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AI Gateway

/eɪ aɪ ˈɡeɪtweɪ/noun

Filed underAiInfrastructureDevops

In brief · quick answer

An AI gateway is a middleware layer that sits between your applications and AI model providers, handling routing, security, rate limiting, cost tracking, and governance for all AI traffic.

§ 1 Definition

An AI gateway (also called an LLM gateway) is a specialized infrastructure middleware that manages, secures, and optimizes all traffic between applications and AI model providers (OpenAI, Anthropic, Google, open-source models, etc.). It functions as a unified control plane for AI operations: routing requests to the appropriate model, enforcing rate limits and authentication, tracking costs and usage, caching responses, applying content guardrails, and collecting observability data. AI gateways extend the API gateway concept with AI-specific features: token-based rate limiting (vs request-based), semantic caching (cache based on meaning, not exact text), multi-provider routing with fallback, prompt inspection and redaction, and cost allocation per team or project. As organizations scale AI usage, a gateway becomes essential for governance and cost control.

§ 2 AI Gateway vs API Gateway

Traditional API gateways (Kong, Apigee, AWS API Gateway) handle REST/gRPC traffic with request-based rate limiting, authentication, and routing. AI gateways add capabilities specific to LLM traffic: token-based rate limiting (LLM costs depend on token count, not request count), semantic caching (cache results for semantically similar queries, not just identical ones), multi-model routing with fallback (try GPT-4o, fall back to Claude if unavailable), prompt/response inspection for PII redaction and safety, streaming response handling, and cost tracking per model, per user, per application. Many organizations start with an API gateway and add an AI gateway alongside it as AI usage grows past the experimental phase.

§ 3 Key Features of an AI Gateway

Multi-provider routing: Route to different models based on cost, latency, or capability requirements. Implement fallback chains for high availability. Token-based rate limiting and cost controls: Limit spend per user, team, or application. Set hard caps to prevent budget overruns. Semantic caching: Cache LLM responses based on embedding similarity. Can reduce costs by 30-60% for common queries. Guardrails and content filtering: Inspect prompts for PII, toxicity, prompt injection, and banned topics before they reach the model. Observability and logging: Track every request, response, latency, token count, and cost. Export to monitoring systems. Key management: Securely store and rotate API keys. Never expose provider keys to client applications. A/B testing: Route a percentage of traffic to different models or prompts for evaluation.

§ 4 When You Need an AI Gateway

You need an AI gateway when: more than one team in your organization uses AI, you need to control costs across multiple projects, you require audit trails for compliance, you route to multiple model providers, or you need to enforce security policies on AI inputs and outputs. If you have a single developer using one API key for one project, start without a gateway. As soon as the second developer or second model provider enters the picture, the gateway becomes valuable. Popular implementations include Portkey, LiteLLM, Cloudflare AI Gateway, and open-source options like MLflow AI Gateway.

§ 5 Common questions

Q. Is an AI gateway the same as a model router?: A. A model router is one component of an AI gateway. The gateway also includes security, caching, cost tracking, and observability. A model router only handles where to send the request. A gateway is the full infrastructure layer.
Q. Can I run an AI gateway on-premises?: A. Yes. Open-source gateways like LiteLLM and MLflow AI Gateway can be self-hosted. This is important for organizations that need data to never leave their infrastructure for compliance or privacy reasons.
Q. Does an AI gateway add latency?: A. Yes, some. Typical gateway overhead is 5-20ms per request, depending on features enabled (caching can reduce overall latency, making up for the overhead). For most use cases, this is negligible compared to LLM response times of 1-10 seconds.

Key takeaways

An AI gateway is middleware that manages, secures, and optimizes all traffic between apps and AI providers.
Key features: multi-provider routing, token-based rate limiting, semantic caching, guardrails, and cost tracking.
Essential when multiple teams or models are in use; overkill for single-developer, single-model projects.
Start with a simple gateway (LiteLLM or Cloudflare) and scale up as AI usage grows.

How Atomic Glue helps

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# AI Gateway

An AI gateway is a middleware layer that sits between your applications and AI model providers, handling routing, security, rate limiting, cost tracking, and governance for all AI traffic.

Category: Ai (also: Infrastructure, Devops)

Author: Atomic Glue Editorial Team

## Definition

An [AI gateway](/glossary/ai-gateway) (also called an LLM gateway) is a specialized infrastructure middleware that manages, secures, and optimizes all traffic between applications and AI model providers (OpenAI, Anthropic, Google, open-source models, etc.). It functions as a unified control plane for AI operations: routing requests to the appropriate model, enforcing rate limits and authentication, tracking costs and usage, caching responses, applying content guardrails, and collecting observability data. AI gateways extend the API gateway concept with AI-specific features: token-based rate limiting (vs request-based), semantic caching (cache based on meaning, not exact text), multi-provider routing with fallback, prompt inspection and redaction, and cost allocation per team or project. As organizations scale AI usage, a gateway becomes essential for governance and cost control.

## AI Gateway vs API Gateway

## Key Features of an AI Gateway

**Multi-provider routing:** Route to different models based on cost, latency, or capability requirements. Implement fallback chains for high availability. **Token-based rate limiting and cost controls:** Limit spend per user, team, or application. Set hard caps to prevent budget overruns. **Semantic caching:** Cache LLM responses based on embedding similarity. Can reduce costs by 30-60% for common queries. **Guardrails and content filtering:** Inspect prompts for PII, toxicity, prompt injection, and banned topics before they reach the model. **Observability and logging:** Track every request, response, latency, token count, and cost. Export to monitoring systems. **Key management:** Securely store and rotate API keys. Never expose provider keys to client applications. **A/B testing:** Route a percentage of traffic to different models or prompts for evaluation.

## When You Need an AI Gateway

## Common questions

Q: Is an AI gateway the same as a model router?

A: A model router is one component of an AI gateway. The gateway also includes security, caching, cost tracking, and observability. A model router only handles where to send the request. A gateway is the full infrastructure layer.

Q: Can I run an AI gateway on-premises?

A: Yes. Open-source gateways like LiteLLM and MLflow AI Gateway can be self-hosted. This is important for organizations that need data to never leave their infrastructure for compliance or privacy reasons.

Q: Does an AI gateway add latency?

A: Yes, some. Typical gateway overhead is 5-20ms per request, depending on features enabled (caching can reduce overall latency, making up for the overhead). For most use cases, this is negligible compared to LLM response times of 1-10 seconds.

## Key takeaways

An AI gateway is middleware that manages, secures, and optimizes all traffic between apps and AI providers.
Key features: multi-provider routing, token-based rate limiting, semantic caching, guardrails, and cost tracking.
Essential when multiple teams or models are in use; overkill for single-developer, single-model projects.
Start with a simple gateway (LiteLLM or Cloudflare) and scale up as AI usage grows.

## Related entries

Last updated July 2026. Permalink: atomicglue.co/glossary/ai-gateway