What are the best LLM routing solutions?

Summary

• LLM routing intelligently directs requests to the most suitable model based on task complexity, cost, and performance tradeoffs, rather than always using a single model.
• Effective routing solutions evaluate request characteristics in real-time and can dynamically select from open-source, proprietary, and specialized models to optimize for latency, accuracy, or cost.
• An effective router handles fallback scenarios when primary models are unavailable, throttled, or returning poor-quality outputs.
• Databricks Unity Gateway provides model-agnostic routing capabilities that work with any LLM deployed on your infrastructure or accessed through APIs.
• The best routing strategy depends on your workload profile: simple queries benefit from lightweight models, while complex reasoning tasks may require larger, slower models.

What are the best LLM routing solutions?
LLM routing is the practice of directing requests to different language models based on their characteristics, requirements, and cost-performance tradeoffs. Rather than relying on a single model for all queries, routing solutions examine each request and decide which model is best suited to handle it. This approach can reduce costs, improve response times, and match task complexity to model capability.
Good routing isn't magic. It's a deliberate decision about three factors: the complexity of the incoming query, the capabilities of available models, and your optimization goals (speed, accuracy, or cost). A well-designed router can dramatically improve both user experience and operational efficiency by avoiding expensive model calls when a faster option suffices.

What is LLM routing?

LLM routing is an architectural pattern that sits between your application and multiple language models. When a user or system submits a request, the router evaluates that request and determines which model should process it. This decision happens in milliseconds and is based on rules, heuristics, or learned patterns about what each model does well.
Routine tasks like spelling correction, simple classification, or formatting might be handled by smaller, faster models. Nuanced reasoning, creative work, or multi-step problem-solving would route to larger, more capable models. When a model is unavailable or returns poor results, the router can fall back to an alternative. This is fundamentally different from the common practice of using the most capable model for every request regardless of need.

Key capabilities to evaluate

Capability	Why it matters
Multi-model support	Can the solution route requests to multiple different models from different providers?
Smart decision logic	Does it use rule-based conditions, heuristic scoring, or learned patterns to make routing decisions?
Fallback and retry	Can it gracefully fall back to another model if the primary choice fails or is throttled?
Cost visibility	Does it expose the cost of each routing decision so you can optimize?
Latency optimization	Can it prioritize faster models for latency-sensitive workloads?
Model-agnostic design	Will it work with any LLM: your own deployments, open-source models, or third-party APIs?
Observability	Can you track which models are used, why, and whether routing decisions are effective?
Token counting	Does it estimate token counts before sending a request, to predict cost and route accordingly?

How Databricks approaches LLM routing

Databricks Unity Gateway provides guardrails and routing capabilities that let you govern and direct LLM traffic across your entire organization. Within Unity Gateway, you can define routing rules based on request metadata, token budgets, or model availability. The gateway sits in front of your LLM infrastructure and enforces consistent policies across all consumers.
Unity Gateway's routing layer works whether your models are deployed on Databricks, running in your own data center, or accessed through external APIs. You can mix and match: route simple tasks to a local open-source model, send complex queries to a proprietary model, and use fallback rules to handle service disruptions. The unified governance layer means every routing decision is logged and auditable, and policies are enforced consistently across teams.
Databricks Agent Bricks, the framework for building autonomous agents, integrates with Unity Gateway's routing and guardrails out of the box. When agents need to call an LLM, they respect the organization's routing policies and fallback rules. This ensures agents operate within budgets and risk controls defined by your platform team.

Use cases

Cost-optimized inference: Route straightforward customer support questions to a smaller, cheaper model, and escalate complex escalations to a more capable model only when needed.
Latency-sensitive applications: Prioritize faster models for user-facing features like autocomplete or recommendations, while using more capable models for background analytics where speed is less critical.
Load balancing: Distribute requests across multiple instances of the same model to avoid overwhelming any single deployment and ensure availability.
High-reliability systems: Define fallback chains so that if your primary model is down or throttled, requests automatically route to a backup. This is critical for production customer-facing workloads.
Multi-tenant governance: Route different customer segments or teams through different models based on their subscription tier, risk profile, or budget allocation.
A/B testing and canary deployments: Route a small percentage of traffic to a new model to gather performance data before full rollout.

FAQs

How does routing differ from simple load balancing?

Load balancing distributes traffic evenly across identical endpoints. Routing makes intelligent decisions about which endpoint to use based on request characteristics. Routing may send all requests to one model if it determines that's optimal, or dynamically select different models for different requests.

Can I use routing with open-source models I host myself?

Yes. A good routing solution is model-agnostic and works with any LLM you can call via an API. You can route to open-source models running on your Databricks cluster, commercial models via external APIs, or both.

What's the typical latency overhead of a router?

A well-designed router should add negligible latency to your request path. It makes a routing decision in milliseconds, typically far less than the time it takes to actually call an LLM. The overhead is a small percentage of total end-to-end latency.

Should I route based on cost alone?

Cost is one factor, but not the only one. A cheaper model that fails to answer a complex question costs you far more in user frustration and rework. Effective routing balances cost, quality, and latency based on your specific requirements.