LLM Gateway: One Endpoint for Safety, Cost Control, and Observability

This post is written for newcomers who want to understand and build an LLM Gateway quickly.

We’ll walk through one clear idea: a single HTTP endpoint /v1/run plus a configuration file policy.yaml that brings safety, cost management, and observability to your LLM workflow.

1. Why Do We Need an “LLM Gateway”?

What’s an LLM?
A Large Language Model (GPT, Claude, Llama, etc.) that takes text as input and returns text as output.

The problem: calling these models directly in production can cause headaches:

1. Privacy risks: emails or phone numbers left visible in prompts
2. Cost explosions: long inputs, expensive models
3. Unstable performance: latency spikes or timeouts
4. No visibility: no unified logging or traceability

The solution: an LLM Gateway
It’s a lightweight layer sitting between your business apps and the LLMs.

Just like an AI front desk, before sending a request to a model, it will:

• Ensure safety: redact PII (Personally Identifiable Information), enforce max input length, check data region.
• Control cost: apply daily budgets, automatically choose cheaper or faster models.
• Enable observability: generate requestId and traceUrl for every call.
• Manage routing: dynamically switch providers based on latency or price.

The LLM generates answers. The LLM Gateway keeps those answers safe, affordable, and traceable.

2. The Fixed Response Format: Why It Matters

Every call to /v1/run should return the same, predictable JSON response.

{
  "requestId": "req_123",
  "output": { "summary": "..." },
  "metrics": {
    "latency_ms": 812,
    "provider": "openai/gpt-4.1-mini",
    "cost_per_1k": 1.9
  },
  "traceUrl": "http://localhost:3000/trace/req_123"
}

Why a fixed response?
Even if you switch from GPT to Claude, or change providers and APIs, the contract stays stable. Your upstream applications don’t need any code changes.

That’s the essence of a Stable Contract: one interface, many interchangeable providers.

3. policy.yaml: The Heart of the Gateway

The gateway’s logic doesn’t live in code. It lives in a policy file. This makes it easy to adjust strategy without redeploying.

Example policy

name: finance
region: ca-central-1

providers:
  routing:
    - when: "latency_p95 > 2000 or cost_per_1k > 2.5"
      to: "anthropic/claude-3-5"
    - default: "openai/gpt-4.1-mini"

guardrails:
  pii_redaction: true
  max_context_tokens: 12000
  cost_cap_usd_per_day: 100

observability: { trace: true }
billing: { split_by: ["dept","project"] }

Field explanation

Upgrading Without Breaking Anything

Policies make upgrades safe and invisible to upstream systems.
Example, You want to:

• Switch from Claude to GPT-4.1-mini, and
• Raise the daily budget from $100 to $300.

Simply update:

providers:
  routing:
    - default: "openai/gpt-4.1-mini"
guardrails:
  cost_cap_usd_per_day: 300

Restart or reload the gateway, and you’re done. No code changes, no downtime, and your clients still call /v1/run the same way. That’s the beauty of decoupling policy from implementation.

4. A Practical Example: From Input to Redacted Output

Imagine a finance report generator that sends data to GPT for summarization. The prompt contains personal info, emails and phone numbers, that must not leave your region.

• Auto-redact private data before sending to the LLM
• Generate a summary
• Log all cost and latency details

The request

curl -s http://localhost:3000/v1/run   -H "Content-Type: application/json"   -H "x-region: ca-central-1"   -H "x-billing-dept: finance"   -H "x-billing-project: daily-report"   -d '{
    "input": {
      "prompt": "Please summarize our Q3 ARR and contact alice@example.com or call +1-416-555-1234 for details."
    },
    "tags": { "usecase": "financial-summary" }
  }'

The response

{
  "requestId": "req_ab12cd",
  "output": {
    "summary": "(openai/gpt-4.1-mini) Summary: Please summarize our Q3 ARR and contact [REDACTED_EMAIL], [REDACTED_PHONE]..."
  },
  "metrics": {
    "latency_ms": 645,
    "provider": "openai/gpt-4.1-mini",
    "cost_per_1k": 1.9
  },
  "traceUrl": "http://localhost:3000/trace/req_ab12cd"
}

What happened behind the scenes

1. The gateway verified region compliance (ca-central-1)
2. Detected PII and redacted it before sending to the model
3. Selected the provider based on routing rules
4. Checked cost limits before execution
5. Logged latency, cost, and trace details

As a result, the prompt is safe, the response consistent, and the call fully traceable.

5. Common Questions

1. Why use one unified /v1/run endpoint?

Because it decouples all your business systems from model details. Every app calls the same API. The gateway handles authentication, retries, routing, and observability.

2. How does automatic downgrade or routing work?

The gateway continuously tracks provider metrics like,

• latency_p95: 95th percentile latency
• cost_per_1k: average cost per 1,000 tokens

Then it evaluates routing rules, for example,

when: "latency_p95 > 2000 or cost_per_1k > 2.5"
to: "anthropic/claude-3-5"

If OpenAI becomes too slow or expensive, traffic automatically switches to Claude. When performance stabilizes, it switches back.

3. How does PII redaction work?

PII (Personally Identifiable Information) includes emails, phone numbers, IDs, etc. When redaction is enabled, the gateway replaces them before sending the prompt out.

alice@example.com → [REDACTED_EMAIL]  
+1-416-555-1234  → [REDACTED_PHONE]

In production, you can use more advanced libraries like Microsoft Presidio or AWS Comprehend PII, but the principle remains the same.

4. How is the cost cap enforced?

Before calling a model, the gateway

1. Estimates token count from the input
2. Multiplies by the provider’s price
3. Checks if adding this would exceed cost_cap_usd_per_day

If so, it either blocks the request or switches to a cheaper model.

For Example

cost_cap_usd_per_day: 100

Once daily spend exceeds $100, any new request gets,

{"error": "Daily cost cap reached"}

This simple mechanism prevents runaway costs and keeps budgets predictable.

5. What’s the purpose of billing tags?

When multiple teams share one gateway, you need cost attribution. You can pass tags in headers.

x-billing-dept: finance  
x-billing-project: daily-report

These tags are recorded in each trace, so monthly reports show,

This makes cost allocation and internal accounting much easier.

6. Summary

An LLM Gateway is your AI firewall.
It doesn’t generate text, but it ensures every generation is safe, cost-effective, and auditable.

With it, you can achieve

• Security and compliance
• Cost governance
• Auto failover and routing
• Full observability
• Zero-change upgrades for upstream systems

Related Resources

• Stable Names + Task Contracts: Keep Callers Stable While You Evolve Internals
• The Reuse Layer: Governing AI as a Capability, Not a Credential