How to Set Up an AI Model Router for Your LLM Stack

Understanding AI Model Routing

Your LLM bill just hit $50,000 for the month. You're sending every request to GPT-4, regardless of whether the user asked "What is 2+2?" or needs help writing a complex legal document. This approach works, but it's expensive and inefficient.

An AI model router solves this problem. It analyzes each incoming request and sends it to the most appropriate model in your stack. Simple questions go to fast, cheap models. Complex tasks go to powerful, expensive ones. The result is lower costs, better performance, and more reliable service.

Most teams see 30-50% cost reduction after implementing intelligent routing. Some achieve savings up to 85% for specific workloads. Beyond cost, routing improves latency by matching request complexity to model capability. A small model can answer basic questions faster than a large one.

Setting up a model router requires making decisions about which models to use, how to classify requests, and when to escalate to more powerful options. This guide walks through the entire process, from choosing your architecture to monitoring performance in production.

Why Model Routing Matters in 2026

The LLM landscape has changed. In 2023, most teams picked one model and stuck with it. By 2026, that approach no longer makes sense.

First, model providers have frequent outages. OpenAI reports 99.8% uptime, which sounds good until you realize that's over 3 hours of downtime per month. Anthropic reports 99.58% uptime. If you rely on a single provider, these outages directly impact your users.

Second, different models excel at different tasks. Claude performs well on complex reasoning. GPT-4 handles general queries effectively. Smaller models like Llama or Mistral work great for simple classification tasks at a fraction of the cost. Using the right model for each task improves both quality and efficiency.

Third, costs vary dramatically between models. GPT-4 costs $0.03 per 1,000 tokens. Smaller models cost $0.001 per 1,000 tokens or less. If 60% of your requests can be handled by a smaller model, you're wasting money on every one you send to the expensive option.

Fourth, new models emerge constantly. Gemini, Claude, and open-source alternatives release improved versions every few months. A routing layer lets you test new models on a subset of traffic without rewriting your application code.

Core Components of a Model Router

A functional model router includes several key components working together.

Request Analyzer

The request analyzer examines incoming queries and extracts relevant features. These features determine which model should handle the request.

Common features include prompt length, detected language, query type, user tier, and estimated complexity. Some systems use a small classifier model to categorize requests. Others use rule-based logic or embeddings.

The analyzer runs quickly because it sits in the critical path. Every millisecond here adds latency to your user's experience. Simple rule-based systems add 10-50ms of overhead. Embedding-based systems add 50-200ms. LLM-based classifiers add 500-2000ms.

Routing Logic

The routing logic decides which model receives each request. This can be as simple as a set of if-then rules or as complex as a machine learning system.

Rule-based routing uses explicit conditions. If prompt length is under 100 words, use Model A. If the query contains code, use Model B. This approach is transparent and easy to debug, but it requires manual tuning.

Semantic routing uses embeddings to match requests to predefined categories. Each category maps to a specific model. This approach handles nuance better than rules but requires maintaining a set of reference examples.

LLM-assisted routing uses a small model to classify requests and decide routing. This approach is flexible and can adapt to new types of queries, but it adds latency and cost.

Model Registry

The model registry tracks available models, their capabilities, current status, and costs. When a model goes down, the registry marks it as unavailable. The router then sends traffic elsewhere.

The registry should track model endpoint URLs, API keys, rate limits, pricing per token, average latency, and recent error rates. This information helps the router make informed decisions.

Fallback Handler

The fallback handler takes over when the primary model fails. This might mean retrying the same model, switching to a backup, or returning a cached response.

A good fallback strategy has multiple levels. First, retry the same model once or twice for transient failures. Second, switch to an equivalent model from a different provider. Third, switch to a simpler model that might handle the request adequately. Fourth, return an error to the user.

Response Cache

A semantic cache stores responses and reuses them for similar queries. Unlike traditional caching that requires exact string matches, semantic caching uses embeddings to find similar queries.

When a request comes in, the system generates an embedding and searches for similar cached entries. If it finds a match above a certain similarity threshold, it returns the cached response instead of calling the model. This can reduce API costs by 40-60% for applications with repeated queries.

Step 1: Choose Your Models

The first step is deciding which models to include in your router. This depends on your specific use case, but most systems benefit from having at least three tiers.

Fast Tier

The fast tier handles simple, high-volume requests. These models cost $0.001-0.002 per 1,000 tokens and respond in under 500ms.

