AI Gateway vs AI Firewall: What Each One Does and When You Need Which

What is an AI gateway?

An AI gateway, sometimes called an LLM gateway, is a control point that sits between your applications and one or more model providers. Applications send requests to the gateway instead of calling each provider directly, and the gateway forwards them. Its core functions are operational:

• Routing: directing requests to the right provider or model, with failover.
• Key management: holding provider credentials centrally so that applications and users do not each carry their own.
• Rate limits and quotas: controlling usage per team, application or user.
• Observability: logging requests, latency, errors and token usage.

A gateway gives you one place to see and shape AI traffic. On its own, though, it does not decide whether a prompt or an answer is acceptable.

What is an AI firewall?

An AI firewall is a control layer that inspects AI traffic and enforces policy on its content. Where a gateway asks where a request should go, a firewall asks whether it should go at all and what it may contain. Typical functions are:

• Policy evaluation: rules about which models, providers, data types and use cases are allowed.
• Inspection: reading prompts, completions, files and agent tool calls for sensitive data or policy violations.
• Redaction: removing or masking personal or confidential data before it leaves your environment.
• Blocking and logging: stopping disallowed requests and recording the decision.

It is not the same as a web application firewall. A web application firewall protects applications from attacks such as malicious HTTP requests, while an AI firewall works on the meaning and content of AI interactions. The two complement each other.

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

The difference is the question each one answers. A gateway manages access and operations: who calls which model, how often and at what cost. A firewall manages content and policy: what may be sent, what may come back and what happens when a rule is broken.

In practice the boundary is blurry, because both need to sit in the request path. A gateway without inspection is a router with good dashboards. A firewall without routing and key control is a filter that still needs plumbing around it. Many deployments therefore treat them as two functions in one position rather than two separate boxes.

For regulated organisations the distinction matters because the evidence differs. Gateway logs show usage. Firewall logs show policy decisions, for example that a request was redacted or blocked and why. Auditors and risk teams usually need the second kind.

When you assess any solution in this area, a few questions are more useful than feature lists. Does it sit in the path of every call, or only those that applications choose to send? Can a policy be changed without a code release? Are decisions recorded with enough detail to reconstruct them later? Who approves a rule change? And where do the prompts, completions and logs physically reside? The answers tell you whether you are buying visibility, enforcement or both.

What are LLM guardrails and how do they differ?

LLM guardrails are checks that constrain model behaviour or output, for example filters for toxic content, schema validation, prompt injection detection or topic restrictions. They come in two broad forms:

• In-app guardrail libraries: code embedded in each application. They can be tailored to one use case, but each team has to adopt, configure and maintain them, and coverage depends on every application doing so.
• Network-enforced guardrails: rules applied at a shared control point that every call passes through. Coverage does not depend on individual developers, and one policy applies everywhere.

Neither form is sufficient alone. Application-level checks understand business context. Network-enforced checks give consistency and an audit trail. The OWASP Top 10 for LLM Applications, available at owasp.org, lists risks such as prompt injection and sensitive information disclosure that both layers help address.

When do you need a gateway, a firewall or both?

A rough guide:

• Gateway only: a small number of applications, low sensitivity data, and a main need for cost control, failover and visibility.
• Firewall policy only: you already have a way to route traffic and mainly need to stop sensitive data leaving or enforce rules about permitted models.
• Both: multiple teams and providers, regulated or personal data, and a need to show evidence of control to auditors, customers or regulators.

Frameworks point the same way. The NIST AI Risk Management Framework, with its Generative AI Profile NIST AI 600-1, expects organisations to govern, measure and manage generative AI risks, and it is available from NIST. The EU AI Act, Regulation (EU) 2024/1689 on EUR-Lex, requires record-keeping and human oversight for high-risk systems, which depend on being able to observe and control AI use in the first place. The Commission summarises the Act on its regulatory framework page.

How do the gateway and firewall combine?

The cleanest pattern is a single transparent proxy in the request path that performs both jobs. Applications change only their base URL. Each request is checked against policy, redacted where required, routed to the permitted provider and logged. Responses come back through the same point. One policy set then covers every application, and one audit trail records what happened.

This also keeps change control manageable. A new rule can be proposed, reviewed by a second person and then published, so that policy changes are themselves traceable. Our article on the drop-in proxy approach to LLM governance describes this model in more depth.

Deployment location is a design decision of its own. Keeping the control plane inside your perimeter means policy logic, audit data and model traffic stay under your control, which matters for regulated organisations in the UAE, the GCC and the EU.

How Krasper helps

Raigate is Krasper's self-hosted AI governance platform. It works as an AI gateway and LLM API gateway in front of your model providers, as a transparent proxy for OpenAI, Anthropic, Gemini, Microsoft Copilot and custom upstreams, and it applies firewall style policy on the same path: every call is policy-evaluated, redacted where required and logged. A proposed rule changes nothing by itself. It is compiled, approved by a different operator and only then published.

The control plane runs inside your perimeter. If you allow an external AI provider, permitted requests go to it after the configured policy checks and redaction. Fully isolated operation requires local models and integrations, and we agree deployment location, outbound access and evidence retention during scoping. Raigate supports evidence for your governance programme. It does not certify an organisation or guarantee compliance.