LLM Gateway
The infrastructure pattern that makes AI governance universal, automatic, and impossible to bypass
Key Takeaways
- 1An LLM gateway is a proxy between your apps and model providers that applies governance controls to every request automatically
- 2Unlike library-based approaches, a gateway ensures 100% coverage — no developer can accidentally bypass controls
- 3Key capabilities: PII redaction, cost tracking, model routing, rate limiting, audit logging, and policy enforcement
- 4Gateway overhead should stay below the threshold buyers can feel; CrewCheck's current production measurement is sub-100ms at P95, excluding upstream provider time
What Is an LLM Gateway?
An LLM gateway is a proxy server that sits between your applications and LLM providers (OpenAI, Anthropic, Google, etc.), applying governance controls like PII redaction, cost tracking, and audit logging to every request that passes through it.
Think of it like a corporate firewall, but for AI traffic. Every prompt leaving your infrastructure and every response coming back passes through the gateway. This gives you a single enforcement point for all AI governance controls — regardless of which team, application, or SDK initiated the request.
The gateway pattern solves the fundamental problem with library-based governance: coverage gaps. When governance is a library that developers must remember to import and call, it only takes one missed endpoint, one new microservice, or one junior developer to create a compliance breach. A gateway eliminates this by operating at the network level.
Why Libraries Fail and Gateways Succeed
Most teams start with a library approach — wrapping their OpenAI SDK calls with PII detection. This works initially but breaks down as the organization scales:
Library-Based Governance
- Developers must remember to use the wrapper
- Each app implements governance differently
- New services can bypass controls entirely
- Version drift across teams
- No central visibility or audit trail
- Testing burden on every team
Gateway-Based Governance
- Automatic — no developer action required
- Consistent controls across all applications
- Impossible to bypass (network-level interception)
- Single version, centrally managed
- Unified audit trail and dashboard
- Test once, enforce everywhere
Core Gateway Capabilities
A production LLM gateway provides multiple governance capabilities in a single infrastructure component:
Architecture: How It Works
The gateway operates as a transparent proxy. Applications point their LLM SDK configuration to the gateway URL instead of directly to OpenAI/Anthropic. The gateway then:
1. Receives the request — authenticates the calling application and identifies the applicable policy pack.
2. Scans for PII — runs the multi-layer detection pipeline (regex → validation → context) on the prompt content.
3. Applies masking — replaces detected PII with masked values before the request leaves your infrastructure.
4. Evaluates policies — checks rate limits, budget caps, content policies, and routing rules.
5. Forwards to provider — sends the cleaned, policy-compliant request to the appropriate model provider.
6. Scans the response — checks the model's output for PII leakage, policy violations, or harmful content.
7. Logs the event — generates an immutable audit record with detection results, policy decisions, and routing metadata.
8. Returns the response — delivers the scanned response to the calling application.
This pipeline is measured as gateway overhead separately from provider time. CrewCheck's current production measurement is sub-100ms at P95.
Deployment Patterns
There are three common deployment patterns for LLM gateways, each with different tradeoffs:
Sidecar proxy: Deployed alongside each application as a container sidecar. Lowest latency, but requires container orchestration and per-service deployment.
Centralized gateway: A shared service that all applications route through. Simplest to manage, provides unified visibility, but adds a network hop.
SDK middleware: Embedded in the application's LLM SDK as middleware. No infrastructure changes needed, but relies on developers using the instrumented SDK.
CrewCheck uses the centralized gateway pattern because it provides the strongest guarantee of universal enforcement — if traffic can reach a model provider, it must pass through the gateway first.
Model Routing: Intelligence at the Gateway
Beyond governance, a gateway enables intelligent model routing — directing requests to different providers based on data sensitivity, cost, performance requirements, or compliance constraints.
Sensitive requests containing financial data can be routed to on-premise models that never send data externally. General queries can go to the fastest/cheapest cloud provider. Requests requiring specific capabilities (vision, code generation) can be routed to specialized models.
Routing rules can be defined per policy pack, per team, or per data classification level. This gives organizations fine-grained control over which data reaches which provider — a critical capability for DPDP compliance.
Performance: The Latency Budget
A governance gateway must be fast enough that users don't notice it exists. Here are the performance targets that matter:
Integration: Zero Code Changes
The gateway is designed for zero-code integration. Applications only need to change their base URL configuration:
// Before: Direct to OpenAI
OPENAI_BASE_URL=https://api.openai.com/v1
// After: Through CrewCheck gateway
OPENAI_BASE_URL=https://gateway.crewcheck.in/v1
// That's it. No SDK changes, no wrapper functions,
// no new dependencies. Same API, same responses,
// but now with full governance controls.This works because the gateway implements the same API interface as the model providers. Your existing OpenAI SDK, LangChain integration, or custom HTTP client works unchanged — it just points to a different URL.
Common Mistakes When Implementing a Gateway
Teams implementing LLM gateways frequently encounter these pitfalls:
- ✗Making the gateway a single point of failure without redundancy or failover
- ✗Adding too much latency by running expensive ML models in the hot path
- ✗Not handling streaming responses (SSE) — governance must work with streamed tokens
- ✗Forgetting to scan model outputs, not just inputs
- ✗Logging original PII values in gateway debug logs
- ✗Not implementing circuit breakers for when downstream providers are slow/down
- ✗Ignoring WebSocket connections for real-time AI features
- ✗Deploying without load testing at production traffic volumes
Frequently Asked Questions
Does an LLM gateway add noticeable latency?
Usually no. CrewCheck's latest production measurement is sub-100ms gateway overhead at P95, and that overhead is reported separately from upstream provider time. Total round-trip latency still depends on the chosen model vendor and region.
Can I use a gateway with streaming responses?
Yes. The gateway must handle Server-Sent Events (SSE) for streaming. PII scanning happens on the initial prompt, and output scanning can operate on accumulated tokens or use a sliding window approach.
What happens if the gateway goes down?
This is a critical design decision. Options include: fail-closed (block all AI traffic until gateway recovers — safest for compliance), fail-open (allow traffic to bypass — risky for compliance), or failover to a secondary gateway instance.
How does this work with LangChain or other frameworks?
Any framework that allows configuring the base URL works transparently. LangChain, LlamaIndex, Semantic Kernel, and direct HTTP clients all support custom base URLs. No framework-specific integration is needed.
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