Examples include GPT-3.5 Turbo, Claude Instant, Llama 3 8B, and Mistral 7B. These models work well for basic classification, simple question answering, short summaries, and template filling.

Aim to route 40-60% of your traffic to this tier if possible. This is where most cost savings come from.

Smart Tier

The smart tier handles standard requests that need better reasoning. These models cost $0.01-0.03 per 1,000 tokens and respond in 1-3 seconds.

Examples include GPT-4, Claude 3.5 Sonnet, and Gemini Pro. These models handle multi-step reasoning, detailed explanations, code generation, and content creation.

Most teams route 30-40% of traffic to this tier.

Power Tier

The power tier handles complex tasks that require maximum capability. These models cost $0.03-0.10 per 1,000 tokens and might take 5-10 seconds to respond.

Examples include GPT-4 Turbo, Claude Opus, and Gemini Ultra. These models handle complex research tasks, advanced coding problems, detailed analysis, and creative writing projects.

Only 10-20% of traffic typically needs this tier. Overusing it is the main source of unnecessary costs.

Specialized Models

Some use cases benefit from specialized models. A coding assistant might include models optimized for code generation. A customer service bot might include models trained on support tickets.

These models fill specific gaps in your model portfolio. They're usually open-source models you fine-tune on your data.

Step 2: Define Routing Rules

Once you have your models, define the rules that determine which requests go where. Start simple and add complexity as needed.

Rule-Based Routing

Rule-based routing uses explicit conditions to classify requests. This approach is straightforward and performs well for many applications.

Start by identifying clear patterns in your traffic. If your application handles customer support, you might route based on urgency, question type, or user tier. If you build a coding assistant, you might route based on code language or task complexity.

Example rules:

• If prompt length is under 50 words, use fast tier
• If prompt contains "explain" or "why", use smart tier
• If prompt contains "analyze" or "research", use power tier
• If user is on premium plan, use smart tier minimum
• If request is marked urgent, use power tier

This approach handles 80% of routing needs with 5-10 simple rules. The router adds minimal latency and is easy to debug.

Semantic Routing

Semantic routing uses embeddings to match requests to categories. Each category maps to a model tier.

First, define your categories. For a customer service bot, categories might include billing questions, technical support, account management, and product information. For a coding assistant, categories might include debugging, code review, feature implementation, and architecture design.

Next, create reference examples for each category. These are typical queries that represent each category. Generate embeddings for these examples using a model like text-embedding-ada-002 or similar.

When a request comes in, generate its embedding and calculate cosine similarity with each reference embedding. Route to the model associated with the most similar category.

This approach handles nuance better than rules. It adapts to new phrasing without manual updates. The tradeoff is added latency for embedding generation and similarity calculation.

LLM-Assisted Routing

LLM-assisted routing uses a small model to classify requests. This provides the most flexibility but adds the most latency.

The classifier model receives each request and returns a category or tier recommendation. You can use a fine-tuned model trained on your specific categories or use a general-purpose model with careful prompting.

This approach works well when you have complex routing logic that's hard to capture in rules or when your categories evolve frequently. The cost is 500-2000ms of added latency plus the cost of calling the classifier model.

Hybrid Approaches

Most production systems use hybrid approaches. They apply simple rules first, then use semantic or LLM-based routing for edge cases.

A typical flow:

1. Check prompt length. If under 50 words, route to fast tier
2. Check for specific keywords. If found, route accordingly
3. If no rules match, generate embedding and use semantic routing
4. If confidence is low, escalate to smart tier by default

This approach combines the speed of rules with the flexibility of semantic routing.

Step 3: Implement Cost Controls

Routing reduces costs, but you still need controls to prevent unexpected bills.

Budget Tracking

Track costs at multiple levels. Monitor total spend across all models, cost per model, cost per user or team, and cost per request type.

This visibility helps you identify expensive patterns. Maybe one user sends particularly complex queries. Maybe one request type consistently routes to expensive models when a cheaper option would work.

Rate Limiting

Implement rate limits for expensive models. If a user or team hits their limit, route subsequent requests to cheaper models or return an error.

This prevents runaway costs from bugs or abuse. A rate limit also encourages users to optimize their prompts.

Cost-Aware Routing

Some routing decisions should consider cost explicitly. If two models can handle a request equally well, route to the cheaper one.

This is particularly useful during peak usage times. When API costs surge due to high demand, the router can shift traffic to alternative providers or models.

