Webinar – Intelligent LLM Routing: Balancing Performance, Cost, and Efficiency in Multi-Model SystemsWebinar –

The rapid proliferation of large language models (LLMs) has created a heterogeneous landscape where models vary dramatically in capability, cost, and latency. While powerful models like GPT-4 deliver high-quality responses, their computational and financial costs can be prohibitive—with pricing differentials exceeding 50x between premium and lightweight models. Conversely, routing all queries to smaller, cost-effective models risks degraded response quality for complex tasks. This fundamental tension between performance and efficiency has given rise to LLM routing: a paradigm that dynamically selects the most suitable model for each incoming query.

LLM routing addresses a core challenge in modern AI deployment: how to maximize response quality while minimizing operational costs and latency. Rather than treating model selection as a static configuration decision, routing frameworks analyze each query’s characteristics—including complexity, domain requirements, and user expectations—to intelligently dispatch requests to appropriate models. Simple queries can be efficiently handled by lightweight models, while complex reasoning tasks are reserved for more capable systems.

Recent advances in LLM routing have explored diverse methodological approaches. Preference-based learning frameworks leverage human judgment data to train routers that distinguish query difficulty, achieving over 2x cost reduction without sacrificing quality. Contextual bandit approaches enable dynamic trade-offs among quality, cost, and latency while supporting continual learning in deployed systems. Graph-based methods model task-query-LLM relationships to enable cost-performance estimation and seamless integration of new models. Reinforcement learning frameworks treat routing as sequential decision-making, allowing routers to coordinate multiple LLMs across multi-round interactions for complex problem-solving.

Key challenges in this domain include generalizing to out-of-domain queries without requiring domain-specific routers, adapting to evolving model pools as new LLMs emerge and others become deprecated, and navigating the multi-objective optimization landscape where quality, cost, and latency often conflict. State-of-the-art systems demonstrate that well-designed routers can achieve approximately 97% of premium model quality at roughly 25% of the cost, representing significant efficiency gains for production deployments.

This presentation examines the foundations, methodologies, and practical implications of LLM routing, highlighting how intelligent query-model assignment transforms the economics of LLM deployment from a binary choice between capability and affordability into a nuanced optimization problem with substantial real-world benefits.

Dr. Igor Mishkovski (Ph.D. in Computer Science) is a Professor at the Faculty of Computer Science and Engineering (FINKI), Ss. Cyril and Methodius University in Skopje, North Macedonia, with expertise in Network Science, Machine Learning, Data Science and Artificial Intelligence

ORGANIZATION: Ss. Cyril and Methodius University – FCSE

ROLE: Professor and Researcher

Personal Info: Dr. Mishkovski is an experienced researcher specializing in complex networks, machine learning, and AI systems. He is actively involved in the VEZILKA project, AI infrastructure initiative funded through EuroHPC and Horizon Europe programs, aimed at establishing North Macedonia’s first National AI Factory Antenna.

His research portfolio includes over 100 publications, covering topics such as network vulnerability and robustness, multiplex networks, link prediction, cryptography, NLP applications in finance and Trustworthy AI . Notable contributions include pioneering work on vulnerability of complex networks, semantic homophily in social media, credit risk modeling using central bank data, and recent advances in company classification using neural networks and zero-shot learning.

His current work focuses on developing sophisticated LLM routing systems that balance model performance and cost using Graph Neural Networks, clustering approaches, and reinforcement learning, involving GPU-accelerated machine learning pipelines for large-scale datasets.