reports, the AI gateway market was valued at USD 3.9 billion in 2024 and is projected to reach USD 9.8 billion by 2031.

An API gateway can technically route LLM requests. It will authenticate them, rate-limit by request count, and forward them to OpenAI or Anthropic. But it cannot tell you that your data science team spent $847 on a specific model last week, or catch a prompt injection attempt before it reaches the model, or automatically switch providers during an outage.

What runtime capabilities does an API gateway provide?

An API gateway sits between API consumers and backend services. As requests pass through it, the gateway routes traffic, validates credentials, applies access and traffic policies, transforms requests and responses, and collects operational telemetry. It is one part of API management, not the entire API lifecycle.

Typical API gateway capabilities include:

• REST, gRPC, and GraphQL traffic handling. Depending on the gateway, this can include protocol-aware routing, pass-through proxying, and request transformation.
• Authentication and authorization enforcement. Validating API keys, access tokens, certificates, and authorization claims before forwarding requests.
• Request routing and load balancing. Directing traffic to the appropriate service, version, region, or healthy backend instance.
• Rate limiting and quotas. Controlling request volumes by consumer, application, API, route, or time period.
• Request and response transformation. Modifying headers, payloads, formats, or protocols as traffic passes between consumers and services.
• Backend service protection. Applying timeouts, traffic limits, circuit breakers, payload restrictions, and other runtime policies that help prevent misuse or overload.
• Runtime monitoring and logging. Recording request volumes, latency, errors, consumer activity, and policy outcomes for traffic passing through the gateway.

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AI applications still depend on these runtime controls. An AI agent that opens a support ticket, checks an account balance, or updates a CRM record is invoking APIs backed by enterprise services. The fact that the caller is an agent does not remove the need to authenticate the request, authorize the action, control traffic, route the call, and protect the backend.

AI gateways add runtime controls designed for LLM traffic, including model routing, token-based rate limits, semantic caching, prompt and response controls, and model-cost tracking. These capabilities may run in a separate AI gateway or be incorporated into an existing gateway product. The architectural requirement is not necessarily two separate gateways; it is appropriate runtime enforcement across both model traffic and the APIs and services that AI systems invoke.

What new API risks do AI agents introduce?

AI gateways solve model traffic problems. They do not solve the full API estate problem. Once agents start calling internal APIs via MCP, tool registries, or direct integration, new governance gaps emerge between the AI gateway and the API gateway.

Undocumented API consumption. When an agent discovers an internal API that no one cataloged, it can turn a dormant endpoint into a load-bearing dependency. The API might lack proper authentication, rate limiting, or ownership metadata. Only 10% of organizations fully document their APIs. Agents accelerate the shadow API problem by finding and consuming endpoints that were never surfaced to any governance process.

Hard-to-trace call paths. Traditional API governance tracks which consumers call which endpoints. A single user prompt can trigger a model call, which in turn triggers a tool call, which in turn triggers an internal API call. The full path from user intent to API consumption passes through multiple systems, and no single gateway or monitoring tool observes the entire chain.

Unauthorized API access through prompt injection. The OWASP Top 10 for LLM Applications lists prompt injection as the number one security risk for LLM applications. In an agentic context, a manipulated prompt can cause the model to invoke tools or APIs with permissions the user did not intend. This is distinct from normal multi-step agent workflows. The risk is that adversarial input bypasses the agent's intended scope and reaches APIs that should be off-limits.

Backend cost blind spots. AI gateways track token spend well. But the APIs that agents call underneath also consume compute, bandwidth, and database resources. Teams focused on AI gateway token dashboards may not notice when agent-driven API traffic starts affecting backend performance or cost. The two cost layers are tracked in different systems, and the connection between them is not always visible.

Ownership gaps. Every API needs an owner responsible for its security posture, uptime, and deprecation timeline. When agents become the primary consumer, the ownership question gets harder. Who owns an API that no human calls directly but an agent invokes thousands of times daily? Without clear ownership, security policies drift, documentation decays, and no one is accountable when the endpoint breaks.

Dangerous deprecation. When an API has human consumers, deprecation notices reach developers through portals, changelogs, and email. When an API has agent consumers, deprecation can break automated workflows without warning. Lifecycle governance needs to account for agent dependencies before an API is retired, not after.

These risks all sit between the AI gateway and the API gateway. Neither gateway alone has the context to catch them. The API estate still needs full lifecycle management: design, build, secure, observe, and distribute, regardless of how many AI gateways sit in front of it.

How do you manage AI and API traffic within one architecture?

AI gateways and API gateways are runtime components of API management. They apply different policies to different types of traffic: AI gateway capabilities handle model routing, token limits, prompt security, and AI costs, while API gateway capabilities handle authentication, routing, rate limits, transformation, and backend protection.

These capabilities may run in separate gateways or within the same gateway product. A federated API management approach allows those runtimes to remain distributed while a shared control plane coordinates inventory, ownership, lifecycle, governance, policies, and visibility across the entire environment.

A practical architecture has three parts:

• Shared control plane. Maintains a common inventory of APIs, models, tools, owners, environments, and lifecycle states. It also defines governance requirements and gives platform teams one place to understand the estate.
• Gateway layer. Applies the appropriate policies as traffic moves through the system. API-oriented policies protect calls to backend services, while AI-oriented policies govern requests to models. The control plane coordinates these policies, while the gateway runtimes enforce them.
• Models, tools, APIs, and backend services. These are the resources that applications and agents ultimately access. Connecting them to the same inventory and ownership model helps teams understand which business capabilities are being used and who is responsible for them.

