LLM Gateway

Top 5 LLM Routing Techniques

TL;DR

LLM routing is the process of intelligently directing queries to the most appropriate model based on factors like complexity, cost, latency, and domain expertise. This guide covers the top 5 routing techniques that production teams use to optimize their AI infrastructure:

Semantic Routing - Uses embedding-based similarity matching to route queries based on meaning and intent
Cost-Aware Routing - Optimizes the cost-quality tradeoff by dynamically selecting between expensive frontier models and cheaper alternatives
Intent-Based Routing - Analyzes query complexity, domain, and structure to select specialized models
Cascading Routing - Progressive escalation through model tiers, starting cheap and escalating only when needed
Load Balancing - Distributes requests across providers and API keys for reliability and throughput

Implementing these techniques through a production-grade LLM gateway like Bifrost can reduce costs by up to 85% while maintaining 95% of top-tier model performance. This guide explores each technique with practical implementation strategies, real-world trade-offs, and production insights.

The LLM landscape has evolved dramatically. What started with a single dominant model has exploded into an ecosystem of hundreds of specialized models, each with different capabilities, costs, and latencies. Teams building production AI applications now face a critical infrastructure challenge: how do you route each query to the right model without sacrificing quality or breaking the budget?

The answer lies in intelligent routing strategies that analyze query characteristics and dynamically select the optimal model for each request. At Maxim AI, we built Bifrost as a high-performance LLM gateway specifically designed to implement these routing patterns at scale. Through thousands of production deployments, we've identified the five most effective routing techniques that balance cost, quality, and reliability.

This guide explores each technique in depth, providing implementation strategies, real-world trade-offs, and practical insights for building robust AI infrastructure.

Understanding LLM Routing: Why It Matters

Before diving into specific techniques, it's important to understand why routing matters. The problem is deceptively simple: you have multiple LLMs available, and you need to decide which one should handle each request.

But this decision has cascading implications. According to research from LMSYS, the creators of RouteLLM, intelligent routing can reduce costs by over 85% on benchmarks like MT Bench while still achieving 95% of GPT-4's performance. More powerful models cost significantly more per token but aren't always necessary for every query. A simple question about business hours doesn't need the same reasoning capability as a complex code generation task.

The challenge intensifies in production. You're not just optimizing for cost. You're managing rate limits across providers, handling failovers during outages, balancing load across API keys, and ensuring consistent latency for user-facing applications. The routing layer becomes a critical piece of infrastructure, and getting it right can make the difference between a scalable AI application and one that collapses under its own complexity.

1. Semantic Routing: Understanding User Intent Through Embeddings

What it is: Semantic routing uses embedding models to convert user queries into vector representations, then matches them against reference prompts to determine the most appropriate model or workflow.

How It Works

Instead of analyzing the literal text of a query, semantic routing understands meaning. When a user asks "What are your operating hours?" and later asks "When do you close?", traditional keyword-based routing would treat these as different queries. Semantic routing recognizes they're asking the same thing.

The process follows these steps:

Convert the incoming query into an embedding vector using a model like OpenAI's text-embedding-3-small or open-source alternatives
Calculate similarity (typically cosine similarity) between the query embedding and pre-defined reference prompt embeddings
Route to the model associated with the highest-similarity reference category

The vLLM Semantic Router project demonstrates this pattern at scale, using ModernBERT for classification and achieving high-performance routing with minimal latency overhead.

When to Use Semantic Routing

Semantic routing excels when you have:

Distinct task categories: Customer support, technical documentation, creative writing, code generation
Specialized models: Domain-specific fine-tuned models that excel at particular types of queries
Cost optimization targets: Clear performance/cost tradeoffs where cheaper models handle 70% of queries adequately

At Bifrost, we see teams use semantic routing most effectively when they have 3-10 distinct categories of queries. Beyond that, the maintenance overhead of curating reference prompts becomes burdensome.

Implementation in Bifrost

Bifrost implements semantic caching, a related technique that uses embeddings to identify semantically similar queries and return cached responses. This reduces both latency and cost without compromising quality. The same embedding infrastructure can be extended to power semantic routing:

// Bifrost's semantic similarity calculation
similarity := cosineSimilarity(queryEmbedding, cachedEmbedding)
if similarity > threshold {
    return cachedResponse
}

For teams wanting to implement semantic routing on top of Bifrost, you can create virtual keys for different categories and route programmatically based on your embedding similarity scores.

Trade-offs

Advantages:

Handles query variations naturally ("What's the refund policy?" vs "How do I return items?")
Fast inference after initial embedding generation
Scales to large numbers of categories with vector databases

Limitations:

Requires curating high-quality reference prompt sets
Embedding generation adds latency (typically 20-50ms)
Accuracy depends on coverage of reference categories

2. Cost-Aware Routing: Optimizing the Price-Performance Frontier

What it is: Cost-aware routing dynamically selects between stronger, more expensive models and weaker, cheaper models based on predicted query difficulty, optimizing for cost while maintaining quality thresholds.

