AI Gateway: The Next Battle for AI Infrastructure

Companies are rolling out their first AI agents—and discovering how to govern the interactions among models, applications, and data.

A new category of software, AI Gateways, aspires to become the control point for these architectures. Like API Gateways did twenty years ago, this layer could redefine how IT leaders design their AI infrastructures—provided it lives up to its promises.

The history of information systems is punctuated by technological ruptures that gradually give rise to new layers of infrastructure.

In the early 1990s, the priority was to connect networks while protecting them: firewalls became the mandatory passage between the enterprise and the Internet.

In the early 2000s, the spread of service-oriented architectures, followed by microservices, created another necessity: controlling exchanges between applications. API Gateways emerged to authenticate calls, enforce security policies, and provide centralized visibility.

A decade later, the cloud explosion gave rise to modern IAM platforms, CASBs (Cloud Access Security Brokers), SASE solutions, and CNAPP platforms (Cloud-Native Application Protection Platform, tools for securing cloud applications) to meet new governance needs.

Generative AI seems to be following the same path. After an initial phase focused on selecting a high-performing language model, companies are realizing they must now manage an ecosystem where multiple models coexist, AI agents make decisions, and interactions with the information system become more numerous and dynamic.

From Single-Model to Multi-Model

Between 2023 and 2024, most experiments relied on a single supplier: an application called a model via an API, end of story. This architecture remains straightforward.

By 2026, the scaling shift is clear. An in-production usage analysis by Vercel found that the share of teams operating five models or more rose from 29% to 37% in a single year. A structural shift rather than a marginal adjustment.

An office assistant can now mobilize a proprietary model for text generation, an open-source model run locally for confidential processing, a specialized model for document analysis, and a multimodal engine for image or video.

This diversification follows three rationales:

• Economic: prices vary significantly from one provider to another, and the “race to the bottom” on token price pushes IT leaders to arbitrate rather than rely on a single actor. Several commercial gateways (Vercel, OpenRouter, Cloudflare, LiteLLM) have even removed token margins in 2026, shifting competition toward reliability and governance.
• Regulatory: with the phased entry into application of the AI Act, some companies want to retain control over sensitive processing or favor providers offering stronger guarantees about data locality.
• Operational reliability: incidents at major model providers are no longer rare. According to data cited by Vercel, the top LLM providers each logged around twenty incidents and nearly 180 hours of downtime in December 2025 alone. A concrete argument in favor of multi-provider setups and automatic failover.

This evolution reveals a new challenge: how to manage an environment where several models are used simultaneously by hundreds of applications, without multiplying API keys and ad hoc integrations?

AI Agents Change the Nature of the Information System

The widespread adoption of agents marks a second rupture.

A traditional chatbot answers a question. An agent can plan a sequence of actions: consult a knowledge base, query an ERP, create a ticket in an ITSM tool, send an email, and then hand the result to another agent. It no longer merely delivers an answer—it takes action.

For architecture teams, this changes the nature of the data flows. Interactions no longer occur only between known applications; they involve components capable of choosing which tools to use based on the context.

The Model Context Protocol (MCP), which standardizes how a model accesses external tools and structures much of these exchanges, is taking hold. Yet its rapid adoption creates a new exposure surface, since every MCP connector can become an entry point into an internal system.

In many companies, each application still talks directly to the model chosen by its development team. This approach works when projects remain few, but becomes difficult to maintain once dozens of applications use multiple providers. Each team must manage API keys, security policies, usage limits, logging, model version changes, and provider-specific compliance rules separately.

As the number of agents grows, so too does the number of interactions, with a well-known risk: the uncontrolled proliferation of point-to-point connections, something the history of information systems has repeated at every technological rupture.

A New Layer Emerges: the AI Gateway

Concretely, an AI Gateway sits as a proxy between applications and model providers (OpenAI, Anthropic, Google, Azure, self-hosted open-source models).

It exposes a single entry point and adds, depending on the vendor, features such as multi-model routing, automatic failover in case a provider goes down, a semantic cache to reduce costs and latency for similar requests, quotas and budgets by team or user, token-level logging, as well as security functions like personal data masking or detection of prompt injection attempts.

The market quickly structured itself in 2026, with differentiated positioning:

This diversity shows that the term “AI Gateway” still covers different realities depending on the editor: some are primarily cost routers, others governance platforms, others extensions of existing API Gateways.

