Unlocking AI Potential: How Model Routing Maximizes Efficiency in Enterprises

As enterprises increasingly embrace AI technologies, they encounter a pivotal challenge: selecting the optimal AI model for each task while balancing performance and costs. Model routing emerges as a groundbreaking solution, enabling organizations to maximize AI efficiency.

Model routing technology empowers businesses to dynamically choose the most suitable AI model on a query-by-query basis, fundamentally transforming how they utilize AI resources. This method enhances performance and significantly cuts costs compared to relying on a single, generalized model.

Martian: Pioneering AI Model Routing

One notable startup in this domain is Martian, which has developed an innovative large language model (LLM) router that has captured the attention of leading technology firms. Recently, Accenture, a global professional services company, announced an investment in Martian, underscoring the rising significance of model routing in enterprise AI strategies.

Accenture plans to integrate Martian into its switchboard services, which assist enterprises in model selection. Since emerging from stealth mode in November 2023, Martian has steadily evolved its technology, now introducing a new AI model compliance feature as part of its routing platform.

The Accenture switchboard has previously facilitated model selection for enterprises, but Martian enhances this capability with dynamic routing, enabling automatic selection of the best model not just per task, but on a query-by-query basis. “This allows for lower costs and higher performance because it means that you don’t always have to use a single model,” explains Shriyash Upadhyay, co-founder of Martian.

Lan Guan, Accenture's chief AI officer, notes that many clients want to harness generative AI while considering performance and cost metrics. “The collaboration between Accenture’s switchboard services and Martian’s dynamic LLM routing streamlines the user experience, enabling enterprises to explore generative AI that aligns with their unique needs,” Guan stated.

How Martian Optimizes AI Query Routing

Martian's model routers adeptly select the optimal AI model for each query, employing core technology aimed at predicting model behavior. Upadhyay emphasizes their unique approach, stating, “We focus on understanding the internals of these models, as a model contains enough information to predict its own behavior.”

This strategy allows Martian to pinpoint the best model for execution, optimizing for factors such as cost, output quality, and latency. Techniques like model compression, quantization, distillation, and specialized models enable these predictions without running the full models, enhancing performance and reducing costs compared to static model options.

The Imperative of Model Routing in Enterprise AI

While the principle of using the best tool for the job is established in business, awareness of diverse AI model options remains a challenge for many organizations. Upadhyay notes, “Often, various parts of large companies remain unaware of the extensive range of specialized models available.”

To effectively utilize AI models, defining success metrics is crucial. Organizations must determine what metrics define success and identify key objectives for specific applications. Cost optimization and return on investment are equally important. According to Upadhyay, model routing addresses both needs effectively.

Compliance also presents a challenge for enterprises, which Martian is addressing with its new compliance feature. This allows companies to vet and approve AI models for application use, with an automated system for establishing compliance policies.

Transforming Agentic AI with Model Routing

Model routing plays a critical role in the burgeoning field of agentic AI, where AI agents chain together multiple models and actions to achieve desired outcomes. Each step in an agent workflow depends on the preceding steps, so errors can compound. Martian’s dynamic routing ensures that the most suitable model is applied at each stage, maintaining high accuracy.

“Agents represent a compelling use case for routing; precision at every step is vital to avoid a cascade of failures,” Upadhyay concludes.