One Gateway.
Every AI Request.
AI traffic that goes straight from a laptop to a provider leaves no record, no attribution, and no opportunity to inspect what was sent. An LLM gateway puts one control point in that path — so every prompt is logged, every token is attributed to a person, and sensitive data is caught before it leaves your network.
What Is an LLM Gateway?
An LLM gateway is a proxy that sits between the people and applications using AI and the model providers serving it. Because every request passes through one point, the gateway can log prompts and responses, attribute token usage to a specific user, enforce policy on what may be asked, and inspect or redact data in flight before it leaves the network.
The result the caller sees is unchanged — the same model, the same response. What changes is that the traffic is now governable. Without a gateway, AI usage is a set of direct, unlogged connections to third parties. With one, it is infrastructure you can audit, budget, and enforce policy on, the same way you already do for every other egress path in your organization.
Two Kinds of Traffic, One Gateway
Same control point — different problem to solve first
Traffic Your Developers Generate
Claude Code, Cursor, and Copilot reading repositories and shipping the results to a provider. The gateway records and inspects that traffic without touching a single subscription.
For Engineering TeamsTraffic From Every Employee
Marketing, sales, support, and finance working through a branded chat portal. The gateway pools usage on one admin key, so access does not require a seat per person.
For Enterprise ITSeven Checks.
Every Request.
Each stage runs in order on the way out, and the inspection and recording stages run again on the way back. Nothing reaches a provider — and nothing returns to a caller — without passing through all of them.
Authenticate the caller via SSO or an issued key and resolve them to a user and team.
Check remaining allocation for that user or team, and reject the call if a hard limit is reached.
Evaluate Rego rules on the request — model, topic, parameters, role, and time of day.
Scan the outbound payload and redact secrets, credentials, and personal data in flight.
Forward to the configured provider — OpenAI, Anthropic, or Azure OpenAI — with credentials from the vault.
Apply the same policy and redaction to the response before it returns to the caller.
Write the full exchange to an immutable audit log and attribute tokens to the user and team.
What the Gateway Enforces
Prompt & Response Audit
An immutable record of every exchange, attributed to a user identity — the trail compliance reviews ask for and direct API access cannot produce.
Token Attribution
Usage broken down by user, team, and model in real time, instead of a single account-level total on a provider invoice.
DLP in Flight
Credentials, card numbers, phone numbers, and customer identifiers redacted before the payload leaves your network.
Rego Policy Engine
Version-controlled policies evaluated on every request and response. Restrict models, block topics, enforce role boundaries.
Credential Vaulting
Provider keys held centrally and never distributed. Rotate or revoke in one place without touching developer machines.
Anomaly Detection
Baselines per user and alerts on spikes, prompt loops, and off-hours consumption — the patterns that precede a large bill or an incident.
Prompts Are Only One Layer
An LLM gateway governs what a model is asked and what it answers. The moment that model can call tools — read mail, search files, update a ticket — the exposure moves to the tool layer, and it needs its own enforcement point.
LLM Gateway FAQ
What is an LLM gateway?
An LLM gateway is a proxy that sits between the people and applications using AI and the model providers serving it. Because every request passes through one point, the gateway can log prompts and responses, attribute token usage to a specific user, enforce policy on what may be asked, and inspect or redact data in flight before it leaves the network.
How is an LLM gateway different from calling the provider directly?
Calling a provider directly gives you no record of what was sent, no attribution beyond an API key, and no opportunity to inspect the payload. A gateway adds those without changing the result: the request still reaches the same model and returns the same response, but now there is a control point where governance can be applied.
Does a gateway add latency?
A pass-through gateway adds a small, generally imperceptible overhead relative to model inference time, which dominates end-to-end latency. Policy evaluation runs in milliseconds, and the request continues to the provider without waiting on anything else.
Do we have to replace our AI subscriptions to use a gateway?
No. That depends entirely on which problem you are solving. Engineering teams typically keep every existing subscription and use the gateway purely for audit and security over coding agent traffic. Organizations rolling AI out company-wide often use the gateway to pool usage on an admin API key instead of buying a seat per employee — but that is a choice, not a requirement.
Which providers does the SecuriX gateway support?
The gateway is provider-neutral and fronts the major model APIs including OpenAI, Anthropic, and Azure OpenAI. Because routing is configuration rather than contract, teams can be pointed at different providers and models without changing application code.
What is the difference between an LLM gateway and an MCP gateway?
An LLM gateway governs the model traffic itself — prompts and responses. An MCP gateway governs tool calls, the actions a model takes against real systems like mail, files, and trackers. They address different layers of the same problem, and SecuriX is building both.
Put a Control Point in the Path
Book a demo to see the gateway running against live traffic — the pipeline, the audit trail, and the policy engine behind both.
Book a Demo
See the SecuriX LLM Gateway in action. We'll reach out within 2 business days.