Model routing directs AI tasks to the most appropriate model based on complexity, cost and latency — reducing spend and improving output quality in production systems.
Model routing is the practice of analysing each AI request and directing it to the most capable, cost-appropriate model available — rather than sending every task to a single large model. In a production system handling thousands of requests per day, routing can reduce inference costs substantially while maintaining or improving output quality by matching task complexity to model capability.
A model router sits between the application layer and the pool of available models. When a request arrives, the router evaluates it — typically using a lightweight classifier or a set of rules — and selects the target model. Simple classification tasks, short summarisation, or structured data extraction go to smaller, faster, cheaper models. Complex multi-step reasoning, long-document analysis, or tasks requiring specialised capability go to larger frontier models.
This is analogous to triage in a medical setting: not every patient needs a specialist, and routing common cases to a GP reduces wait times and cost without compromising patient outcomes. The same logic applies to AI workloads.
Without routing, organisations default to sending every request to the largest, most capable model in their stack. This is safe but expensive. Frontier model APIs charge by token, and the difference in cost between a large model and a capable small model for the same simple task can be an order of magnitude.
Gartner predicts that by 2027, inaccurate AI cost and budget calculations will drive 60% of large enterprises to adopt FinOps practices for AI. Model routing is one of the primary mechanisms through which AI cost control becomes practical — it operationalises cost-awareness at the infrastructure level rather than relying on policy alone.
Beyond cost, routing also improves latency. Smaller models respond faster, which matters significantly for real-time customer interactions where a two-second response is acceptable but a six-second response is not.
A model routing layer typically operates as follows:
Classification-based routing: A lightweight model (often a fine-tuned classifier under 1B parameters) evaluates each incoming prompt and assigns it a complexity or task-type label. The router uses that label to select the target model. This adds minimal latency — typically under 50ms.
Rule-based routing: Simpler than a classifier, rules can route based on prompt length, presence of specific keywords or domains, user role, or explicit application-level flags. This is lower maintenance and more predictable but less adaptive.
Cost-aware routing: The router tracks rolling cost per session or per user and can downgrade to smaller models when a budget threshold is approached — useful in internal tooling where spend per employee matters.
Latency-aware routing: During high-load periods, the router can prefer faster, lighter models to maintain response time SLAs, accepting a modest quality trade-off over a significant latency increase.
Common routing targets might include: a small local model (Llama 3, Mistral) for simple classification and filtering; a mid-tier API model (GPT-4o mini, Claude Haiku) for standard summarisation and structured extraction; a frontier model (GPT-4o, Claude Sonnet/Opus) for complex reasoning and nuanced generation.
Model routing adds architectural complexity. Before implementing it, teams should confirm that the quality difference between models is meaningful enough for their use cases to justify the overhead. If 90% of tasks are complex enough to require the frontier model, routing adds cost and latency without proportionate savings.
The most common starting point is a two-tier router: one tier for simple, deterministic tasks and one for everything else. Evaluate a representative sample of real production requests before classifying them — the distribution of task complexity in practice often differs from the distribution assumed during design.
Edison AI's AI implementation team recommends instrumenting routing decisions from the start. Log which model handled which request, the latency, cost, and — where possible — output quality metrics. Without this data, it is impossible to tune the classifier or rules over time as request patterns evolve.
Security and data residency constraints can also determine routing. Tasks involving personally identifiable information under the Privacy Act 1988 may be restricted to specific endpoints with appropriate data processing agreements in place.
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Model routing is the practice of analysing each incoming task and directing it to the most appropriate AI model — balancing capability, latency, and cost rather than sending everything to a single large model.
Simple tasks that do not need a frontier model's full capability are handled by smaller, cheaper models. This can reduce token costs on those tasks by 80–90% while maintaining acceptable output quality.
Common routing criteria include task complexity (estimated by query length or a classifier), required output type (structured data vs prose), latency sensitivity, required context window, and whether the task involves sensitive data that restricts which endpoints can be used.
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Article: Model Routing: Sending Each Task to the Right Model