Step 4: Set Up Fallback Chains

Model failures happen. Your routing system needs to handle them gracefully.

Primary and Backup Models

For each tier, define a primary model and at least one backup. When the primary fails, the router tries the backup.

Choose backups with similar capabilities. If your primary smart tier model is GPT-4, your backup might be Claude 3.5 Sonnet. If your primary fast tier model is GPT-3.5, your backup might be Llama 3.

Retry Logic

Not all failures require switching models. Transient network errors, temporary rate limits, and brief service hiccups often resolve quickly.

Implement exponential backoff for retries. Try once immediately, then wait 1 second and try again, then wait 2 seconds, then 4 seconds. After 3-4 retries, give up and try the backup model.

Circuit Breakers

Circuit breakers prevent the router from repeatedly calling a failing model. If a model returns errors on 50% of requests over 1 minute, the circuit breaker opens. The router stops sending traffic to that model and uses the backup instead.

After a cooldown period, the circuit breaker moves to a half-open state. It sends a small percentage of traffic to the previously failing model. If those requests succeed, the circuit breaker closes and normal routing resumes. If they fail, the circuit breaker opens again.

This pattern prevents retry storms and gives failing services time to recover.

Degraded Mode

When multiple models fail, the system enters degraded mode. This might mean routing all traffic to the last working model, serving cached responses only, or returning errors to users.

Define your degraded mode behavior in advance. Users prefer slow responses to no responses.

Step 5: Add Observability

You can't optimize what you don't measure. Comprehensive observability is essential for a production routing system.

Request Metrics

Track metrics for every request. Log which model handled it, how long it took, what it cost, whether it succeeded, and what the user response was.

Key metrics include:

• Requests per second by model
• Average latency by model and tier
• Error rate by model
• Cost per request
• Token usage by model
• Cache hit rate
• Fallback frequency

Quality Metrics

Routing decisions affect output quality. A request routed to a cheaper model might produce a worse response.

Track quality metrics where possible. For customer service, track resolution rate and customer satisfaction scores. For coding assistants, track whether generated code compiles and passes tests. For content creation, track user edits and regeneration requests.

These metrics help you tune routing rules. If you notice that semantic routing sends too many requests to the fast tier and quality suffers, adjust your similarity thresholds.

Dashboard and Alerts

Build a dashboard that shows routing performance at a glance. Display current request volume by tier, average latency, error rates, cost trajectory, and cache hit rates.

Set up alerts for anomalies. Alert when error rates exceed 5%, when average latency doubles, when daily costs exceed budget, or when cache hit rate drops below baseline.

Step 6: Implement Semantic Caching

Semantic caching can eliminate 40-60% of LLM API calls for applications with repeated queries.

Embedding Generation

When a request comes in, generate an embedding that captures its semantic meaning. Use a model like text-embedding-ada-002 or an open-source alternative like sentence-transformers.

Store this embedding along with the request text and response in a vector database. Options include Pinecone, Weaviate, Qdrant, or pg_vector for PostgreSQL.

Similarity Search

Before routing a request to a model, search the cache for similar queries. Calculate cosine similarity between the new request's embedding and cached embeddings.

If you find a match above your threshold, return the cached response. If not, route the request normally and cache the result.

Choosing Thresholds

The similarity threshold determines how similar a cached entry must be to count as a hit. Higher thresholds mean fewer false positives but also fewer cache hits.

Start with a threshold of 0.95. This only matches very similar queries. Monitor your cache hit rate and false positive rate. Adjust the threshold based on your quality requirements.

Different use cases need different thresholds. Customer service queries can tolerate a lower threshold because variation in wording rarely changes the answer. Code generation needs a higher threshold because small changes in requirements matter.

Cache Invalidation

Cached responses eventually become stale. Implement expiration policies based on your needs.

Time-based expiration removes cache entries after a certain period. This works well for dynamic content like news or pricing information.

Event-based invalidation removes cache entries when underlying data changes. If your system updates a product catalog, invalidate cached responses about those products.

LRU eviction removes the least recently used entries when the cache reaches capacity. This keeps the cache focused on current queries.

How MindStudio Simplifies Model Routing

Building a custom model router requires significant engineering effort. You need to integrate with multiple LLM providers, implement routing logic, set up monitoring, and handle edge cases.

MindStudio provides a no-code platform that handles model routing out of the box. You get access to 200+ models through a single interface, with intelligent routing built in.

