What is an LLM Gateway?

An LLM gateway (also called an AI gateway) is a single, governed entry point that sits between your applications and the large language models they consume. Instead of each application holding its own provider API keys and calling api.openai.com or api.anthropic.com directly, every request flows through one endpoint that applies routing, authentication, rate limiting, caching, cost accounting, data loss prevention, and audit logging in a consistent way.

The pattern exists because the naive alternative does not scale. The moment a second team ships a second AI feature, you have two sets of provider keys, two billing surfaces, two retry strategies, two places where a prompt containing a customer record might leak, and zero unified visibility into what your organisation is actually sending to third-party models. An LLM gateway collapses that sprawl into one chokepoint - the same architectural instinct that produced API gateways for microservices and egress proxies for outbound traffic.

The category is young but real. Gartner places AI gateways inside the emerging AI engineering and AI TRiSM stack, and open-source projects in the space have grown fast - LiteLLM alone has tens of thousands of GitHub stars as a developer-facing routing layer. The reason the term appears in security and platform conversations at once is that a gateway is simultaneously a productivity tool (one SDK, many models) and a control tool (one place to enforce policy).

A gateway on its own, though, is plumbing. It moves requests and meters them. Turning that plumbing into governance - who is allowed to ask what, which data may leave the building, and an audit trail an auditor will accept - is a separate layer that the strongest platforms build on top of the gateway rather than beside it. That distinction is the throughline of this page, and it is exactly the gap the Areebi platform closes.

These three terms are used loosely and often interchangeably, which causes real confusion in procurement. They describe different scopes of responsibility, and conflating them leads teams to buy a proxy when they needed a control plane.

Dimension	Reverse Proxy	LLM Gateway	AI Control Plane
Primary job	Forward and load-balance traffic	Unify and meter LLM API calls	Govern AI usage across the organisation
Protocol awareness	HTTP only - payload is opaque	Understands prompts, models, tokens, streaming	Understands users, policy, data class, intent
Provider abstraction	None	One schema across many providers	Inherited from the gateway it includes
Data inspection	None or basic WAF rules	Optional content filters	Real-time DLP with PII and PHI redaction
Identity model	IP or API key	API key or service account	SSO, SAML, RBAC, per-user attribution
Audit output	Access logs	Request and cost logs	Immutable, per-user, compliance-grade trail
Answers "who asked what?"	No	Partially (by key)	Yes (by named user and policy decision)

The clean way to hold it in your head: a reverse proxy moves bytes and does not know it is carrying an AI prompt. An LLM gateway knows it is carrying a prompt and can normalise, route, cache, and meter it across providers. An AI control plane knows who sent the prompt, what data class it contains, and what policy applies - it treats the gateway as one component inside a governance system that also covers data inspection, identity, and audit.

Most teams discover they wanted the control plane after they built the gateway, because the questions that arrive from security, legal, and audit - "show me everything the claims team sent to an external model last quarter, and prove no PHI left the building" - are governance questions a bare gateway cannot answer. We cover that progression in detail in our comparison of the AI control plane versus the AI gateway.

A production-grade LLM gateway earns its place by doing several jobs that you would otherwise reimplement, inconsistently, in every application. The four that matter most for an enterprise are routing, rate limiting, data loss prevention, and audit.

1. Routing and Provider Abstraction

The gateway exposes one request schema and translates it to whichever provider should serve the call. That single abstraction unlocks several capabilities at once: failover (if one provider returns errors, retry against another), load balancing across multiple keys or regions, model routing by task (send cheap classification to a small model, hard reasoning to a frontier model), and vendor portability (swap a model without touching application code). This is the function that makes a model-agnostic enterprise LLM strategy practical rather than aspirational.

2. Rate Limiting and Quotas

Provider rate limits are blunt - they protect the provider, not your budget. A gateway adds your limits: per-user, per-team, per-application, and per-model token and request quotas, with spend caps that prevent a runaway agent loop from generating a five-figure bill overnight. This is the same discipline described in our guide to AI rate limiting, applied at the one point where every request is visible.

3. Data Loss Prevention

Because the gateway sees the full prompt before it leaves your environment, it is the natural place to inspect for sensitive data. A governance-grade gateway scans prompts for PII, PHI, secrets, and source code, and applies a policy action - block, redact, or warn - before the request reaches a third-party model. It also inspects responses, since models can echo sensitive input or surface confidential documents through retrieval. Without this layer, the gateway is a faster way to leak data, not a safer one. See what is AI DLP for the detection mechanics.

4. Audit, Observability, and Cost Accounting

Every request through the gateway produces a record: who or what made the call, which model served it, token counts in and out, latency, cost, and any policy decisions applied. Aggregated, this is your AI observability and chargeback data; per-request and immutable, it is your compliance audit trail. The difference between a logging gateway and a governance platform is whether those records are tamper-evident and attributable to a named user - see AI observability and AI audit.

Secondary functions worth noting: semantic and exact caching (return a stored response for a repeated query, cutting cost and latency), prompt and response transformation, guardrails against prompt injection, and secrets management so that provider keys live in the gateway rather than scattered across application config.