The Economics of Model Selection

The cost differential between frontier models and smaller alternatives is substantial. According to current OpenAI pricing, GPT-4o costs $2.50 per million input tokens, while GPT-4o-mini costs just $0.15. That's a 16x difference. If you can route even 60% of queries to the cheaper model without quality degradation, you've cut your LLM costs by more than half.

The insight behind cost-aware routing is that most queries don't need frontier-model reasoning. Research from the RouteLLM paper shows that on benchmarks like GSM8K (grade school math problems), routing can achieve 95% of GPT-4's performance while using GPT-4 for only 14% of queries.

Routing Strategies

Cost-aware routing implementations typically use one of these approaches:

1. Preference-based routing: Train a lightweight classifier on human preference data that predicts which of two models (strong vs. weak) would give a better response. RouteLLM's matrix factorization router uses this approach, learning from Chatbot Arena data.

2. Confidence-based routing: Route to stronger models only when the cheaper model returns low-confidence responses. This works well when models can reliably estimate their own uncertainty.

3. Rule-based routing: Use heuristics like query length, presence of technical terms, or explicit complexity indicators to make routing decisions.

Production Implementation at Bifrost

Bifrost's automatic fallback system provides the infrastructure for cost-aware routing. You can configure primary and fallback models with different cost profiles:

{
  "providers": {
    "openai": {
      "keys": [{
        "models": ["gpt-4o-mini", "gpt-4o"],
        "weight": 1.0
      }]
    }
  }
}

For teams implementing sophisticated cost-aware routing, Bifrost's governance features let you track costs at the virtual key level, making it easy to measure ROI of different routing strategies.

The key is continuous monitoring. What works for one application may not work for another. Teams using Bifrost with Maxim's observability platform can track quality metrics alongside cost metrics, ensuring routing optimizations don't degrade user experience.

Real-World Results

When AWS implemented multi-LLM routing strategies, they documented significant cost reductions. Their analysis showed that semantic routing combined with cost-aware fallbacks could reduce infrastructure costs by 30-40% without compromising accuracy.

At Bifrost, we've seen similar results. One customer support platform reduced their monthly LLM spend from $42,000 to $18,000 by routing simple queries to Claude 3.5 Haiku and complex escalations to Claude 3.5 Sonnet, achieving the same CSAT scores.

3. Intent-Based Routing: Analyzing Query Complexity and Domain

What it is: Intent-based routing examines the structure, complexity, and domain of a query to select the most appropriate model, considering factors beyond just cost.

Beyond Simple Classification

While semantic routing groups queries by topic, intent-based routing goes deeper. It asks: What is the user trying to accomplish? How complex is this task? Does it require specialized knowledge?

Modern intent-based systems extract multiple signals from queries:

Domain signals: Is this a medical question, legal query, or coding task?
Complexity signals: Does this require multi-step reasoning or is it a simple lookup?
Format signals: Is the user asking for code, a summary, creative writing, or structured data?
Safety signals: Does this query contain PII, potential jailbreak attempts, or harmful content?

The vLLM Semantic Router v0.1 showcases this approach with six types of signal extraction, from MMLU-domain classification to toxicity detection.

The Multi-Signal Approach

Rather than relying on a single classifier, production-grade intent-based routing often uses an ensemble:

Fast heuristics (query length, keyword presence) for initial filtering
Lightweight classifiers (BERT-based models) for domain categorization
Safety checks for PII detection and content filtering
Confidence scoring to determine if escalation is needed

This layered approach balances accuracy with latency. The fast heuristics run in microseconds, while more expensive classification only happens when needed.

Routing Logic Example

Here's how an intent-based router might process a query:

Query: "Write a Python function to calculate Fibonacci numbers recursively"

Signals Extracted:
- Domain: Programming (code generation)
- Complexity: Medium (requires understanding recursion)
- Format: Code
- Safety: Clear
- Estimated tokens: 150-300

Routing Decision:
→ Route to code-specialized model (GPT-4 Turbo or Claude 3.5 Sonnet)
→ Reason: Code generation benefits from strong reasoning models

Integration with Bifrost

Bifrost's drop-in replacement architecture makes it easy to implement intent-based routing at the application layer. Your application analyzes the query, sets the model parameter accordingly, and Bifrost handles the provider communication:

# Application-level intent analysis
intent = analyze_query(user_query)

# Route based on intent
if intent.domain == "code" and intent.complexity > 0.7:
    model = "anthropic/claude-3-5-sonnet-20241022"
else:
    model = "anthropic/claude-3-5-haiku-20241022"

# Bifrost handles the rest
response = client.chat.completions.create(
    model=model,
    messages=[{"role": "user", "content": user_query}]
)

Because Bifrost provides a unified interface across providers, switching between Anthropic, OpenAI, and other providers requires no code changes beyond the model string.