Multi-Model Workflows

MindStudio lets you build workflows that use different models for different steps. A workflow might use a small model for classification, a medium model for processing, and a large model for final output. The platform handles routing automatically.

This approach gives you the performance benefits of custom routing without the engineering overhead. You can test different models for each step and see which combination delivers the best results.

Dynamic Tool Use

MindStudio agents can decide which tools to use within a single session. This includes selecting which LLM to call based on the task at hand.

The agent analyzes the request, determines what needs to happen, and routes to the appropriate model automatically. This creates truly agentic experiences where the system adapts to each unique situation.

Built-In Fallbacks

MindStudio handles failover automatically. If a model is unavailable, the platform routes to an alternative without intervention. This keeps your applications running even during provider outages.

No Markup on Model Costs

Many AI platforms charge a markup on top of model provider costs. MindStudio passes through provider pricing directly. You pay the same as you would calling the API yourself, but get the benefits of a unified interface and intelligent routing.

Visual Configuration

Setting up routing in MindStudio requires no code. You use a visual interface to define which models handle which tasks. This makes it easy to test different configurations and iterate quickly.

For teams without dedicated AI engineering resources, this approach dramatically reduces time to production. You can build sophisticated multi-model systems in hours instead of weeks.

Advanced Routing Strategies

Once you have basic routing working, consider these advanced techniques.

Confidence-Based Routing

Some models return confidence scores with their outputs. You can use these scores to route requests that need higher certainty to more powerful models.

Send a request to a fast model first. If the confidence score is above your threshold, return that response. If not, route to a smarter model. This creates a cascade where each tier only handles requests it can confidently answer.

Load-Based Routing

Route traffic based on current system load. When your primary model is under heavy load and latency increases, shift some traffic to alternative models.

This keeps response times stable during peak usage. Monitor latency for each model in real-time and adjust routing weights dynamically.

A/B Testing

Use your router to run experiments. Route 5% of traffic to a new model and compare its performance to your current default. This lets you validate new models before fully switching over.

Track quality metrics, cost, and latency for both variants. If the new model performs well, gradually increase its traffic share.

User-Based Routing

Different users have different needs. Premium users might always route to smart tier models. Free tier users might be limited to fast tier models. Enterprise customers might have dedicated model instances.

Include user context in your routing decisions. This ensures you deliver appropriate service levels while controlling costs.

Time-Aware Routing

Your traffic patterns change throughout the day. During peak hours, prioritize latency by routing more traffic to fast models. During off-hours, prioritize quality by using smarter models more freely.

This optimization depends on understanding your users' expectations at different times. Business users might tolerate slightly slower responses during their workday but expect instant answers late at night when they're working on urgent issues.

Monitoring and Optimization

Routing isn't set-it-and-forget-it. Continuous monitoring and optimization are essential.

Weekly Review

Review routing performance weekly. Look at traffic distribution across tiers, cost trends, latency by model, error rates, and cache hit rates.

Identify opportunities to optimize. Maybe you're routing too conservatively and could shift more traffic to cheaper models. Maybe certain request types always get routed to expensive models when a cheaper alternative would work.

Quality Spot Checks

Randomly sample requests and review their responses. Check that cheaper models are producing acceptable output. Look for cases where expensive models are being wasted on simple tasks.

This manual review catches issues that metrics miss. A response might technically succeed but not fully answer the user's question.

Rule Tuning

Your routing rules will need adjustment over time. User behavior changes. New models become available. Your understanding of which models work best for which tasks improves.

Update rules based on data. If you notice that prompts containing certain keywords consistently route to the wrong tier, add rules to handle them better.

Cost Analysis

Break down costs by request type, user, model, and time of day. This analysis reveals optimization opportunities.

If one request type dominates your costs, focus optimization efforts there. If certain users consistently generate expensive requests, consider whether they're using the system as intended or if you need to add guardrails.

Common Pitfalls and How to Avoid Them

Teams building routing systems make predictable mistakes. Here's how to avoid them.

Over-Engineering

Don't build complex routing logic before you need it. Start with simple rules. Add complexity only when you have data showing it will help.

Many teams spend weeks building sophisticated ML-based routing systems only to discover that five simple rules handle 95% of their traffic effectively.

Ignoring Latency

Routing adds latency. Every step in your routing logic—classification, embedding generation, similarity search—adds milliseconds. These add up.

