LogRouter: Adaptive Two-Level LLM Routing for Log Question Answering in Big Data Systems
LogRouter demonstrates a pragmatic shift in production AI deployment: routing queries intelligently across multiple execution paths rather than defaulting to expensive LLM inference. Deployed on Turkey's national big data platform, the system uses a two-tier cost-aware router to dispatch queries to keyword search, SQL generation, or semantic retrieval with appropriately sized models (14B or 32B), reducing computational overhead while maintaining accuracy. This pattern reflects growing maturity in enterprise AI infrastructure, where the competitive edge lies not in model scale but in orchestration efficiency and resource allocation under real-world constraints.
Modelwire context
ExplainerLogRouter's contribution isn't the routing itself, but the explicit cost-aware tiering: the system learns when a 14B model suffices versus when 32B is necessary, rather than defaulting to the largest available model. This is a learned efficiency pattern, not a hand-tuned heuristic.
This connects directly to the federated learning work from mid-May, particularly FedUCA's insight that real deployments face economic constraints on participation and resource allocation. LogRouter operationalizes that principle within a single system: by making routing decisions that preserve accuracy while cutting compute, it solves the local efficiency problem that federated clients face when deciding whether to participate. The MAD framework's emphasis on orchestrating heterogeneous data streams in real time also parallels LogRouter's two-tier dispatch logic, though LogRouter applies it to query classification rather than lab instrumentation.
If TUBITAK BILGEM publishes operational metrics showing that the 14B routing decisions maintain sub-100ms latency while cutting inference cost by 40%+ compared to always-32B baselines, that confirms the pattern scales to production. If the same routing accuracy holds on out-of-distribution log types (e.g., new system architectures added post-deployment), that signals the learned routing generalizes beyond the training domain.
Coverage we drew on
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MentionsLogRouter · TUBITAK BILGEM · PySpark · Drain3 · Apache Druid · PostgreSQL
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