The Case for Intelligent Routing
Most LLM applications use a single model for every request. This is the simplest architecture and the right starting point — but it is almost always suboptimal once you have enough production traffic to analyse. The request distribution of any real application is highly heterogeneous: some requests are simple and well-defined (a customer asking about their order status), some are moderately complex (a user requesting a summary of a 10-page document), and some are genuinely hard (a question requiring multi-step reasoning across multiple conflicting sources). Serving all three with the same frontier model means massively overpaying for the simple requests, which may represent 60–70% of your volume.
Intelligent routing — directing each request to the most appropriate model based on its characteristics — captures the cost savings of economy models on simple requests while maintaining the quality of frontier models where they are actually needed. Done well, it reduces total API spend by 40–70% with minimal impact on average quality, and improves quality on hard requests by ensuring they always reach capable models.
What Routing Actually Is
LLM routing sits as a lightweight layer between your application and the model APIs. When a request arrives, the router classifies it — by complexity, topic, required capability, data sensitivity, or latency requirement — and selects the appropriate model from your portfolio. The selected model receives the request and returns the response, which is passed back to the user. The router itself should be fast (under 50ms), cheap (a small model or rule-based classifier), and conservative (when uncertain, route to the better model).
Routing is distinct from load balancing (distributing requests across identical endpoints) and from prompt engineering (changing what you say to the model). It is also distinct from the model tier decision at design time (choosing which model to use for a feature) — routing makes that decision dynamically, per request, based on the characteristics of each specific input.
Routing Signals: What to Route On
Complexity signals. Request length, vocabulary complexity, the presence of multi-step instructions, hedging language that suggests ambiguity, and the number of distinct sub-questions in a single request all correlate with task difficulty. Simple extraction and classification requests tend to be short and unambiguous; complex reasoning requests tend to be longer and more nuanced. A length-based heuristic (route requests above N tokens to a better model) is a crude but surprisingly effective starting point that costs nothing to implement.
Topic and domain signals. For applications covering multiple domains with different quality requirements, topic classification enables domain-specific routing. Medical and legal queries route to frontier models. General product questions route to economy models. Coding questions route to a code-specialised model. This type of routing is easy to implement with keyword matching or a lightweight topic classifier and can produce large quality improvements on high-stakes domains without increasing cost on low-stakes ones.
User tier signals. Free users and paying enterprise customers often have different quality expectations and willingness to accept latency. Routing enterprise users to frontier models and free users to economy models is a straightforward way to differentiate product tiers while managing cost. This requires careful UX communication — users should understand they are on different tiers — but it is a common and legitimate practice.
Data sensitivity signals. Requests containing PII, regulated data, or confidential information route to self-hosted models or providers with appropriate data processing agreements, regardless of the complexity of the request. This routing is not optional for regulated industries — it is a compliance requirement that must be enforced before any quality-based routing decision.
Latency requirement signals. Interactive chat requests that must respond in under 2 seconds route to fast, smaller models. Batch processing requests with no latency constraint route to the cheapest appropriate model regardless of response time. Background tasks route to the Batch API at 50% discount. Latency-based routing is particularly valuable for architectures that serve both real-time and async use cases from the same application.
Figure 1 — LLM Routing Decision Flow
Building a Routing Classifier
The routing classifier is the heart of an intelligent routing system. There are three implementation approaches in increasing order of sophistication. Rule-based routing uses hand-crafted rules: if the request contains certain keywords, exceeds a token length threshold, or falls into a defined topic category, route to a specific model tier. This is the fastest to implement, easiest to debug, and most transparent. It works well when the routing criteria are clear and the input distribution is predictable. Embedding-based routing embeds the request and compares it to labelled examples of easy/medium/hard requests in a vector store, routing based on the difficulty of the nearest neighbours. This captures semantic complexity rather than just surface features and requires a labelled dataset of requests with known appropriate model tiers. LLM-based routing uses a small, cheap model to classify the incoming request: “Is this request simple, moderate, or complex? Respond with one word.” The classifier itself must be fast and cheap enough that its cost does not materially affect the savings from routing. A 7B model or Gemini Flash running locally works well for this role.
