What is the best LLM router for cost optimization?

Summary

• LLM routing directs each request to the most cost-efficient model capable of handling that specific task, not just the best model for every request.
• The core strategy is tiered routing: route simple tasks (classification, routing, simple summarization) to small cheap models, route complex reasoning only to expensive top-tier models, and fall back to higher-tier models only if confidence is low.
• Main cost optimization levers: aggressive context trimming, token budgets, response format constraints, caching embeddings and responses, batch processing, and self-hosting quantized open-source models.
• Detailed cost attribution (billing per model, user, project) is essential for optimization: measure cost per inference, cache hit rate, and model tier distribution to identify high-cost flows and improve routing.
• Databricks Unity Gateway provides cost-aware routing policies, per-endpoint budgets, rate limiting, and complete billing attribution for closed-loop optimization.

What is the best LLM router for cost optimization?

The best LLM router is not a specific product; it's a strategy. Route requests to models based on task complexity and cost tolerance, not just capability. A frontier model is excellent for novel reasoning tasks but wastes money on routine classification or summarization. A router solves this by making a real-time decision: "This query is simple enough for a small model; route it there for cost savings."

What is LLM routing?

LLM routing is the practice of directing requests to different models based on predicted task complexity, cost, or quality requirements. A typical routing pipeline has multiple tiers: a small, cheap model for low-complexity tasks; a medium model for retrieval-augmented answers and moderate complexity; and a large, expensive model only for high-value or novel tasks. Some routers add a pre-flight classifier (a tiny model that predicts complexity) to make routing decisions efficiently.

Cost drivers and optimization levers

Understanding what drives LLM cost is the foundation of routing strategy:

Cost Driver	Impact	How to optimize
Model size and provider	Larger models and hosted providers cost more per token.	Use tiered routing: small model for routine tasks, expensive model only when needed.
Output tokens	Inference tokens are the biggest cost factor.	Limit max_tokens; use concise templates; enforce structured output to prevent "rambling."
Context size (input tokens)	More input = higher cost.	Trim conversation history aggressively; retrieve only relevant passages; summarize old turns.
Embeddings calls	Repeated embeddings for the same documents waste cost.	Cache embeddings in a vector store; batch embedding requests.
Duplicate / unbatched requests	No caching = repeated compute cost.	Cache entire responses; batch small requests; use a request deduplication layer.
Overprovisioned compute	Always-on serving clusters cost money.	Use autoscaling; prefer serverless/pay-as-you-go for sporadic workloads.

The routing strategy

The core innovation is multi-tier routing with confidence-based fallback:

1. Pre-flight classification: Before calling any model, use a lightweight classifier to predict task complexity from the prompt (heuristics, length, context). This adds minimal latency and cost.
2. Tiered routing: Route based on predicted complexity:
   • Low complexity: small open-source model or quantized variant
   • Medium complexity: medium model (open or proprietary)
   • High complexity or low-confidence: frontier model
3. Confidence-based fallback: If a cheap model returns low confidence, escalate to a higher tier. Surface the fast answer and send a higher-quality replacement asynchronously if needed.
4. Batch and cache: Aggregate small requests into batches for better throughput. Cache responses for identical queries or canonical prompts.
5. Budget enforcement: Set per-endpoint spending caps and rate limits so one expensive query doesn't run away with the budget.

How Databricks approaches LLM routing and cost optimization

Databricks Unity Gateway is designed as a central control plane for LLM routing and cost governance. Here's how it works:
Cost-aware routing: Define policies that inspect request metadata and route to the appropriate model endpoint. Include fallback chains: try small model; escalate to medium or large if confidence is low.
Model Serving integration: Host multiple models on Databricks Model Serving with autoscaling. Serve both self-hosted quantized models and proxy to external APIs. Unity Gateway makes routing decisions in real-time.
Budget controls: Set monthly spend caps, per-endpoint rate limits, and quota enforcement. When approaching budget limits, throttle or reject requests.
Cost attribution: All requests log to system.billing.usage with model, principal, project, and tokens. Query this to identify high-cost flows and optimize routing.

Cost optimization in practice

Question-answering system: Start with frontier models for all queries. Many are simple FAQs that don't need top-tier reasoning. Add a classifier to detect FAQ-style queries and route them to a small open-source model. Route novel or complex questions to the frontier model. Cost savings come from reduced expensive-model calls.
Content moderation: Pre-filter messages with rules and heuristics (profanity lists, account signals) before calling an LLM. Use a small model for first-pass LLM filtering; escalate borderline cases to a larger model. This dramatically reduces LLM usage and per-decision cost.

FAQs

Does LLM routing degrade quality?

Not if done right. Routing decisions should match task requirements. Route routine requests to small models because they genuinely don't need frontier capability. Route requests requiring deep reasoning to the expensive model. Overall quality is stable; cost is lower.

How do I know if a task is "simple enough" for a small model?

Run an offline benchmark on a representative sample. Send requests to both small and large models; measure accuracy, latency, and cost. If the small model's accuracy meets your SLA, use it for that task class. If not, keep routing to the large model.

Can I implement LLM routing without a specialized platform?

Yes, but it becomes complex: managing endpoints, handling fallbacks, and tracking costs manually is error-prone. Unity Gateway centralizes routing, so policies are defined once and observable globally.

What's the difference between LLM routing and load balancing?

Load balancing distributes requests across identical or similar instances for throughput. LLM routing distributes requests to different models based on cost/quality trade-offs. They're complementary: load balance across instances of the same model, then use routing to pick which model to use in the first place.

How often should I revisit my routing policies?

Monthly or quarterly. As new models emerge (cheaper alternatives, better small models), as your user base grows, or as task complexity shifts, your routing thresholds may need adjustment. Use cost observability to detect these shifts and A/B test new policies in production before rolling out.