AI Gateway
AI Gateway is a proxy or platform that abstracts multiple LLM inference providers behind a consistent API, enabling applications to switch models, implement fallback routing, enforce rate limits, cache responses, and capture observability data without changing application code; Application code sends requests to the AI gateway using a standardized format (often OpenAI-compatible); An AI gateway is particularly valuable for teams using more than one model provider or anticipating provider migration
AI Gateway is a proxy or platform that abstracts multiple LLM inference providers behind a consistent API, enabling applications to switch models, implement fallback routing, enforce rate limits, cache responses, and capture observability data without changing application code. It performs for LLM APIs a role similar to what API gateways do for microservices.
How it works
Application code sends requests to the AI gateway using a standardized format (often OpenAI-compatible). The gateway authenticates the request, applies routing rules based on model name, cost policy, or load, forwards the request to the selected provider, streams or returns the response, logs request and response data, and optionally applies caching for identical prompts. Fallback rules redirect traffic if a provider returns errors or exceeds latency thresholds.
Key facts
- Open-source options: LiteLLM, LM-Router, and Portkey are widely used open-source AI gateways.
- Managed options: Braintrust, Helicone, and cloud-native gateways from AWS and GCP offer managed gateway services.
- Capabilities: Routing, caching, rate limiting, cost tracking, PII scrubbing, and model fallback are standard features.
- OpenAI compatibility: Most gateways expose an OpenAI-compatible API, enabling drop-in replacement without SDK changes.
For builders
An AI gateway is particularly valuable for teams using more than one model provider or anticipating provider migration. It centralizes authentication management, provides a single point for cost monitoring and budget enforcement, and enables A/B testing of models without code deploys. Caching semantically identical prompts at the gateway layer can yield significant cost savings for high-volume applications with repetitive queries.
Sources
- Zhao, W. X., et al. (2023). A Survey of Large Language Models. arXiv:2303.18223. arxiv.org
- Yu, G., et al. (2022). Orca: A Distributed Serving System for Transformer-Based Generative Models. OSDI. usenix.org
- Kwon, W., et al. (2023). Efficient Memory Management for LLM Serving with PagedAttention. vllm-project. github.com
- Anthropic. Claude API documentation. docs.anthropic.com
- OpenAI. API reference. platform.openai.com
