LLM Gateway
A proxy between your app and model providers that centralises keys, routing, fallbacks, logging and spend limits.
/ quick answer
An LLM gateway exposes one API to the application while forwarding requests to many providers and models. It is where teams enforce rate limits, budgets, retries on transient errors, provider fallback and request logging, so that application code does not hard-wire a single vendor.
/ frequently asked
What is LLM Gateway?
An LLM gateway exposes one API to the application while forwarding requests to many providers and models. It is where teams enforce rate limits, budgets, retries on transient errors, provider fallback and request logging, so that application code does not hard-wire a single vendor.
What is an example of LLM Gateway?
When one provider returns repeated 5xx errors, the gateway retries with backoff and then fails over to a second model with the same capability.
Why does LLM Gateway matter?
A proxy between your app and model providers that centralises keys, routing, fallbacks, logging and spend limits.
What should I learn after LLM Gateway?
Open the related workflows and prompts further down this page: they show how this idea is applied in a real task, step by step.
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Related concepts
The vocabulary this page depends on.
- →AI Red Teaming
Deliberately attacking an AI system — jailbreaks, prompt injection, data leaks — to find failures before real users do.
- →Vector Database
A database optimized for similarity search over embeddings.
- →Embedding
A numerical vector representation of text, image or audio that captures meaning for similarity search.
- →Edge Computing
Running code and AI inference close to the user instead of in a central data center.
Related workflows
Turn this into a repeatable process.
- →Route Requests Between Models
Send each request to the cheapest model that can handle it, escalating only when needed.
- →Cut LLM Inference Costs
A step-by-step method to reduce model spend without degrading output quality.
- →Red Team an AI App
A repeatable process for attacking your own AI product before launch.
- →Cut Agent Costs by 60% Without Losing Quality
A measurable cost-reduction pass for any agent already in production.
Related tool stacks
The tools that run it in production.
- →LLM Cost Optimization Stack
The layers that keep AI spend predictable as usage grows.
- →AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
- →AI Security Stack
Least-privilege tooling, approval gates and audit trails for agentic systems.
- →Local AI Stack
Open-source tools to run chat, retrieval and agents entirely on your own hardware.
Related prompts
Reusable prompts for this job.
- →Model Routing Classifier Prompt
A cheap-model prompt that labels request difficulty so it can be routed to the right model.
- →Eval Rubric Prompt
Builds a scoring rubric a grader model can apply consistently.
- →AI System Threat Model Prompt
Produces a concrete threat model for an AI system with tool access.
- →Red Team Attack Plan Prompt
Generates a tailored list of adversarial tests for your own AI feature.
Related use cases
How people apply it, and what came out.
- →Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.
- →SaaS Cuts AI Bill With Routing
Illustrative example: routing easy requests to a small model reduces spend while holding quality steady.
Comparisons & alternatives
Pick between the options.
- →Model-Graded Evals vs Assertion Evals
Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.
- →Local LLM vs Cloud API
Local models win on privacy and fixed cost; cloud APIs win on quality, scale and zero maintenance.
- →Reasoning Model vs Standard LLM
Reasoning models are more accurate on multi-step problems but slower and pricier; standard calls suit simple, fast tasks.