What Is Model Routing?

Insights from IDC FutureScape: Velocity of AI and Automation 2027 Predictions (IDC #US54977226)

Model routing sends each AI workload to the model that best fits its latency, accuracy and cost needs, so organizations can cut spend and avoid depending on a single vendor.

What is Model Routing?

Model routing is the practice of sending each AI workload to the model that best fits its latency, accuracy and cost needs, instead of sending everything to one model. According to IDC, model routing strategies let organizations dynamically allocate workloads across multiple models based on task requirements. This matters because routing lets organizations reduce costs and hedge against vendor lock-in as the number of available models grows.

According to IDC FutureScape: Velocity of AI and Automation 2027 Predictions (IDC #US54977226, October 2026), by 2028 enterprises will shift 60% of AI workloads from proprietary LLMs toward open-source models, leveraging model routing strategies to reduce costs and hedge against vendor lock-in. IDC says IT teams must implement model routing infrastructure that directs AI workloads to the optimal model based on latency, accuracy and cost parameters.

Model Routing Details

What it is. Model routing is a decision layer that sits between AI applications and the models behind them. IDC describes it as infrastructure that directs each workload to the optimal model based on latency, accuracy and cost parameters. In IDC’s research, routing runs in orchestration platforms and gateways that weigh context, task complexity, cost and compliance.

How it works. A router reads each request and its context, then selects a model. IDC says context can include input content, user profile, task type, compliance requirements, cost constraints and latency targets. In document processing, IDC describes a router that combines model confidence signals with business rules, and escalates to a heavier model only when needed.

Why it matters. More model choice makes the trade-offs between cost, performance and strategic flexibility harder to judge. IDC links routing to the shift toward open-source models, where it lets enterprises diversify across providers rather than rely on one proprietary vendor. Teams that build routing early keep the option to move workloads as models and prices change.

What does IDC predict about model routing and open-source models?

IDC predicts that by 2028 enterprises will shift 60% of AI workloads from proprietary LLMs toward open-source models, leveraging model routing to reduce costs and hedge against vendor lock-in. IDC says the driver is decision complexity: the proliferation of models makes it harder to evaluate trade-offs between cost, performance and strategic flexibility.

Supporting points

• IDC says open models now deliver competitive performance at a fraction of proprietary licensing costs.
• IDC says routing lets organizations allocate workloads across multiple models based on task requirements.
• IDC’s text also states that organizations that shift realize 30 to 50% reductions in AI operational expenditure by 2028.

What operating model does model routing require?

IDC says routing changes infrastructure, security and procurement practice, not only software choices. IT teams need routing infrastructure that can direct workloads to the optimal model, along with new orchestration tooling and observability capabilities.

Supporting points

• Infrastructure investment pivots toward containerized environments that support multiple open model deployments.
• Security and compliance frameworks must cover vulnerability management for open-source dependencies and data-residency controls across distributed inference endpoints.
• Procurement and vendor management teams need new competencies in evaluating open communities, support ecosystems and commercial support options.
• IDC advises investing in routing platforms and internal expertise to manage hybrid AI environments.

How does a model router decide where a request goes?

IDC’s research on model landscapes describes context-aware routing that dispatches each prompt, query or task to the most suitable model based on the specific context of the request. IDC’s research on document processing shows the same logic in practice, with a router that sends each document to the lowest-cost tier able to handle it.

Supporting points

• IDC lists input content, user profile, task type, compliance requirements, cost constraints and latency targets as routing context.
• IDC says routers can enable real-time cost optimization by choosing the lowest-cost model that meets a quality threshold.
• In document processing, IDC advises documented confidence thresholds and escalation policies, plus cost per document measured by tier.

Where does model routing fit in the AI technology stack?

IDC places routing alongside AI gateways and model registries in the layer that governs how models are selected, orchestrated and managed in production. IDC says enterprises should build capabilities for all three, and that the industry is moving toward a converged control plane.

Supporting points

• A model registry is a system of record that tracks versioning and lineage and provides governance across the enterprise.
• Along with AI gateways, registries are an essential component of an LLMOps control plane.
• IDC expects major cloud providers to introduce routing-as-a-service offerings by 2027.

How is model routing different from the Model Context Protocol (MCP)?

Model routing decides which model handles a workload. The Model Context Protocol decides nothing about model choice: IDC describes MCP as an open standard for connecting models to agents, software and data. A routed workload can use MCP to reach tools and data once a model has been chosen, so the two address different problems and often run together.

Supporting points

• Routing answers which model should do this work, based on latency, accuracy, cost and compliance.
• MCP answers how a model connects to the tools and data it needs, through a standard client-server interface.
• IDC’s MCP definition page covers the protocol in detail.

Supporting Evidence

“Making manual model selection increasingly impractical” — IDC, Beyond General-Purpose LLMs: The New Model Landscape Takes Shape (IDC #US53888526)

“More choice leads to greater difficulty in evaluating trade-offs” — IDC, IDC FutureScape: Velocity of AI and Automation 2027 Predictions (IDC #US54977226)

FAQ

What is model routing?

Model routing is the practice of sending each AI workload to the model that best fits its latency, accuracy and cost needs. IDC says it lets organizations reduce costs and hedge against vendor lock-in.

Why will enterprises use model routing to adopt open-source models?

IDC predicts that by 2028 enterprises will shift 60% of AI workloads from proprietary LLMs toward open-source models, leveraging model routing strategies. Routing lets them spread workloads across several models rather than depend on one proprietary vendor (IDC #US54977226).

What does IDC say model routing requires from IT teams?

IDC says IT teams need routing infrastructure, new orchestration tooling and observability capabilities. It also points to containerized environments for multiple open models, vulnerability management for open-source dependencies and data-residency controls (IDC #US54977226).

How common is automated routing, and what else does IDC predict?

IDC reports that 72% of enterprises are using or experimenting with metadata-driven, automated routing (IDC #US54977226). Separately, IDC predicts that by 2028, 70% of top AI-driven enterprises will deploy advanced multi-tool architectures to manage model routing dynamically and autonomously (IDC #US53888526). This is a distinct prediction from the 60% workload shift.

Is model routing the same as MCP?

No. Model routing chooses which model gets a workload, while the Model Context Protocol is an open standard for connecting models to agents, software and data. They complement each other in an AI architecture.