Multi-model routing

Multi-model routing places a decision layer between your application and the models. Each request is classified, by rules, a small classifier or the task's origin, and dispatched to the model that fits: a small, cheap model for classification and simple replies, a larger one for reasoning and drafting, an open-weight model on your own infrastructure for sensitive data, a fallback provider when the primary is down or rate-limited.

The router depends on a consistent interface (same prompt structure, same structured output contract) across models, and on evals that tell you which model clears the quality threshold for each task. Without evals, routing is guessing; with them, it is a measured trade between cost, speed and accuracy that can be revisited as prices and models change.

Routing is not the same as ensembling (asking several models and combining answers), and it is not a licence to ignore quality. It is also not only about cost: resilience against outages and model deprecations is often the stronger reason to adopt it.

• CFO: routing is the biggest structural lever on inference spend, often without a visible quality change.
• CTO / Head of Engineering: removes single-vendor dependency and makes provider outages and deprecations a configuration change.
• CISO / Compliance officer: lets sensitive requests stay on a private model while general ones use a hosted API.
• Product manager: allows different latency and quality profiles per feature without separate codebases.

Multi-model routing exists because no single model is best at everything, and pinning a product to one vendor exposes it to price changes, outages and retirements. Routing lets you use the cheapest adequate model per task, keep regulated data on private infrastructure, and switch providers when the benchmark shifts. The trade-off is added complexity: a router, per-model prompts and tests, and the discipline to keep evals current. Eazyware builds model-agnostic by default because the alternative is a product whose economics and uptime belong to someone else.

• A SaaS copilot that classifies intents on a small model and drafts long answers on a larger one.
• A bank's assistant that routes anything containing customer PII to a self-hosted model inside the VPC.
• A support platform that fails over to a second provider automatically during an API outage.
• A retailer's catalogue pipeline that uses a cheap model for bulk tagging and a stronger one for ambiguous items.
• A multilingual voice agent that picks the model with the best benchmark for each Indian language.

Technique / practiceAn architectural technique and a core part of Eazyware's model-agnostic stance. Every LLM application and private agentic AI build includes a routing layer with per-model evals so the choice stays yours.

Eazyware service that covers it: LLM Application Development. Starting prices are on the pricing page.