@heyshrutimishra: Most LLM routers are static rules; OrcaRouter is a router that learns. It embeds every prompt, scores it against past p…
Summary
OrcaRouter is a learning-based LLM router that dynamically routes prompts to appropriate models based on quality, cost, speed, and reliability, improving over time with production traffic.
Similar Articles
@amitiitbhu: New article: LLM Routing Read here: https://outcomeschool.com/blog/llm-routing…
A tutorial blog post explaining LLM Routing — the practice of directing user queries to the most appropriate LLM based on cost, latency, and quality. Covers routing strategies, anatomy of an LLM router, and comparisons with Mixture of Experts.
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs
Researchers from National Taiwan University propose replacing fixed translation-based prompting strategies in multilingual LLMs with lightweight learned classifiers that route each instance to either native or translation-based prompting. Their analysis across 10 languages and 4 benchmarks shows no single strategy is universally optimal, with translation benefiting low-resource languages most, and the learned routing achieving statistically significant improvements over fixed strategies.
TRACER: Trace-Based Adaptive Cost-Efficient Routing for LLM Classification
TRACER is an open-source system that trains lightweight ML surrogates on production traces from LLM classification endpoints, routing requests through a parity gate that activates surrogates only when agreement with the original model exceeds a specified threshold. This approach achieves 83-100% surrogate coverage on intent classification benchmarks while maintaining interpretability into handling boundaries and failure modes.
decolua/9router
9router is an open-source tool that provides unlimited free AI coding by connecting various coding assistants to multiple LLM providers with auto-fallback and token reduction features.
@Modular: HTTP routing has been a solved problem for many years. Then came Large Language Models. Their backends aren't interchan…
Modular published a blog post explaining why traditional HTTP routing doesn't work for LLM inference workloads. The article describes how their distributed inference framework handles stateful, heterogeneous GPU pods with KV caches, specialized prefill/decode backends, and conversation-level routing that traditional stateless routing algorithms cannot address.