What Analysts Say

Gartner published several works in 2025 and 2026 dedicated to this space (Market Guide for AI Gateways and Market Overview updated in 2026) positioning this layer as a component of future enterprise AI architectures. It is responsible for managing connections to AI services, applying security policies, distributing requests across multiple models, and improving cost visibility.

The firm broadly includes the AI Gateway within its AI TRiSM (Trust, Risk and Security Management) framework, estimating the market at around $3.1 billion in 2025, with projected annual growth on the order of 35% through 2030. This should be read as a rough analyst-level figure rather than a certainty, as forecasts are regularly revised as the market consolidates.

The NIST AI Risk Management Framework converges on the same core idea. Without using the term “AI Gateway,” it stresses the need for governance, traceability, and continuous monitoring throughout the lifecycle of AI systems. Functions that a gateway can technically support, without the framework prescribing this exact architecture.

The Cloud Security Alliance advocates a similar interpretation, arguing that AI governance can no longer be addressed solely at the application level.

What the AI Act Actually Makes Necessary

The regulatory argument deserves clarification because the timeline shifted in 2026.

The Digital Omnibus package, adopted definitively at the end of June 2026, pushed most of the high-risk obligations for Annex III systems (recruitment, credit, education, justice, biometrics) to December 2, 2027. The essence of the transparency obligations under Article 50—informing users that they are interacting with an AI and identifying AI-generated or AI-modified content—remains in force on August 2, 2026.

For systems that will remain under the high-risk regime, Article 12 requires automatic and tamper-evident logging of events, with a minimum retention period of six months (twenty-four months for biometric data), and Article 14 requires effective human oversight.

These are precisely the functions (centralized logging, per-call traceability, and human-in-the-loop controls) that an AI Gateway can industrialize across dozens of applications, rather than having each project team rebuild them. The regulation also notes—in Recitals 99 and 100—that in an AI agent chain, the obligation to comply extends to every agent executing a high-risk function. A strong argument for centralizing governance rather than dispersing it.

The postponement therefore does not change the fundamental logic: it merely removes the near-term urgency without negating the need to prepare an architecture capable of producing these compliance proofs.

Limitations of the Concept

This convergence of analyses should not obscure areas of uncertainty.

A market that is still young and unstable. The Portkey acquisition by Palo Alto Networks, the security incident affecting LiteLLM, and the large number of vendors listed (more than 160 in some directories) suggest rapid consolidation rather than a mature market. A CIO adopting a gateway today is betting on the long-term viability of the vendor.

A new single point of failure. Centralizing all AI traffic through one layer inherently creates a single point of failure: if the gateway goes down, all of the company’s AI applications stop, even if the underlying models are functioning.

Added latency, but typically marginal. Several independent comparisons estimate the overhead of a well-operated gateway at a few milliseconds to a few tens of milliseconds. This is negligible next to a model call lasting several seconds, except in cases of poor implementation.

A blurry boundary with the existing setup. Some functions claimed (authentication, quotas, logs) can also be handled by an existing API Gateway, which raises the question: should there be a dedicated brick or an extension of the existing API infrastructure? Solutions like Kong or Zuplo, which blend the two worlds, illustrate that the boundary between “API Gateway” and “AI Gateway” is not yet settled.

The risk of governance on the surface. A gateway logs and controls flows, but it does not in itself address the root causes of AI incidents in enterprises. Gartner estimates that a large majority of AI transactions that are not authorized stem from internal misuse rather than malicious attacks. This is more of an organizational issue than a technical one, which no single tool can fix.

What This Means for CIOs

The history of firewalls, API Gateways, and then CASBs shows that these infrastructure layers, once they establish themselves, become hard to remove.

For a CIO deploying multiple models today and beginning to experiment with agents, the question isn’t whether centralized governance will be necessary, but when to introduce it and how tightly to couple it to the existing infrastructure.

A sensible starting point is to map current and planned AI usages, assess whether extending the existing API Gateway suffices in the short term, and commit to a dedicated vendor only after evaluating its security posture, financial stability, and the degree of lock-in it would introduce.

In a market that consolidates as quickly as it grows, prudence in choosing a vendor matters at least as much as the decision to adopt the gateway itself.

Sources:
> Gartner (Market Guide for AI Gateways, 2025; Market Overview for AI Gateways, 2026; Market Guide for AI Trust, Risk and Security Management, 2025
> NIST AI Risk Management Framework 1.0
> Cloud Security Alliance
> Regulation (EU) 2024/1689 (AI Act)
> Digital Omnibus package adopted in late June 2026