For example, a user request may first pass through AI-oriented policies before reaching a model. If the model invokes a tool, the resulting API call may then pass through API-oriented policies before reaching a backend service. The shared control plane connects both parts of that interaction to the same ownership, lifecycle, policy, and operational context.

This federated model allows gateway products to remain distributed—or converge over time, without creating separate governance processes for every runtime.

Why does LLM traffic make API management more important?

LLM traffic does not stop at the model. AI agents use model capabilities to invoke tools, call APIs, access data, and perform actions. This creates longer and less predictable call paths across models, gateways, APIs, and backend services. Organizations must still know which APIs are being used, who owns them, what data they expose, and which agents depend on them.

Gateway runtimes can enforce policies at different points, but runtime enforcement alone does not provide a complete view of the API estate. LLM traffic therefore makes API management more important by increasing the need for shared inventory, ownership, lifecycle governance, and operational visibility. A federated API management control plane connects these capabilities across distributed API and AI gateway runtimes without creating another management silo.

How does APIwiz provide a unified control plane for AI and API traffic?

APIwiz provides a federated control plane for managing APIs across distributed gateway runtimes. It does not replace an API gateway or an AI gateway. Instead, it connects those runtimes through shared inventory, ownership, lifecycle governance, policy context, and operational visibility.

This allows platform teams to manage the APIs used by applications and agents without creating a separate management silo for LLM traffic.

Key capabilities that support this unified control plane include:

• Federated gateway management. APIwiz sits above multiple API gateways and AI gateways, providing a single control plane for inventory, policy, and observability regardless of which runtime gateway handles traffic.
• Zero-touch API discovery. APIwiz automatically scans cloud and on-premises environments to catalog every API, including shadow and zombie endpoints that agents might discover and call.
• Lifecycle state tracking. Every API carries its lifecycle status (design, build, deploy, deprecate, retire) through the platform. When an agent depends on an API nearing deprecation, the dependency is visible.
• Ownership and accountability. Every API has an assigned owner. When agents create new API consumption patterns, the ownership model ensures someone is accountable for security, uptime, and documentation.
• eBPF-powered observability. Kernel-level tracing captures every API transaction, including agent-driven calls that bypass traditional gateway logging. This provides end-to-end visibility across the full call chain.

Enterprise customers like Tonik use APIwiz to manage API estates that support partner ecosystems and digital banking products. Tonik achieved $1.5 million in API-enabled revenue and $3.5 million in OPEX savings by centralizing API governance across their entire stack.

Explore how APIwiz secures and governs APIs across any gateway.

Key takeaways

Modern enterprise stacks require gateway runtime capabilities for both traditional API traffic and AI traffic. These capabilities may operate in separate gateways or within the same gateway product, but they apply different policy sets to service requests and LLM interactions.

The rise of LLM traffic makes unified API management more important than ever. As AI agents invoke tools, access enterprise data, and call backend APIs, organizations need authentication, traffic controls, lifecycle governance, clear ownership, and operational visibility that extend beyond the model layer.

A federated API management control plane addresses this need by connecting distributed gateway runtimes through shared inventory, coordinated governance, and unified operational context.

Book a demo to see how APIwiz provides a unified control plane for traditional and AI-driven traffic.

FAQs about AI gateways

What is an AI gateway?

An AI gateway is middleware that centralizes how applications access LLM providers. It solves a specific operational problem: when multiple teams or services call different LLM providers, each with its own API keys, rate limits, and failover behavior, managing that complexity at the application level does not scale. The gateway provides one endpoint, handles provider routing and credentials, and enforces token-level budgets and security policies in one place.

Is an AI gateway the same as an API gateway?

No. An API gateway manages HTTP traffic between clients and backend services, handling authentication, routing, and rate limiting by request count. An AI gateway manages LLM traffic between applications and model providers, handling token-based rate limiting, semantic caching, model routing, and prompt security. They serve different traffic types and are complementary, not interchangeable.

Do AI applications still need API management?

Yes. AI agents call APIs to take actions: opening tickets, querying databases, updating records. These API calls still need authentication, authorization, rate limiting, lifecycle governance, and observability. An AI gateway governs the model side. API management governs the service side. Production AI systems need both.

What is an LLM gateway?

An LLM gateway is a narrower term for a gateway that focuses specifically on model-provider access: routing requests to the right LLM, managing API keys across providers, tracking token usage, and handling failover when a provider goes down. An AI gateway is broader and can also cover agent workflows, MCP server access, tool calls, and cost governance.

How do you monitor API calls made by AI agents?

Agent-driven API calls need monitoring at three levels: the AI gateway (which logs model calls and token usage), the API gateway (which logs service calls and latency), and a governance layer that traces the full chain from user prompt to model response to tool call to API response. Tools like eBPF-based observability can capture agent traffic at the kernel level, including calls that bypass traditional gateway logging.

Related reads

• API Observability in the Age of AI
• API Management Goes Beyond the API Gateway
• Your API Gateway Is Lying to You
• How to Improve API Adoption with a Platform-Based Approach
• The Hidden Cost of API Sprawl
• Why Your Organization Needs API Governance
• How APIwiz Solves API Observability
• How to Evolve APIs

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