What Is an LLM Gateway — and Where Does Mizan Fit?

An LLM gateway is an infrastructure layer

An LLM gateway is middleware that sits between your application and the AI models (or "providers") your application uses. Instead of integrating directly with every provider, your application sends requests through one unified gateway.

Think of it like an API gateway for traditional cloud infrastructure. The same way you might route all HTTP traffic through a gateway before it reaches your services, an LLM gateway routes all AI requests before they reach the models.

Why gateways are becoming important

Most AI applications start simple. A startup builds a feature using OpenAI. It works. The feature ships. But as the product grows, complexity increases.

You might need Claude for certain tasks but GPT-4 for others. You want to test a cheaper model to reduce cost. A provider has an outage, so you need a fallback. Different models have different latency profiles — some are fast but expensive, others are slow but cheap.

Without a gateway, you're stuck maintaining multiple integrations, managing separate credentials, trying to track cost across providers, and rebuilding your application logic every time you want to switch models.

An LLM gateway eliminates that friction. It becomes the single point of integration for model access, routing, observability, and cost control.

What does an LLM gateway actually do?

Unified API

Instead of maintaining separate integrations with OpenAI, Anthropic, Google, and others, one gateway provides a single API.

This means one set of credentials. One SDK. One way to make a request. Your application talks to the gateway; the gateway talks to the models.

Model routing

A gateway can route requests based on policies you define. Route requests for math problems to one model, creative writing to another. Route expensive models only to premium users. Route all traffic to a cheaper model when a budget limit approaches.

Routing happens automatically. Your application doesn't need to know the policy — it just sends a request, and the gateway handles model selection.

Observability

A gateway is the perfect place to collect usage data. Every request passes through it, so you can track:

• Tokens consumed per provider and model
• Number of API calls
• Response latency
• Error rates and failure modes
• Cost per provider

This telemetry becomes your source of truth for AI usage.

Cost management

Two different models might solve the same problem with radically different cost profiles. Claude 3.5 Sonnet might cost 50% more than Claude 3 Haiku for a task that doesn't require maximum reasoning.

A gateway can help you understand and optimize those tradeoffs. Route appropriate workloads to cheaper models automatically. Set spending limits. Alert when costs spike.

Caching

If the same request is made multiple times, a gateway can cache the response instead of purchasing new inference. This eliminates redundant spend without changing your application logic.

Reliability

A gateway architecture makes it easier to build resilience. If one provider is slow or fails, the gateway can automatically fall back to another. Your application doesn't need to know about the failure — the gateway handles it transparently.

How a request flows through an LLM gateway

Understanding the flow helps clarify what a gateway does:

1. 1Application sends request. Your code sends a request to the gateway instead of directly to a provider.
2. 2Gateway authenticates. It verifies that the request is legitimate and authorized.
3. 3Routing policy evaluated. Based on rules you define (cost, latency, model capability), the gateway decides which provider and model to use.
4. 4Request sent to selected provider. The gateway formats the request for that provider and sends it.
5. 5Response returns through gateway. The provider returns a response; the gateway passes it back to your application.
6. 6Usage recorded. The gateway logs cost, tokens, latency, and other metadata for observability and optimization.

LLM gateway vs. direct provider integration

Here's how the approaches compare:

So where does Mizan fit?

Mizan includes the gateway layer. But it's being built around a broader problem: AI spend.

A typical gateway is primarily concerned with moving requests between applications and models efficiently. Mizan's direction connects three layers:

• Router: How AI requests move. Routing across models, providers, and policies.
• Analyst: Where AI money goes. Visibility into spend across routed usage, provider APIs, and subscriptions.
• Optimization (Khabir): How unnecessary spend can be reduced. Identifying savings opportunities and recommending changes.

The gateway tells you where the request went. Mizan is designed to also help answer what it cost, who generated the spend, and where there may be an opportunity to spend less.

That's the key difference. Mizan is trying to join technical AI infrastructure with AI FinOps.

When should a team consider using an LLM gateway?

Not every application needs a gateway immediately. But here are signs it's time:

• Using multiple models or providers
• Model costs becoming material to your business
• Running production AI workloads
• Needing centralized usage visibility
• Frequently experimenting with different models
• Wanting to reduce provider lock-in
• Engineering and finance both needing visibility into spend

Getting started with Mizan

Mizan is designed to be adopted incrementally. The flow is straightforward:

1. 1Create an account: Sign up with Google or Microsoft. Set up your organization.
2. 2Top up your wallet: Add credits to your Mizan wallet. Use them across any AI model or provider.
3. 3Generate an API key: Create your unique Mizan API key. One key, every model.
4. 4Integrate: Change your application's base URL to point at Mizan. Start routing and tracking immediately.

You can start with just routing. Add spend visibility later. Add budget controls when you need them. The platform grows with your use case.

The future of AI infrastructure

As AI becomes more central to how companies operate, the infrastructure around it will become more sophisticated.

LLM gateways are a foundational piece of that infrastructure. They're the layer where routing, observability, and control converge. As AI spend becomes a material line item for more organizations, gateways — and the broader FinOps tooling around them — will become standard practice.

One API. Every AI dollar in view.