What is the difference between an AI gateway and an LLM gateway?

The terms are used interchangeably, and both refer to a component that sits between callers and model APIs. The meaningful split is not in the name but in the job: some gateways center on routing, caching, and cost control, while others center on identity, policy enforcement, and audit. When you read either term, look past the label to which of those two problems the product is built to solve, because that determines whether it fits a governance need or a cost need.

Can one AI gateway do both routing and enforcement?

Some products span both to a degree, but the design centers of gravity differ, and depth on one side usually means shallower coverage on the other. A routing-first tool may add basic allow and block rules; an enforcement-first tool may add basic provider failover. For a governed environment, weight the evaluation on identity binding, prompt-level policy, and the audit record, and treat routing features as useful extras rather than the deciding factor.

How do we evaluate an AI gateway for regulatory compliance?

Map the gateway's audit record against the questions your regulation asks. If the regime wants to know who was involved, what data was processed, and what governed the decision, check that a single record names the identity, classifies the data, and carries the policy version, and that it cannot be altered by the application it audits. A gateway that produces evidence at that resolution supports compliance; one that logs shared-key access events does not, regardless of certifications on the marketing page.

Does an AI gateway replace our data-loss prevention tools?

It covers a gap most DLP tools miss rather than replacing them wholesale. Traditional DLP watches email, endpoints, and file movement and rarely inspects the prompt inside a model API call. An enforcement AI gateway terminates that call and acts on its content, so it governs the outbound AI path specifically. Many organizations run both, with the gateway owning model traffic and DLP owning the channels it was built for.

Should the AI gateway run in our own environment or as a vendor service?

For sensitive prompts, running the enforcement point inside your own trust boundary keeps the data where it originates and avoids adding a third party to the path the prompt travels. A fully external service that inspects your prompts has relocated the exposure rather than removed it. Ask where request contents are processed and whether they are retained, and let that answer weigh as heavily as any feature when the prompts in question carry regulated data.

How to Choose an AI Gateway: The Security and Governance Criteria That Matter

The term "AI gateway" covers two products that solve different problems. One camp sits in front of models to route requests across providers, cache responses, and control cost and latency. The other sits in the same spot to enforce identity, apply policy to the prompt, and produce an audit record. A trial goes badly when a security owner buys the first kind expecting the second. I want to walk through the criteria that tell them apart, in the order I would weigh them for a governed environment.

Start with the problem you are actually buying for

Before comparing features, name the job. If the pressure is model cost, provider failover, and developer speed, a routing-first gateway is the right shape. If the pressure is a regulator, a data-exposure incident, or a shared key that names no one, the job is enforcement, and routing features are a bonus rather than the point. These are not the same purchase, and most of the disappointment I see comes from evaluating an enforcement need against a routing product's demo.

The rest of this guide assumes the governance job, because that is the harder one to evaluate and the one where marketing language blurs the differences.

Criterion one: does it bind identity to the request

The first question is whether the gateway can attach a named caller to every model request, or whether it forwards traffic under the application's shared key. A gateway that only holds the provider key and routes by destination cannot tell you who sent a given prompt. Ask to see a single model call in the audit view and check whether it names a user or agent, or only a service. If the answer is only a service, the product is a router, whatever the label says.

Criterion two: does policy act on the prompt

An enforcement gateway terminates the call and reads the request body, so it can detect PII, secrets, and regulated data and act on them. Ask whether policy can permit, redact, or deny based on the content of the prompt and the caller's role, and ask to watch a request carrying a customer record get redacted in real time. A product that can only allow or block by endpoint is applying network logic to a content problem, and the sensitive payload it misses is inside the body it never opened.

Criterion three: what does the audit record contain

Every gateway logs something. The question is whether the record is evidence. Look for per-decision entries that carry identity, role, policy version, data classification, outcome, and timestamp, and ask whether the record is tamper-evident and committed independently of the calling application. A log the application can rewrite, or one scoped to a shared key, will not answer a regulator's questions no matter how much of it there is.

Criterion four: where the data goes and how the box is run

For a governed environment, deployment model is a real criterion, not a footnote. Ask where prompts are processed, whether request contents are retained by the vendor, and whether the gateway can run inside your own trust boundary. A tool that ships your prompts to a third party for inspection has moved the exposure rather than closed it. The data-handling answer belongs in the evaluation next to the feature list.

Criterion five: latency and failure behavior under load

An enforcement point sits in the request path, so its overhead and its failure mode matter. Ask for the added latency under production-like load; a well-built inline decision runs in tens of milliseconds against model inference that takes 500 ms to 5 seconds, so enforcement should not be the bottleneck. Then ask what happens when the gateway itself is unavailable. A fail-open default that lets traffic bypass policy under load is a different security posture than fail-closed, and you want to choose it deliberately rather than discover it during an incident.

A short way to run the trial

Bring one real, sensitive request to the proof of concept: a prompt that contains a record you would never want to leave ungoverned. Send it through as two different identities with two different roles. A gateway built for governance will name both callers, apply different policy to each, redact the sensitive field, and show you two per-decision records afterward. A gateway built for routing will forward both, log one shared caller, and cache the response. Fifteen minutes of that tells you more than a feature matrix.

DeepInspect

DeepInspect is the enforcement kind of AI gateway. It runs as a stateless proxy between your applications, agents, and staff and any LLM, binds a named identity to every request, applies per-route and per-role policy to the prompt with PII detection and redaction, and writes a tamper-evident per-decision record committed before the model responds.

It is built to run inside your trust boundary so prompts are governed where they originate rather than shipped elsewhere for inspection, and its inline overhead is measured in tens of milliseconds. If you are evaluating AI gateways against a compliance deadline, book an audit and we will run your own sensitive request through the criteria above.