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What is an AI Gateway?
An AI gateway is a reverse proxy layer specifically designed for LLM traffic. It intercepts requests destined for AI providers like OpenAI, Anthropic, Google, Cohere, and others, applies policy controls, and forwards them to the correct backend. Just as an API gateway standardizes access to microservices, an AI gateway standardizes access to language model APIs — adding observability, governance, and cost management in a single control plane.
AI Gateway vs API Gateway
Traditional API gateways are built for request/response workloads measured in milliseconds. AI gateways handle streaming responses, token-based pricing, model fallback logic, and prompt/completion logging — none of which fit cleanly into a traditional gateway model. AI gateways also understand LLM-specific concepts like context windows, temperature settings, and system prompts, enabling policy decisions that would be impossible for a generic API proxy.
Key Capabilities
A fully-featured AI gateway provides: unified multi-provider routing (send the same request to any LLM with a single endpoint), semantic caching (cache semantically similar prompts to cut costs and latency), rate limiting per user, team, or model, API key rotation and vault integration, real-time cost tracking with per-request token accounting, fallback and load balancing across providers, and full audit trails for compliance. These capabilities are especially critical when multiple teams share access to expensive frontier models.
Why You Need One
Without an AI gateway, each team or application manages its own LLM provider keys, rate limit handling, and cost tracking — creating security gaps and runaway spend. A centralized AI gateway enforces consistent policy across every LLM call in the organization, prevents API key proliferation (a leading cause of AI-related breaches), and provides the observability needed to detect misuse, abuse, and anomalous prompt patterns before they become incidents.
How G8KEPR's AI Gateway Works
G8KEPR's AI Gateway deploys as a drop-in proxy — point your OpenAI SDK at G8KEPR's endpoint and every LLM call flows through centralized controls. The gateway handles provider routing, semantic caching with configurable TTLs, per-workspace rate limits, automatic key rotation, and real-time spend dashboards. Security teams get full prompt and completion logging with PII redaction, while developers keep their existing SDK integrations unchanged.
Explore G8KEPR AI Gateway
See how G8KEPR puts AI Gateway controls into practice — from real-time detection to compliance documentation.
Explore G8KEPR AI GatewayRelated Terms
API Security
API security is the practice of protecting application programming interfaces from attacks, misuse, and unauthorized access. It covers authentication, authorization, input validation, rate limiting, threat detection, and compliance monitoring across REST, GraphQL, and other API protocols.
MCPMCP Security
MCP Security is the practice of protecting Model Context Protocol integrations — the open standard that enables AI agents to call external tools and APIs. It covers tool governance, session monitoring, prompt injection detection, and PII redaction for agentic AI systems.
AI SecurityLLM Security
LLM security encompasses the controls, monitoring, and policies needed to safely deploy large language models in production. It addresses prompt injection, data leakage, model abuse, output validation, and compliance requirements for AI-powered applications.