Measure the latency your routing adds and keep it under 200ms if possible. Users notice delays above that threshold.

Poor Error Handling

Model providers return various error types. Rate limits, service outages, and validation errors all need different handling.

Don't treat all errors the same. Retry transient errors. Route around rate limits. Return clear messages for validation errors. This prevents retry storms and improves user experience.

Insufficient Monitoring

You can't fix problems you don't know about. Many routing issues only become visible with detailed monitoring.

Log every routing decision with context. Track how often each rule fires. Monitor error rates by model. Set up alerts for anomalies.

Ignoring Cache Invalidation

Stale cached responses create user confusion. A customer might see outdated pricing or incorrect product information.

Implement proper cache invalidation from the start. This is easier than fixing cache consistency issues later.

No Graceful Degradation

When all your models fail, what happens? Many systems simply return errors, which frustrates users.

Design for degraded operation. Serve cached responses, route to a basic model running on your own infrastructure, or show helpful error messages that explain the situation.

Security and Compliance Considerations

Model routing affects security and compliance in several ways.

Data Residency

Different models run in different regions. If you're subject to data residency requirements, your router must respect them.

Tag requests with data sensitivity levels. Route sensitive data only to models running in approved regions. This might mean using a less capable model to maintain compliance.

PII Handling

Personally identifiable information requires special handling. Some model providers store input data. Others process it transiently.

Detect PII in requests before routing. Route PII to providers with appropriate data handling policies. Consider stripping or masking PII before sending requests.

API Key Management

Your router needs API keys for multiple providers. Secure these carefully.

Store keys in a secrets manager like AWS Secrets Manager or HashiCorp Vault. Rotate keys regularly. Use different keys for different environments. Monitor key usage for anomalies.

Audit Logging

For compliance, you might need to log which model handled which request. This audit trail proves you followed data handling policies.

Log routing decisions with timestamps, user IDs, model selections, and reasons. Store these logs securely with appropriate retention.

Implementation Checklist

Use this checklist to ensure your routing implementation is production-ready.

Architecture

• Choose models for each tier
• Define routing rules or classification logic
• Set up model registry with status tracking
• Implement fallback chains
• Add circuit breakers
• Configure semantic caching

Reliability

• Implement retry logic with exponential backoff
• Add timeout handling
• Set up health checks for each model
• Configure alerts for failures
• Test failover scenarios

Performance

• Measure routing latency overhead
• Optimize embedding generation
• Add caching at multiple levels
• Use connection pooling for API calls
• Monitor and optimize cache hit rates

Cost Control

• Set up cost tracking by model
• Implement budget limits
• Add rate limiting for expensive models
• Track cost per request type
• Monitor cost trends over time

Observability

• Log all routing decisions
• Track key metrics in real-time
• Build monitoring dashboard
• Set up alerts for anomalies
• Create reports for weekly review

Security

• Secure API key storage
• Implement PII detection
• Add audit logging
• Configure data residency rules
• Set up access controls

Testing Your Router

Thorough testing prevents production issues.

Unit Tests

Test each routing rule individually. Verify that requests matching certain patterns route to expected models. Test edge cases like very long prompts, empty requests, and special characters.

Integration Tests

Test the full routing flow with real model providers. Verify that requests complete successfully, fallbacks work correctly, and timeouts are handled properly.

Load Tests

Simulate production traffic volumes. Verify that your router handles expected load without degradation. Test behavior under high concurrency.

Chaos Tests

Simulate failures. Shut down model providers randomly. Send malformed requests. Trigger rate limits. Verify that your system recovers gracefully.

Shadow Testing

Before deploying routing changes to production, run them in shadow mode. Route production traffic through both old and new logic. Compare results without affecting users. This validates that changes work as expected.

Deployment Strategies

Roll out routing carefully to minimize risk.

Canary Deployment

Start by routing 5% of traffic through your new system. Monitor for issues. If everything looks good, gradually increase to 10%, 25%, 50%, and finally 100%.

This approach catches problems early when they affect few users.

Feature Flags

Use feature flags to control routing behavior. This lets you enable routing for specific users or request types first. If issues arise, disable the flag to instantly revert.

Gradual Model Introduction

When adding new models to your router, start with a small percentage of appropriate traffic. Monitor quality and costs. Increase traffic share if results are positive.

Future-Proofing Your Router

The LLM landscape evolves quickly. Design your router to adapt.

