Key facts
| Definition | A single endpoint that fronts many models with shared policy and observability |
| Core functions | Auth, routing, fallbacks, rate limits, caching and usage tracking |
| Why it matters | Applications stop hard-coding models; policy is enforced in one place |
| Plugsky implementation | OpenAI-compatible API exposing 30+ models through one integration |
| Routing | Select models per request for cost, latency or capability |
| Governance | Scoped keys, RBAC, SSO and audit logs |
| Deployment | Cloud, VPC, on-prem and air-gapped options |
| Status | Chat, streaming, JSON mode, function calling and embeddings are live |
TL;DR
- One endpoint, many models, shared policy.
- Clients integrate once; model choice becomes configuration.
- OpenAI compatibility keeps the client surface familiar.
- Route by task to control cost without changing code.
- Centralized keys and audit logs simplify governance.
How it works, step by step
- Consolidate model access behind a single endpoint instead of per-service integrations.
- Issue scoped keys per application and environment through the gateway.
- Define routing rules and fallbacks per task type.
- Set rate limits and budgets so one workload cannot starve others.
- Collect usage, latency and error metrics from the gateway layer.
- Review model availability and routing quarterly, and retire unused routes.
Try it yourself
Open the OpenAI-compatible API tester →
What an LLM gateway centralizes
Without a gateway, every service embeds provider SDKs, keys, retry logic and model names. With one, applications authenticate once to a single endpoint and the gateway handles provider credentials, model selection, rate limits and logging. The result is fewer integration points, uniform policy and one place to see usage. It is the difference between model access being an application concern and being platform infrastructure.
Design decisions
- Protocol: OpenAI-compatible endpoints minimize client changes.
- Routing: static task rules first, learned routing later.
- Failover: fallback models and retries for availability.
- Limits: per-key quotas that protect shared capacity.
- Caching: only for responses safe to reuse, never user-specific data.
- Observability: per-key usage, latency and error metrics.
Each decision should be documented, because the gateway is now a dependency every service shares.
Common mistakes
- Treating the gateway as fire-and-forget infrastructure with no health monitoring.
- Centralizing keys but leaving retries and timeouts scattered in clients.
- Caching personalized responses and leaking them across users.
- Routing by cost without measuring quality impact.
- Ignoring the added hop in tight latency budgets.
Plugsky as your LLM gateway
Plugsky exposes 30+ models through one OpenAI-compatible endpoint, so applications integrate once and change models by parameter. Platform controls cover scoped API keys, RBAC and SSO, audit logs, and usage visibility — the governance half of a gateway. Routing across models supports cost management, and streaming, JSON mode, function calling and embeddings are live. When traffic must not leave your network, VPC, on-prem and air-gapped deployments place the gateway inside your environment without changing application code.
Start with one workload behind the gateway, then migrate the rest.
Honest comparison
| Aspect | LLM gateway | Direct SDK per provider | Self-built proxy |
|---|---|---|---|
| Integrations | One endpoint | One per provider | You maintain it |
| Model switching | Configuration | Code changes | Your abstraction |
| Governance | Central keys, RBAC, audit | Scattered | You implement |
| Observability | Unified usage view | Per-service | You instrument |
| Availability risk | Single hop to harden | Many failure points | You operate |
Frequently asked questions
What is an LLM gateway?
A single endpoint that fronts multiple models and providers, providing authentication, routing, fallbacks, rate limits, caching and observability so applications integrate once.
How is it different from a normal API gateway?
A traditional API gateway routes HTTP traffic. An LLM gateway understands model semantics — token usage, streaming, tool calls, model selection and prompt-level policy.
Is Plugsky an LLM gateway?
Yes, in function: one OpenAI-compatible endpoint exposes 30+ models with key management, RBAC, audit logs and usage visibility.
Will a gateway slow down my requests?
It adds a hop, so latency increases slightly. Connection reuse, caching and thoughtful deployment placement keep the overhead small.
Can a gateway cache responses?
Yes, but only where reuse is safe. Never cache responses that depend on user-specific context, because that risks cross-user data leakage.
Does a gateway lock me in?
A well-chosen gateway reduces lock-in: applications speak a standard protocol, and models behind the endpoint can change without code changes.