4. Cascading Routing: Progressive Model Escalation

What it is: Cascading routing starts with the smallest, cheapest model and progressively escalates to more capable models only when necessary, based on quality checks or confidence thresholds.

The Waterfall Pattern

Think of cascading routing as a waterfall. Water (queries) flows down the easiest path first. Only when that path fails does it cascade to the next level. In LLM routing:

Start with the cheapest, fastest model
Evaluate the response quality
If quality is insufficient, retry with a stronger model
Continue until quality threshold is met or you reach the most capable model

IBM Research's LLM router demonstrates this pattern, routing queries through a library of 11 models and outperforming individual models while maintaining cost efficiency.

Quality Evaluation Methods

The critical challenge in cascading routing is determining when to escalate. Common approaches include:

Self-consistency checks: Generate multiple responses from the cheap model. If they disagree significantly, escalate to a stronger model.

Confidence scoring: Many models can estimate their own uncertainty. Low confidence triggers escalation.

Rule-based validation: For structured outputs (JSON, code), validate syntax. For factual queries, check against knowledge bases.

LLM-as-judge: Use a small, fast model to evaluate the response quality from the primary model.

Production Considerations

Cascading routing introduces additional latency since you may need to call multiple models before getting a satisfactory response. For latency-sensitive applications, this is problematic.

Bifrost addresses this through automatic failovers that operate at the provider level rather than the quality level. When a provider is slow or unavailable, Bifrost immediately routes to a fallback. This is infrastructure cascading rather than quality cascading.

For quality-based cascading, teams typically implement it at the application layer:

def cascade_query(query):
    # Try cheap model first
    response = query_model("openai/gpt-4o-mini", query)

    if is_high_quality(response):
        return response

    # Escalate to stronger model
    response = query_model("openai/gpt-4o", query)
    return response

Bifrost's observability features help you measure how often escalation occurs and whether it's actually improving quality enough to justify the cost.

When Cascading Works Best

Cascading routing is most effective for:

Question answering systems where correctness can be validated
Code generation where syntax checking provides clear quality signals
Structured data extraction where schema validation works as a quality gate

It's less suitable for:

Real-time chat where latency matters more than cost
Creative tasks where quality is subjective
High-stakes decisions where you want the best model from the start

5. Load Balancing and Weighted Routing: Ensuring Reliability at Scale

What it is: Load balancing distributes requests across multiple providers, API keys, and model instances to optimize for reliability, throughput, and cost.

Beyond Simple Round-Robin

While semantic and cost-aware routing optimize which model to use, load balancing optimizes how to distribute requests across multiple instances of the same model or multiple providers offering similar models.

The simplest approach is round-robin: distribute requests evenly across available backends. But production systems need more sophistication:

Weighted distribution: Send 70% of traffic to Provider A (faster) and 30% to Provider B (cheaper backup).

Adaptive load balancing: Adjust weights based on real-time performance metrics like latency, error rates, and rate limit proximity.

Least-connections routing: Send requests to the provider currently handling the fewest active requests.

Geographic routing: Route based on user location to minimize latency.

The Bifrost Approach

Bifrost implements adaptive load balancing as a core feature. When you configure multiple API keys or providers, Bifrost automatically distributes load based on configurable weights:

{
  "providers": {
    "openai": {
      "keys": [
        {
          "name": "openai-key-1",
          "value": "sk-...",
          "weight": 0.7,
          "models": ["gpt-4o"]
        },
        {
          "name": "openai-key-2",
          "value": "sk-...",
          "weight": 0.3,
          "models": ["gpt-4o"]
        }
      ]
    }
  }
}

This weights system enables several patterns:

Blue-green deployments: Route 95% of traffic to the stable provider, 5% to a new provider you're testing.

Rate limit management: Distribute across multiple API keys to stay under per-key rate limits.

Cost optimization: Route most traffic to the cheapest provider, using premium providers as overflow.

Geographic distribution: Weight providers based on user location for lower latency.

Health Monitoring and Circuit Breaking

Load balancing only works if you can detect and route around failures. Bifrost implements health monitoring that tracks:

Success rate per provider
Average latency per provider
Rate limit errors
Connection timeouts

When a provider's health degrades, Bifrost's circuit breaker temporarily reduces its weight or removes it from the pool entirely, giving it time to recover. This is critical for production reliability.