Measuring Routing Performance
A routing system has two failure modes: routing too many requests to expensive models (over-routing, which limits cost savings) and routing too many requests to cheap models (under-routing, which degrades quality on hard requests). Measure both. Track the routing decision for every request alongside the quality score of the response — a well-routed system should show high quality scores across all model tiers, because each request reaches an appropriately capable model. A pattern where economy-tier responses have lower quality scores than mid-tier or frontier responses indicates under-routing: some complex requests are being incorrectly classified as simple. A pattern where frontier-tier usage is higher than expected without quality improvement indicates over-routing: requests are reaching frontier models that mid-tier would have handled equally well. Tune your classifier thresholds based on these measurements — routing is not a set-and-forget system but an iteratively calibrated one.
Open-Source Routing Tools
Several open-source projects implement LLM routing with different trade-offs. RouteLLM (from LMSYS) provides a research-validated routing framework with pre-trained classifiers that route between strong and weak models based on predicted quality. LiteLLM includes routing and load-balancing across providers with fallback support. OpenRouter provides a managed routing service that abstracts provider selection based on price, availability, and capability. For teams that want to get started quickly without building a custom classifier, these tools provide a reasonable starting point — evaluate their routing decisions against your specific task distribution before relying on them in production, since their default classifiers are trained on general benchmarks that may not reflect your application’s request characteristics.
Fallback and Failover Routing
Routing systems should include failover logic for when a provider experiences outages, rate limit exhaustion, or elevated error rates. A fallback chain — if the primary model returns an error or times out, route to the next model in the chain — prevents provider outages from causing application downtime. Implement with exponential backoff: on first failure, retry once on the same provider; on second failure, fail over to the next provider in the chain; log every failover event for incident analysis. The fallback chain should be tested deliberately — simulate provider failures in your staging environment and verify that failover works correctly before relying on it in production.
The Economics: Quantifying the Saving
The business case for routing investment is straightforward to model. If 70% of your requests are classifiable as simple and routable to an economy model at one-tenth the cost, and 20% are moderate routable to a mid-tier model at one-third the cost, and only 10% require frontier model quality, your average cost per request drops dramatically. Specifically: if your current average cost is $0.01 per request using a frontier model, a routing system that achieves the above distribution reduces average cost to approximately $0.001 + $0.00067 + $0.001 = $0.0027 per request — a 73% reduction. At 1 million daily requests, this saves roughly $7,300 per day or $2.7 million annually. The engineering investment to build and maintain a routing system — typically 2–4 weeks of engineer time plus ongoing calibration — recovers within days at this volume. Even at 100,000 daily requests, the annual saving of $270,000 justifies a meaningful engineering investment. Calculate your specific numbers before deciding whether routing is worth prioritising — the business case is often more compelling than expected.
Implementation Sequence
Build routing incrementally rather than attempting to implement a sophisticated multi-signal classifier from the start. Begin with a single rule-based routing decision on your highest-volume, most uniform task type — if 40% of your requests are a well-defined task that a cheap model handles reliably, route those specifically and measure the quality and cost impact. Expand to a second routing decision once the first is validated. After two or three validated routing rules, you have the quality data and engineering patterns needed to build a lightweight ML classifier that handles the ambiguous middle cases more accurately than rules alone. This incremental approach produces real cost savings within days of starting, rather than waiting weeks for a complete routing system before seeing any return. The data collected from simple routing also provides the labelled examples needed to train the more sophisticated classifier, creating a virtuous cycle where each step makes the next one better informed.
Routing done right is one of the compounding engineering investments in LLM infrastructure — it gets more accurate as you gather more data, saves more money as your volume grows, and improves quality on your hardest requests by ensuring they always reach capable models.