The architecture is conceptually simple and operationally exacting. A request travels through an ordered pipeline, and the order is not arbitrary - DLP and policy must run before the request leaves your boundary, and audit must capture both the pre- and post-policy state.

A typical request lifecycle:

1. Ingress and authentication. The application calls the gateway endpoint. The gateway authenticates the caller - ideally a named user via SSO or SAML, not just an API key - and resolves their role and permissions.
2. Policy evaluation. The gateway checks which models the caller may use, what data classes are permitted, and what quotas apply. A request to send regulated data to an external model may be denied here outright.
3. Inbound DLP. The prompt is scanned for sensitive data. Detected entities are blocked, redacted, or flagged according to policy, and the original and sanitised versions are recorded for audit.
4. Routing. The gateway selects the target provider and model based on the request, routing rules, health checks, and load-balancing state, then translates the request to that provider's schema.
5. Rate limiting and caching. Quotas are enforced; the cache is checked for a valid stored response before any external call is made.
6. Provider call. The request is dispatched, with retries and failover to an alternate provider if the primary fails.
7. Outbound DLP and guardrails. The response is inspected for leaked data and policy violations before it returns to the application.
8. Audit and metering. A complete, immutable record is written: caller, model, tokens, cost, latency, policy decisions, and DLP actions.

Two deployment topologies dominate. In the centralised SaaS model the gateway is a managed service your apps call out to - lowest operational burden, but your prompts transit the vendor's infrastructure, which reintroduces the data-path question. In the self-hosted model the gateway runs inside your own VPC, on-premise, or air-gapped environment, so prompts never leave your trust boundary on their way to inspection. For organisations with residency or regulatory obligations, the self-hosted topology is usually mandatory - the same logic that drives a private LLM deployment in the first place. Areebi runs in either topology, including fully air-gapped, so the gateway sits wherever your data residency rules require.

The build-versus-buy question for LLM gateways has a deceptively easy first answer and a hard second one. Standing up an open-source gateway for routing is genuinely a short project. Operating a gateway that satisfies security, legal, and audit is not.

Capability	DIY open-source gateway	Governance platform (e.g. Areebi)
Multi-provider routing	Strong - this is the core feature	Included, plus 30+ providers
Rate limiting and cost tracking	Available, needs configuration	Built in, per-user and per-team
Real-time DLP / PII redaction	Not included - you build or bolt on	Native on prompts and responses
SSO / SAML / RBAC	Usually basic key auth; enterprise SSO is paid or absent	First-class identity and role model
Immutable audit trail	Request logs you must harden and retain	Tamper-evident, compliance-grade
No-code policy engine	Config files and code	Policies authored without engineering
RAG and workspace isolation	Out of scope	Included with per-workspace boundaries
Ongoing operations	Your team, permanently	Managed or self-managed with support

The honest decision rule: build if you need routing and metering and nothing more - a small developer platform team can run an open-source gateway competently. Buy if the gateway has to be a compliance control - the moment DLP, named-user attribution, an auditor-grade trail, and a policy engine that non-engineers can operate become requirements, you are no longer building a gateway, you are building a governance platform, and that is a multi-quarter programme with permanent operational cost. We walk through the full economics in our Areebi versus DIY open-source comparison and the broader build-versus-buy analysis.

A common and sensible hybrid: keep an open-source router for low-sensitivity internal experimentation, and route anything touching customer, regulated, or proprietary data through a governed platform. The routing layer is cheap; the governance layer is where the liability lives.

Areebi is built on the premise that the gateway and the governance layer should not be two procurements. It is an enterprise secure AI platform that gives applications a single governed endpoint to 30+ LLM providers, then wraps that endpoint in the controls that turn routing into governance.

• One endpoint, 30+ providers: route across open-weight models on your own GPUs and commercial APIs through a single schema, with failover and per-task model routing - the foundation of a model-agnostic strategy.
• Inline DLP, both directions: real-time PII and PHI detection and redaction on every prompt and response before anything reaches an external model.
• Identity-aware by default: SSO, SAML, MFA, and RBAC mean every request is attributable to a named user, not an anonymous API key.
• No-code policy engine: security and compliance teams author routing and data rules without filing engineering tickets.
• Immutable audit logs: a tamper-evident, per-request trail that answers "who sent what to which model, and what did policy do about it" - the question a bare gateway cannot.
• Deploy where your data must stay: Docker, Kubernetes, VM, fully air-gapped, or local-only inference via Ollama or LM Studio, so the gateway lives inside your residency boundary.
• Browser extension control and RAG: block external AI tools at the endpoint and run workspace-isolated retrieval over your own documents through the same governed layer.

If you are weighing whether to assemble an open-source gateway plus a DLP product plus an identity layer plus an audit pipeline, compare that effort against one platform that ships them integrated. Start with the platform overview, see pricing, or book a demo to route your own traffic through it. For the conceptual map of where the gateway fits, read what is an AI control plane and what is LLM security.