Model-Agnostic Design

Don't hardcode model names in routing logic. Use tiers or categories instead. This lets you swap models without changing rules.

Instead of routing to "GPT-4", route to "smart tier". Then configure which model the smart tier uses. This separation makes updates easy.

Dynamic Configuration

Store routing rules in configuration files or databases rather than code. This lets you update rules without deploying new code.

Use a configuration service that allows hot reloading. Changes take effect immediately without downtime.

Pluggable Routing Strategies

Design your system to support multiple routing strategies. You might start with rules, add semantic routing later, and eventually use ML-based routing.

Make strategies pluggable so you can switch between them or run them in parallel for comparison.

Provider Abstraction

Abstract provider-specific API details. Your routing logic shouldn't know whether it's calling OpenAI, Anthropic, or a local model. This abstraction makes it easy to add new providers.

Conclusion

Setting up an AI model router requires careful planning and implementation. The payoff is substantial: lower costs, better performance, and more reliable service.

Start simple. Choose three models covering different capability levels. Write five routing rules based on obvious patterns in your traffic. Add semantic caching. Set up monitoring. Deploy to a small percentage of traffic.

Once this basic system runs smoothly, add sophistication. Implement fallback chains. Add more models for specific use cases. Use semantic routing for nuanced classification. Build custom features for your specific needs.

The most important aspect is iteration. No routing system is perfect on day one. Measure constantly. Adjust based on data. Over time, you'll develop routing logic that perfectly matches your application's needs.

For teams that want to avoid building custom infrastructure, platforms like MindStudio provide model routing capabilities out of the box. You can focus on building your application instead of managing routing complexity.

Model routing transforms how you use LLMs. It turns a single powerful tool into a flexible system that adapts to each request. This adaptability is essential for building production applications that are both high-quality and cost-effective.

Key Takeaways

• Model routing can reduce LLM costs by 30-85% without sacrificing quality
• Start with simple rule-based routing before adding complexity
• Use multiple model tiers: fast, smart, and power
• Implement fallback chains to handle provider outages
• Add semantic caching to eliminate 40-60% of API calls
• Monitor routing decisions closely to identify optimization opportunities
• Design for gradual rollout and easy configuration updates
• Consider platforms like MindStudio that handle routing automatically

Frequently Asked Questions

What is the minimum number of models needed for effective routing?

You can start with just two models: one fast and cheap, one powerful and expensive. This simple setup already provides cost savings by routing obvious simple requests to the cheap model. Most production systems eventually use 3-5 models covering different capability levels and specialized use cases.

How much latency does routing add to requests?

Simple rule-based routing adds 10-50ms. Semantic routing with embedding generation adds 50-200ms. LLM-assisted routing adds 500-2000ms. Keep total routing overhead under 200ms to avoid impacting user experience. The latency trade-off is usually worthwhile given the cost savings and reliability improvements.

Should I build a custom router or use an existing solution?

Build custom if you have specific requirements that existing solutions don't meet or if you have engineering resources to maintain it. Use existing solutions like MindStudio if you want to focus on your application instead of infrastructure. Most teams benefit from existing solutions that provide routing as a managed service.

How do I handle model deprecations?

Design your router with model abstraction. Don't hardcode model names in routing logic. Use tiers or categories instead. When a model is deprecated, swap it for a replacement in your configuration. Your routing rules continue working without changes. Monitor for performance differences after swaps.

Can routing work with streaming responses?

Yes, but it adds complexity. You need to make routing decisions before streaming starts. This means you can't use confidence-based routing where you examine the response to decide whether to upgrade to a better model. Rule-based and semantic routing work fine with streaming. Buffer the first few tokens to validate output quality if needed.

How do I measure routing success?

Track cost reduction, latency improvement, error rate, and quality metrics specific to your application. Compare these metrics before and after implementing routing. A successful router reduces costs by 30-50%, maintains or improves quality, and keeps error rates low. Set up dashboards to monitor these metrics continuously.

What happens when all models in a tier fail?

Your fallback strategy should define this behavior. Options include routing to the next tier up, serving cached responses if available, using a model running on your infrastructure, or returning a clear error message to users. Design for graceful degradation rather than complete failure.

How often should I update routing rules?

Review routing performance weekly and adjust rules as needed. Major updates might happen monthly as you learn which patterns work best. Use A/B testing to validate rule changes before rolling them out fully. Keep a history of rule changes so you can revert if needed.