Artificial Intelligence in the enterprise space is consolidating around a single point of control: the AI Gateway. This operational layer routes, monitors, and governs every enterprise application call made to language models, regardless of the provider.

Market data from Gartner, IBM, and Menlo Ventures point in the exact same direction from different angles: adoption, cost of risk, ROI, and regulatory compliance.

AI Enterprise: A Rapidly Growing Market

The shift from 25% to 70% adoption estimated by Gartner (Market Guide for AI Gateways) is not an isolated optimistic forecast: it aligns directly with global spending trajectories. Worldwide AI spending is set to surpass $2.5 trillion in 2026, nearly a 50% increase year-over-year, following a 2025 marked by hundreds of billions invested in GenAI alone. 

However, every euro of this flow represents application traffic that remains unmonitored without a centralized control point.

On the enterprise side, the signal is even clearer: this is where the direction of money, not just intentions, can be seen. According to Menlo Ventures (The State of Generative AI in the Enterprise), spending on GenAI solutions grew from under $2 billion in 2023 to nearly $37 billion in 2025: a jump of more than twenty times in two years. This spending is currently focused primarily on purchased solutions rather than internally developed ones: companies are buying AI infrastructure, not building it from scratch.

In this scenario, an AI Gateway represents the infrastructure that allows purchasing multiple providers without multiplying operational risk.

AI Gateway and Market Data: the Real Cost of Lacking a Control Point

Estimates regarding the absolute size of the AI Gateway market vary widely across sources, as it is a relatively recently established segment. Gartner evaluates it more conservatively than other analysts, whose estimates are up to twice as high.

The figure on which sources converge is the growth rate, estimated at around 15–20% per year (360iResearch, AI Gateway Market 2026–2032): a trend to mention, not the absolute value.

To complete the picture, Gartner predicts that by 2028, a significant share of companies using AI will adopt AI Observability tools, the discipline that monitors the accuracy, drift, and performance of models in production. This is the true pillar upon which the monitoring component of an AI Gateway is built, such as the one by Radicalbit.

Conversely, the part of the market that has not yet adopted an AI Gateway is paying a measurable, not merely theoretical, cost. IBM (in the Cost of a Data Breach 2025, based on roughly 600 companies analyzed by the Ponemon Institute) found that a high level of Shadow AI — employees using AI tools not approved by IT — adds nearly $700,000 to the average cost of a data breach, which globally hovers around $4.4 million. It is one of the factors that most amplifies breach costs.

The details are even more telling regarding the cause: almost all companies hit by an AI-related security incident lacked adequate access controls on their systems, and a large portion did not even have an AI Governance policy. About one in five breaches, according to a Nudge Security analysis of the same IBM report, was delivered specifically via Shadow AI tools. It is not a problem of poorly chosen models: it is a problem of ungoverned traffic.

The issue is that much of this usage is invisible to corporate channels by design, not due to carelessness. Cyberhaven research conducted on its customer base reveals that a significant portion of employee interactions with AI tools exposes sensitive data. Netskope, analyzing cloud telemetry between late 2024 and late 2025, found that nearly half of GenAI platform usage occurs through personal accounts that the company can neither see nor govern.

The conclusion to be drawn is obvious: either you provide your employees with secure, traceable access, or people will continue to use the “back door.”

Tracking AI Spend

Gartner had predicted that by late 2025, about one in three GenAI projects would be abandoned immediately after the proof of concept due to one of three reasons: rising costs, inadequate risk controls, and unclear business value.

The first front — costs — is also the most difficult to monitor using traditional tools. Only recently have most companies begun seriously tracking AI spend: cost visibility remains the most cited operational gap by FinOps teams because per-token costs, variable and distributed across multiple providers, escape cloud cost management tools designed for traditional infrastructure.

On this front, semantic caching — the technique that recognizes queries semantically equivalent to requests already served and returns the response from the cache instead of calling the model again — is one of the interventions backed by the strongest empirical evidence.

An AWS study on over 60,000 real chatbot queries measured a cost reduction close to 86% and a latency reduction near 88% on responses served by semantic caching, with accuracy remaining above 90%. Other independent studies confirm results in the same order of magnitude. These numbers apply to the share of traffic that is genuinely repetitive, not to total spending: a conservative estimate, cited by multiple sources, is a 60–80% reduction on repetitive requests, not a linear cut on the entire AI bill.

Compliance Requirements

For those reading this article today, compliance with the EU AI Act is no longer a future risk to plan for: the regulation’s penalty regime (EU Reg. 2024/1689, Art. 99) is fully operational as of August 2, 2026. Envisaged fines reach up to €35 million, or 7% of global turnover if higher, for prohibited practices, and remain significant even for less severe violations.

In this scenario, features natively offered by an AI Gateway, such as interaction audit trails, call traceability, and granular access control to models, represent the essential operational requirement to demonstrate compliance, far beyond a simple security measure.

For operators in the finance and critical infrastructure sectors in Italy and Europe, the regulatory framework is completed by the DORA and NIS2 regulations. While differing in form, these regulations share the same fundamental principle as the AI Act: enforcing full control and traceability over ICT suppliers and data flows, establishing that any interaction toward a third-party model must be adequately monitored.

How to Evaluate AI Gateway Adoption in your Company

In practice, those adopting an AI Gateway tend to go through a few “routine” steps that remain the same regardless of the chosen vendor. What changes is the tool, not the sequence:

• Map existing LLM entry points: before governing traffic, it is necessary to inventory which teams call which models, with which credentials, highlighting and eliminating a Shadow AI perimeter often broader than estimated.
• Define access policies and roles: who can use which models, with which input datasets, and with what spend limits per project or individual token caps.
• Enable semantic caching on high-repeat use cases: customer support, internal search, and document assistants are contexts where the rate of repeated queries makes caching most effective, reducing costs and latency.
• Instrument cost monitoring per team and project: replace retrospective reviews with real-time visibility into per-token spend and consumption tracking.
• Integrate audit trails for regulatory compliance: ensure traceability of interactions and access control fully aligned with the applicable requirements of the EU AI Act, DORA, and NIS2, rather than managing them as a post-hoc adaptation.
• Measure drift and response quality in production: connect the Gateway to observability tools to intercept hallucinations, performance decay, and semantic issues before they become an operational or compliance issue.

What an AI Gateway concretely does

If AI spending in your company is still only uncovered after the fact, or no one knows for sure which teams are calling which models, the starting point is mapping how much LLM traffic is already passing through without control.

An AI Gateway makes that traffic immediately visible, secure, and traceable. Specifically, it performs four functions that rarely coexist in a single tool prior to its adoption:

• Multi-model routing: routing between different providers based on cost, latency, or availability.
• Access governance: who can call which model, with what data, from which application.
• Granular financial monitoring: cost per team, per project, in real time rather than at the end of the month.
• Audit trails for traceability: to ensure the traceability required by regulators and enterprise clients.

The Radicalbit AI Gateway builds these four pillars around a specific concept: the boundary between observability and governance should not exist. The same platform that detects drift and performance degradation during monitoring is the one that applies access policies and measures actual spending per individual LLM call: two functions that elsewhere typically remain separate.

Through integrated mechanisms such as semantic caching, which answers common queries without querying the models again, and token caps, the solution reduces computational costs and ensures near-zero latency on recurring requests.

To explore the architecture in detail and learn about all the features of the Radicalbit AI Gateway, visit the dedicated page. If you would like to evaluate the actual impact on your infrastructure, requesting a personalized demo is the fastest way to measure the visibility and control achievable within your operational context.