Large Language Model Access Points to Improve Efficiency and Reliability
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| Source: Dev.to | Original article
LLM gateways optimize AI model performance with routing and caching. They improve request handling and latency.
LLM gateways have become a crucial component in managing large language models, providing a single, stable API across multiple model providers. As we previously reported, LLMs have been gaining attention for their capabilities, but also pose challenges in terms of routing, fallbacks, and semantic caching.
The use of LLM gateways matters because they standardize access, control cost, and improve uptime. Teams utilize gateways for LLM routing, AI monitoring, observability, and governance. Gateways add features such as observability, rate limiting, cost tracking, fallback, and caching on top of routing, making them a valuable tool for managing LLMs.
As the landscape of LLM gateways continues to evolve, it will be important to watch for developments in routing, fallbacks, and semantic caching. With various gateways available, such as LiteLLM, OpenRouter, and Portkey, teams will need to compare and evaluate the best options for their specific needs, considering factors such as cache hit rates, retries, and queueing.
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