An AI gateway is a proxy layer that sits between your application and one or more LLM provider APIs. It centralises concerns that would otherwise be scattered across every service that calls a model: authentication, rate limiting, cost allocation, logging, and failover.
Why a separate layer exists
Calling an LLM API directly works fine for a single service in development. As soon as multiple services, teams or environments are involved, the same boilerplate repeats: API key management, retry logic, timeout handling, spend tracking. An AI gateway moves all of that to one place, the same way a conventional API gateway centralises auth and routing for microservices.
The difference is that LLM traffic has characteristics ordinary HTTP traffic does not. Requests are expensive, slow, and variable in token count. Responses may need to be cached at the semantic level (returning a stored answer for a query that is close enough to a previous one, not just identical). The gateway can also apply content filtering before a prompt reaches the model and before a response reaches the user.
What an AI gateway typically does
- Provider routing. Send requests to OpenAI, Anthropic, a self-hosted model, or others based on rules: cost, latency, availability, or model capability required.
- Fallback and retry. If one provider returns an error or breaches a latency threshold, the gateway retries with another without the calling service knowing.
- Rate limiting and quotas. Enforce spend or request limits per team, per product, or per environment.
- Observability. Centralised logs of every request, token count, latency, and cost. This is difficult to reconstruct if each service logs independently.
- Caching. Exact-match caching is straightforward. Some gateways also support semantic caching using vector similarity, which reduces redundant calls for queries that are functionally the same.
- Prompt and response filtering. Strip or flag sensitive content, apply guardrails, or redact PII before data leaves your infrastructure.
How it differs from an ordinary API gateway
A standard API gateway routes HTTP traffic, enforces auth, and handles rate limits. An AI gateway does those things too, but adds token-aware cost controls, model-specific retry strategies, and often semantic caching. The two are not mutually exclusive: many teams run an API gateway in front of an AI gateway, or use a single product that handles both.
Where it fits in a production system
For a small project with one model and one calling service, a gateway adds operational overhead without much return. The calculus changes when:
- Multiple services or teams are sharing model access
- You need auditability for compliance or cost allocation
- You want to swap providers without touching application code
- You are working with a context window large enough that redundant calls carry material cost
For systems using RAG pipelines, a gateway often sits alongside the retrieval layer, handling the actual model calls while the retrieval logic operates separately.
Deployment options
Gateways can be self-hosted (open-source options exist) or run as managed services. Self-hosted gives more control over where data goes, which matters if your compliance posture requires prompts to stay within a specific jurisdiction. Managed services reduce operational burden but introduce a third party into the data path.
For teams with EU data residency requirements, the gateway's hosting location is not a minor detail. If prompts contain personal data, the gateway is a data processor and needs to be treated as one.
What we test for
Engineers working with AI gateways need to understand more than configuration. Miyagami's vetting covers Orchestration, not prompting: whether a candidate can reason about where a gateway fits in a multi-service architecture, what it should and should not be responsible for, and how to avoid over-centralising logic that belongs in the application. We also assess judgement on incomplete specs, since gateway configuration decisions, such as when to cache, when to fail over, and what to log, often involve trade-offs the initial brief does not resolve.
Short answers
Do I need an AI gateway if I'm only using one LLM provider?
Not necessarily. With one provider and one service, direct API calls are simpler. A gateway becomes worthwhile when you need centralised logging, cost controls across teams, or the ability to switch providers without changing application code.
Can an AI gateway reduce my LLM costs?
It can. Caching repeated or near-identical queries avoids redundant model calls. Rate limits and quotas prevent runaway spend. Routing to cheaper models for simpler requests also reduces cost, but that routing logic needs to be defined explicitly.
Is an AI gateway the same as an MCP server?
No. An AI gateway sits between your application and model APIs, handling routing and observability. An MCP server exposes tools and context to an agent at runtime. They address different layers and can coexist in the same system.