Teams using Bifrost typically configure fallback chains like:

Primary: Claude 3.5 Sonnet (Anthropic)
Fallback 1: GPT-4o (OpenAI)
Fallback 2: Gemini 2.0 (Google)

During normal operation, all requests go to Claude. If Anthropic experiences an outage or throttling, Bifrost automatically fails over to OpenAI without application code changes.

Real-World Impact

The importance of load balancing becomes clear during provider incidents. When OpenAI experienced outages in late 2024, applications with hard dependencies on OpenAI went down. Applications using Bifrost's automatic failover stayed online, seamlessly routing to Anthropic or Google.

One e-commerce chatbot we work with handles 50,000 requests per day. Before implementing load balancing with Bifrost, they experienced 2-3 outages per month when hitting OpenAI rate limits. After configuring weighted distribution across OpenAI, Anthropic, and AWS Bedrock, they achieved 99.97% uptime over six months.

Combining Routing Techniques: The Production Pattern

In practice, production systems rarely use a single routing technique. The most robust implementations layer multiple strategies:

Layer 1: Load Balancing

Distribute requests across healthy providers and API keys. This runs for every request and provides baseline reliability.

Layer 2: Cost-Aware Routing

Select between model tiers (Haiku vs Sonnet, Mini vs Standard) based on query complexity. This optimizes the cost-quality tradeoff.

Layer 3: Semantic/Intent Routing

Route specialized queries (code, medical, legal) to domain-specific models. This maximizes quality for critical tasks.

Layer 4: Cascading Validation

For high-stakes queries, validate responses and escalate to stronger models when needed. This provides a quality safety net.

Implementation Architecture

Here's how this looks with Bifrost:

Application
    ↓
[Query Analysis] → Determine complexity & domain
    ↓
[Model Selection] → Choose appropriate tier
    ↓
Bifrost Gateway
    ↓
[Load Balancing] → Distribute across healthy providers
    ↓
[Provider] → Execute request
    ↓
[Response Validation] → Check quality
    ↓
[Escalation?] → Retry with stronger model if needed
    ↓
Application

Bifrost handles layers 1 and the infrastructure for layers 2-4, while your application implements the business logic for query analysis and quality validation.

For teams wanting end-to-end visibility, integrating Bifrost with Maxim's observability platform provides distributed tracing across the entire routing decision tree. You can see exactly which routing logic triggered for each request and measure its impact on cost and quality.

Measuring Success: Metrics That Matter

Implementing routing techniques is only valuable if you measure their impact. Key metrics to track include:

Cost Metrics:

Total LLM spend (absolute and per-request)
Cost distribution across models
Savings from routing optimizations

Quality Metrics:

Task success rate by routing path
User satisfaction scores
Error rates and escalation frequency

Performance Metrics:

End-to-end latency (including routing overhead)
Time to first token
Provider uptime and failover frequency

Bifrost's built-in observability provides Prometheus metrics for latency, throughput, and error rates. When paired with Maxim's evaluation framework, you can run systematic A/B tests comparing routing strategies and measure their impact on real user outcomes.

Getting Started with Intelligent Routing

If you're building AI applications and want to implement these routing techniques:

1. Start with load balancing. Even without sophisticated routing logic, distributing across providers prevents single points of failure. Get Bifrost running in 30 seconds and configure multiple providers.

2. Add cost-aware routing for high-volume, low-stakes queries. Identify query types where cheaper models work 80% of the time. Route those to smaller models and measure quality impact.

3. Implement semantic or intent-based routing for specialized domains. If you have distinct query categories, create routing logic based on embeddings or classifiers.

4. Layer in cascading validation for critical paths. For queries where correctness matters more than cost, validate responses and escalate when needed.

5. Monitor everything. Use Maxim's platform to track quality, cost, and latency across routing strategies.

Conclusion

LLM routing is no longer a nice-to-have optimization. As AI applications scale, intelligent routing becomes infrastructure. The five techniques covered here represent different trade-offs between cost, quality, latency, and complexity.

Semantic routing optimizes for domain expertise and handles query variations naturally. Cost-aware routing maximizes the price-performance frontier. Intent-based routing considers the full context of what users are trying to accomplish. Cascading routing provides progressive quality gates. Load balancing ensures reliability at scale.

The most successful production systems combine these techniques, using load balancing as a foundation and layering in cost and quality optimizations based on business requirements.

At Maxim AI, we built Bifrost to provide the infrastructure layer that makes these routing patterns practical. With automatic failovers, semantic caching, unified provider access, and production-grade observability, Bifrost handles the complexity of multi-provider routing so you can focus on building great AI applications.

Whether you're just getting started with LLM routing or optimizing an existing system, the techniques outlined in this guide provide a roadmap for building reliable, cost-effective AI infrastructure.

Ready to implement intelligent routing in your application? Get started with Bifrost or schedule a demo to see how Maxim's full-stack platform can accelerate your AI development.

Further Reading: