What Is an AI Model Router? Optimize Cost Across LLM Providers

Introduction

Your AI bill doubled last month. Again. You're using GPT-4 for every request because switching between providers means rewriting code. When OpenAI has an outage, your entire application goes down. You know there are cheaper models that could handle simple queries, but managing multiple providers feels like building a second product.

An AI model router solves this problem. It sits between your application and AI providers, automatically selecting the most cost-effective model for each request. Organizations using routers report 30-70% cost reductions while maintaining quality. Some achieve up to 98% savings on specific workloads.

This guide explains how AI model routers work, what to look for when choosing one, and how to implement routing in your infrastructure. You'll learn the difference between routing strategies, common pitfalls to avoid, and how tools like MindStudio simplify the entire process.

What Is an AI Model Router?

An AI model router analyzes incoming requests and directs them to the most appropriate language model based on complexity, cost, latency requirements, and other factors. Instead of sending every query to one expensive model, the router evaluates each request and picks from a pool of available models.

Think of it as a traffic controller for AI requests. A simple question like "What's the capital of France?" gets routed to a lightweight model that costs $0.10 per million tokens. A complex coding task requiring multi-step reasoning goes to a more capable model at $30 per million tokens. The router makes this decision in milliseconds.

The cost difference matters at scale. A customer support chatbot handling 100,000 daily requests could spend $4,500 monthly using GPT-4 for everything. With intelligent routing, that same workload might cost $1,500 by sending 80% of requests to cheaper models.

Why Routers Matter Now

The AI landscape has changed. Two years ago, you picked one model and stuck with it. Now there are hundreds of options. OpenAI offers multiple GPT variants. Anthropic has Claude in several sizes. Google provides Gemini Pro, Flash, and Nano. DeepSeek delivers frontier performance at a fraction of the cost. Open source models like Llama and Mistral run on your own infrastructure.

No single model excels at everything. GPT-4 handles complex reasoning but costs more. Claude excels at long context windows. Smaller models respond faster and cheaper for routine tasks. Using the right model for each job is the only way to balance cost, performance, and latency.

The numbers back this up. Enterprise LLM spending hit $8.4 billion in the first half of 2025. Nearly 40% of enterprises now spend over $250,000 annually on language models. At that scale, a 30% cost reduction saves $75,000 per year. A 50% reduction saves $125,000.

How AI Model Routers Work

A router operates in three stages: analysis, selection, and execution. The entire process takes under a millisecond for most implementations.

Request Analysis

When a request arrives, the router examines multiple signals to understand what it needs. This analysis happens before any model sees the prompt.

Complexity signals: The router looks at prompt length, structure, and content. A 50-word question asking for a definition differs from a 500-word prompt requesting code generation with specific constraints.

Intent classification: Some routers use lightweight classifiers to categorize requests. Is this a factual lookup, creative writing, code generation, data analysis, or reasoning task? Each category maps to models with proven strengths.

Context requirements: Requests with large context windows need models that support them. A query referencing a 50,000-token document requires a model with sufficient context capacity.

Domain signals: Certain keywords indicate specialized domains. Medical terminology might route to a model trained on healthcare data. Legal language could route to a model with strong performance on regulatory text.

Model Selection

After analysis, the router picks a model using one of several strategies.

Rule-based routing uses predefined logic. If the prompt is under 100 tokens and asks a factual question, use Model A. If it requires code generation, use Model B. If it needs complex reasoning, use Model C. These rules are fast and predictable but require manual tuning.

Similarity-based routing compares the incoming request to past queries using embeddings. If a new question is semantically similar to previous questions that were successfully answered by Model A, route it there. This approach adapts to real usage patterns.

Machine learning routing trains a classifier on historical data. The classifier learns which model performs best for different request types. It considers features like token count, embedding similarity to known categories, and past performance metrics.

Cost-aware routing factors in budget constraints. If you set a maximum cost per request, the router picks the cheapest model likely to succeed. If that model fails or returns low-confidence results, it escalates to a more expensive option.

Execution and Fallback

Once selected, the request goes to the chosen model. But intelligent routers don't stop there. They monitor for failures and have fallback strategies.

If the primary model is unavailable, the router tries the next best option. If a model hits rate limits, requests shift to alternative providers. If a response seems incorrect or incomplete based on confidence scores, the router can automatically retry with a more capable model.

This resilience is critical. When OpenAI experienced outages in 2025, applications using routers stayed online by switching to Anthropic or Google. Applications without routing went down.

Cost Optimization Through Routing

The primary reason to implement routing is cost reduction. Here's how the math works and what savings to expect.

The Cost Breakdown

LLM pricing varies dramatically by model tier. As of early 2026:

• Premium models (GPT-4, Claude Opus): $30-60 per million tokens
• Mid-tier models (GPT-4 Turbo, Claude Sonnet): $10-15 per million tokens
• Lightweight models (GPT-3.5, Claude Haiku): $0.50-2 per million tokens
• Small models (Llama, Mistral): $0.10-0.50 per million tokens
• Local deployment (open source on your hardware): $0.0001 per million tokens

The difference between premium and lightweight models is 60-300x. Even a 30% reduction in premium model usage creates significant savings.

Real-World Savings

A development team building a coding assistant found that 70% of requests were simple tasks. Code formatting, syntax checking, and basic completions worked fine with GPT-3.5. Only 30% of requests needed GPT-4's reasoning capabilities.

Before routing:

• 100,000 requests per day
• Average 1,000 tokens per request (500 input, 500 output)
• All requests to GPT-4 at $30 per million tokens
• Daily cost: $3,000
• Monthly cost: $90,000

After implementing routing:

• 70,000 requests to GPT-3.5 at $1 per million tokens: $70
• 30,000 requests to GPT-4 at $30 per million tokens: $900
• Daily cost: $970
• Monthly cost: $29,100
• Savings: 68%

That's $60,900 per month or $730,800 per year. The entire routing implementation took less than a week.

Optimization Strategies

Several techniques maximize cost savings while maintaining quality.

Caching: Store responses for repeated queries. If someone asks "What is Python?" and you've answered it before, return the cached response without calling any model. Semantic caching goes further by recognizing similar questions. "What is Python?" and "Can you explain Python?" get the same cached answer.

Organizations report 40% cache hit rates in production, translating to 40% fewer API calls. One company saved $3,000 monthly through caching alone.

Prompt optimization: Trim unnecessary tokens from prompts. A verbose system message of 500 tokens might compress to 200 tokens without losing meaning. At scale, that's a 60% reduction in input costs.

Output length limits: Set maximum response lengths. A chatbot doesn't need 1,000-token responses when 200 tokens suffice. Enforcing limits prevents runaway costs from verbose model outputs.

Batch processing: For non-real-time tasks, batch multiple requests together. Many providers offer discounted rates for batch processing. Analysis tasks, content generation, and data labeling work well in batches.

Streaming responses: For user-facing applications, stream responses as they generate. Users see output immediately, improving perceived latency. If a response starts going off track, you can stop generation early, saving output tokens.

Routing Strategies in Detail

Different routing approaches suit different use cases. Most production systems use a combination.

Static Rule-Based Routing

The simplest approach defines rules manually. This works well when request types are predictable.

Example rules for a customer support system:

• Greeting detection (Hi, Hello, Hey): Route to smallest model, return cached greeting
• Account balance inquiry: Route to mid-tier model with access to account data
• Complex troubleshooting: Route to premium model with reasoning capabilities
• Requests over 2,000 tokens: Route to long-context model

Benefits: Fast, predictable, easy to debug. If something goes wrong, you know exactly which rule triggered.

Drawbacks: Requires maintenance. As your application evolves, rules need updates. Edge cases slip through. A request that matches multiple rules needs priority logic.

Embedding-Based Routing

This approach uses semantic similarity. Create embeddings for categories of requests. When a new request arrives, generate its embedding and compare it to category embeddings. Route based on the closest match.

For a documentation assistant:

• Category: Getting started (use lightweight model)
• Category: API reference (use mid-tier model with function calling)
• Category: Complex integration (use premium model)

A request asking "How do I authenticate?" gets an embedding close to the "Getting started" category. It routes to the lightweight model. A request about "implementing OAuth with custom claims and token refresh" matches "Complex integration" and routes to the premium model.

Benefits: Adapts to usage patterns without manual rules. Handles variations in phrasing naturally.

Drawbacks: Requires initial training data. Needs periodic retraining as request patterns shift.

Classifier-Based Routing

Train a machine learning classifier to predict which model performs best for each request. The classifier learns from historical data about request characteristics and outcomes.

Features the classifier considers:

• Token count
• Presence of code blocks
• Question versus statement
• Detected language
• Domain-specific keywords
• Time of day (some models perform better under different load)
• Past success rates for similar requests

The classifier outputs a probability distribution over available models. You can configure it to pick the highest probability, the cheapest model above a threshold, or use the probabilities to implement fallback chains.

Benefits: Learns from real performance data. Optimizes for your specific use case and data distribution.

Drawbacks: Requires labeled training data. Needs infrastructure to retrain regularly. Can be opaque about why it made a decision.

Reinforcement Learning Routing

Advanced routers use reinforcement learning to optimize over time. The router gets feedback on whether its decisions were correct. Did the selected model produce a good response? Did it complete within latency requirements? Did it stay within budget?

The router adjusts its policy based on this feedback. If it routed simple questions to an expensive model too often, it learns to use cheaper options. If cheap models failed on certain request types, it learns to escalate sooner.

This approach handles the exploration-exploitation tradeoff. The router occasionally tries different models on requests to learn about performance. It exploits what it knows by routing to proven models most of the time.

Benefits: Continuously improves. Adapts to changes in model capabilities, pricing, and your application's needs.

Drawbacks: Complex to implement. Requires careful reward function design. Can make suboptimal decisions during exploration phases.

Hybrid Approaches

Production systems often combine strategies. Start with simple rules to catch obvious cases. Use embedding similarity for general classification. Apply a trained classifier for borderline decisions. Include manual overrides for specific high-value use cases.

A content generation platform might use:

• Rules: Blog post introductions always use Model A (proven through testing)
• Embeddings: Classify request type (how-to, listicle, opinion, analysis)
• Classifier: Within each type, pick the best model based on topic complexity
• Override: Premium customers always get the best model regardless of other factors

Key Features of Modern Routers

When evaluating routing solutions, look for these capabilities.

Multi-Provider Support

A router should support major providers (OpenAI, Anthropic, Google, Cohere) and open source models (Llama, Mistral, local deployments). The more options available, the better you can optimize for cost and capability.

Unified API access means you write code once. Switching providers is a configuration change, not a rewrite. When a new model launches, you add it to the pool without touching application code.

Automatic Failover

Providers have outages. Rate limits hit. API keys expire. A production router handles these failures gracefully.

When the primary model is unavailable, the router tries alternatives. It tracks error rates per provider and temporarily shifts traffic away from struggling services. When providers recover, it rebalances load gradually.

Circuit breaker patterns prevent wasting time on failing providers. If a provider returns errors for 10 consecutive requests, the circuit opens. The router stops sending traffic there for a cooldown period. After cooldown, it tries again with a single request. If that succeeds, the circuit closes and normal traffic resumes.

Cost Tracking and Budgets

Visibility into spending is essential. Routers should track:

• Cost per request
• Cost per user or team
• Cost per model
• Cost per time period (hourly, daily, monthly)
• Token usage (input and output separately)

Budget controls prevent surprises. Set spending limits at multiple levels:

• Global budget: Total monthly spending cap
• Per-user budget: Limit individual users to prevent abuse
• Per-application budget: Allocate budgets across different services

When budgets approach limits, routers can throttle requests, downgrade to cheaper models, or alert administrators. The goal is to maintain service while staying within financial constraints.

Caching and Optimization

Built-in caching reduces costs and improves latency. Request caching stores complete responses. If the same query arrives, return the cached result. Semantic caching uses embeddings to match similar queries.

Prompt caching stores intermediate computations. If multiple requests share a long system prompt, cache those key-value pairs. Only the unique part of each request needs processing. Anthropic reports 90% input cost reduction for repeated context.

Observability and Monitoring

Understanding what your router does helps optimize it. Key metrics include:

• Request volume per model
• Success and error rates
• Latency distribution (p50, p95, p99)
• Token usage trends
• Cache hit rates
• Routing decision distribution

Detailed logging shows why the router made each decision. When something goes wrong, logs reveal whether it was a routing error, model failure, or application issue.

Integration with monitoring tools (Datadog, Prometheus, CloudWatch) centralizes observability. Set alerts for anomalies like sudden cost spikes, latency increases, or error rate jumps.

Security and Compliance

Routers handle sensitive data flowing to external providers. Security features matter:

• API key management: Secure storage and rotation of provider credentials
• Data filtering: Remove personally identifiable information before requests leave your infrastructure
• Audit logging: Record all requests and responses for compliance
• Data residency: Route requests to providers in specific geographic regions
• Provider restrictions: Block certain providers for sensitive workloads

Implementation Considerations

Adding a router to your infrastructure requires planning. Here are the practical steps and common issues.

Architecture Patterns

Routers can deploy as a proxy service or embedded library.

Proxy deployment: The router runs as a separate service. Applications make requests to the router instead of directly to providers. The router forwards requests to the appropriate model and returns responses.

Benefits: Centralized control, language-agnostic (any application can use it), easy to update routing logic without changing applications.

Drawbacks: Adds network hop, becomes a single point of failure unless deployed redundantly.

Library deployment: The router is a library integrated into your application code. Each application instance makes routing decisions.

Benefits: No extra network hop, no separate service to maintain, works offline if using local models.

Drawbacks: Harder to update routing logic across many applications, each instance needs configuration, less visibility into global routing patterns.

Most production systems use proxy deployment. The slight latency increase (typically 10-50 microseconds) is worth the operational benefits.

Performance Considerations

Routing adds overhead. The goal is to minimize it.

Routing latency: The time to analyze a request and select a model should be under 10 milliseconds. Lightweight routers using rules or simple classifiers achieve 1-5 milliseconds. Complex models for routing can add 50-100 milliseconds, which may be too slow for interactive applications.

Cache performance: In-memory caching is fast but limited by available RAM. Redis provides shared caching across instances with single-digit millisecond latency. Persistent storage like S3 works for large caches but adds 50-200 milliseconds per lookup.

Throughput: A single router instance should handle thousands of requests per second. Compiled language implementations (Go, Rust) typically handle 5,000-10,000 requests per second per core. Python implementations handle 500-2,000 requests per second per core.

For high-volume applications, deploy multiple router instances behind a load balancer. Scale horizontally as traffic grows.

Testing and Validation

Before production deployment, validate that routing improves your specific workload.

Baseline performance: Measure current costs, latency, and quality without routing. Use these as comparison points.

Shadow mode: Run the router in parallel with existing infrastructure. It makes routing decisions but doesn't affect actual requests. Compare what the router would have done against what you're doing now.

Canary deployment: Route a small percentage of traffic (1-5%) through the router. Monitor cost, latency, and quality. Gradually increase the percentage if results are positive.

A/B testing: Split users into groups. Route one group through the new system, keep another on the old system. Compare outcomes across groups.

Quality evaluation: Routing decisions trade cost for quality. Ensure quality remains acceptable. Use human evaluation or automated metrics to check response quality across different models.

Common Pitfalls

Over-optimization: Spending weeks tuning routing to save an extra 5% isn't worth it if you could ship features instead. Start simple, measure, iterate.

Ignoring edge cases: Rules that work for 95% of requests fail on the remaining 5%. Implement fallbacks and monitoring to catch issues.

Forgetting maintenance: Routing logic needs updates as models improve, pricing changes, and your application evolves. Schedule regular reviews.

Black box routing: If you don't understand why the router made a decision, debugging is hard. Choose explainable approaches or log detailed reasoning.

Security gaps: Routing can expose data to providers you didn't intend. Review provider agreements, implement data filtering, and audit where sensitive information goes.

How MindStudio Simplifies AI Model Routing

MindStudio provides a complete platform for building AI applications with intelligent routing built in. Instead of managing infrastructure, configuring routers, and writing routing logic, you define workflows visually and let MindStudio handle optimization.

Visual Workflow Design

Build AI applications by connecting blocks in a visual interface. Add an AI model block and MindStudio automatically provides access to multiple providers. Switch between GPT-4, Claude, Gemini, or open source models with a dropdown selection.

For routing, add a decision block that evaluates request characteristics and branches to different models. You define the logic visually. No code required for basic routing scenarios.

Advanced users can write custom routing logic in JavaScript or Python. The code runs within MindStudio's runtime with access to all available models.

Automatic Cost Optimization

MindStudio tracks costs across all models and providers. The dashboard shows spending trends, per-user costs, and cost per request. Set budget alerts to stay within limits.

Built-in recommendations suggest where you can save money. If MindStudio detects that 80% of your requests to GPT-4 are simple queries, it suggests routing those to GPT-3.5. You review the suggestion, adjust the routing logic, and deploy with one click.

Caching is automatic. MindStudio recognizes repeated queries and serves cached responses without configuration. Semantic caching is available with a toggle.

Production-Ready Infrastructure

Deploy applications instantly. MindStudio handles hosting, scaling, and monitoring. Applications scale automatically based on traffic. No servers to manage.

Failover works out of the box. If a provider has issues, MindStudio routes to alternatives. You can configure fallback preferences in the settings.

Observability is built in. View request logs, error rates, latency metrics, and token usage in real-time. Export data to your existing monitoring tools.

Enterprise Features

For larger teams, MindStudio provides:

• Role-based access control for managing who can edit workflows and view data
• SSO integration with your identity provider
• Data residency controls to route requests through specific regions
• Audit logs for compliance requirements
• Custom model deployments for models you host

Getting Started

Create an account and start with templates. MindStudio provides pre-built workflows for common use cases like customer support, content generation, and data analysis. Each template includes recommended routing logic.

Modify the template to fit your needs. Connect your data sources. Test the workflow with sample requests. Deploy when ready.

The free tier includes generous usage limits. Paid plans scale with your needs, with transparent pricing based on request volume.

Security and Compliance in Routing

Routing introduces new security considerations. Data flows through multiple systems and potentially multiple providers. Understanding the risks and mitigations is critical.

Data Privacy

When you route requests to external providers, those providers see your data. Different providers have different data policies:

• Some providers use data to improve models unless you opt out
• Some providers store data for 30 days for abuse monitoring
• Some providers offer zero data retention for enterprise customers
• Some providers don't have data processing agreements for certain regions

Review provider agreements carefully. For sensitive workloads, restrict routing to providers with acceptable policies. Use data filtering to remove sensitive information before requests leave your infrastructure.

Regulatory Compliance

Different regulations apply depending on your industry and location.

GDPR: If you process data of EU residents, ensure providers are GDPR-compliant. Check data processing agreements. Implement data residency controls to keep data within the EU when required.

HIPAA: Healthcare data requires providers with Business Associate Agreements. Not all providers offer BAAs. Routing healthcare queries requires restricting the model pool to compliant providers only.

SOC 2: Many enterprises require vendors to be SOC 2 certified. Verify that routing solutions and providers meet these standards.

Data residency laws: Some countries require data to remain within their borders. Implement geographic routing to comply. Route requests from users in China through models hosted in China.

Attack Vectors

Routing systems face specific security risks.

Prompt injection: Attackers craft prompts that manipulate routing decisions. Adding specific trigger phrases could force routing to expensive models, inflating costs. Mitigation: Implement prompt filtering, rate limiting, and budget caps.

Model confusion: Attackers exploit differences between models. A prompt safe for Model A might cause Model B to leak training data or behave unexpectedly. Mitigation: Test routing decisions across the entire model pool, implement output filtering.

Cost attacks: Attackers submit requests designed to maximize costs. Long prompts requesting maximum-length responses can drain budgets quickly. Mitigation: Set token limits, implement request throttling, monitor for anomalous usage patterns.

Provider spoofing: In self-hosted routing, attackers might impersonate providers to intercept data. Mitigation: Use HTTPS, verify provider certificates, implement mutual TLS.

Best Practices

• Use separate API keys per application to limit damage from compromised keys
• Rotate API keys regularly
• Implement least privilege access to routing configuration
• Log all routing decisions for audit trails
• Filter sensitive data before routing
• Test routing logic with adversarial inputs
• Monitor for unexpected routing patterns
• Have an incident response plan for security issues

Future of AI Model Routing

Routing is still early. Several trends will shape how it evolves.

Multimodal Routing

Current routers focus on text. Future routers will handle images, audio, and video. A request with an image and text question might route to a vision-language model. A request with audio could route to a speech-specialized model or a general model with audio understanding.

Multimodal routing is more complex. Different modalities have different costs. Image processing might cost 100x more than text per "token" equivalent. Video is more expensive still. Routers will need to consider modality costs and model capabilities across multiple input and output types.

Agent Workflows

AI agents make multiple model calls to complete tasks. They plan, execute actions, and synthesize results. Each step might use different models.

Future routers will optimize entire workflows, not just individual requests. Plan with a lightweight model, execute actions with specialized models, synthesize with a strong general model. The router considers the full task graph and assigns models to nodes.

This gets complex fast. Agents can spawn sub-agents. Tasks branch into parallel paths. Routers will need sophisticated logic to handle these patterns.

Federated Routing

Organizations might run models in multiple locations: cloud, on-premises, edge devices. Routing decisions will factor in where data and compute are located.

A request from a mobile device might route to an edge model for low latency. If the edge model can't handle it, escalate to a cloud model. Data that must stay on-premises routes only to local models.

This distributed routing requires new infrastructure. Routing decisions happen at multiple layers. Coordination between routers becomes necessary.

Standardization

Routing is fragmented. Every solution uses different APIs and configurations. The Model Context Protocol (MCP) and Agent-to-Agent Protocol (A2A) are emerging standards for how models and agents interact.

Standardization will make routing more portable. You could define routing logic once and use it across multiple platforms. Models would expose their capabilities in a standard format, making routing decisions easier.

Quantum and Novel Architectures

Research explores using quantum computing concepts for routing optimization. The idea is to evaluate many routing decisions simultaneously and select optimal paths faster than classical approaches.

This is speculative but highlights that routing is an optimization problem. As the model landscape grows more complex, traditional routing methods might hit limits. Novel approaches could provide the next performance leap.

Conclusion

AI model routing is no longer optional at scale. The cost difference between using one model for everything versus intelligent routing reaches tens or hundreds of thousands of dollars annually. The reliability improvement from multi-provider failover can mean the difference between an always-available service and frequent outages.

Start simple. Pick 2-3 models with different cost and capability profiles. Implement basic rules to route obvious cases. Measure the impact on cost and quality. Iterate from there.

Key takeaways:

• Routers analyze requests and select the most appropriate model based on complexity, cost, and other factors
• Organizations typically save 30-70% on AI costs through intelligent routing
• Multiple routing strategies exist, from simple rules to machine learning classifiers
• Modern routers provide multi-provider support, automatic failover, cost tracking, and caching
• Implementation requires careful testing, monitoring, and security considerations
• Tools like MindStudio simplify routing by providing visual workflow design and automatic optimization

The AI landscape changes fast. New models launch monthly. Pricing shifts. Capabilities improve. Routing provides the flexibility to adapt without rewriting your application. Build with routing from the start, even if you only use one model initially. When you need to optimize or switch providers, the infrastructure is ready.

Frequently Asked Questions

How much latency does routing add?

Lightweight routers using rules or simple classifiers add 1-10 milliseconds. This is typically imperceptible to users. Complex routing logic using large models can add 50-100 milliseconds, which might be noticeable in interactive applications. Choose routing complexity based on your latency requirements.

Can I use routing with self-hosted models?

Yes. Most routers support routing to custom endpoints, including self-hosted models. This lets you mix cloud providers with your own infrastructure. You might route sensitive queries to on-premises models and general queries to cloud models.

What happens if routing makes a wrong decision?

Good routers implement fallback mechanisms. If a model returns a low-confidence response or fails, the router can automatically retry with a more capable model. You can also configure manual overrides for specific cases where routing should always use a particular model.

How do I measure if routing is working?

Track three metrics: cost, latency, and quality. Compare these before and after implementing routing. Cost should decrease. Latency should remain similar or improve slightly due to using faster models for simple requests. Quality should stay the same or improve.

Is routing secure?

Routing introduces security considerations. You're sending data to multiple providers, which increases exposure. Mitigate this by reviewing provider security practices, implementing data filtering, using providers with appropriate compliance certifications, and logging all routing decisions for audit purposes.

Do I need a router if I only use one provider?

It depends. If you use multiple model tiers from one provider (like GPT-4 and GPT-3.5), routing still helps optimize costs. If you only ever use one model, routing adds complexity without benefit. However, implementing routing infrastructure early makes it easier to expand to multiple providers later.

How often should I update routing logic?

Review routing decisions quarterly. Model capabilities improve, pricing changes, and your application evolves. Check if current routing still makes sense. Look for patterns where the router consistently makes suboptimal choices. Adjust rules or retrain classifiers based on recent data.

Can routing handle fine-tuned models?

Yes. If you have fine-tuned models for specific domains, include them in the routing pool. The router can direct domain-specific requests to fine-tuned models and general requests to standard models. This optimizes both quality and cost since fine-tuned models often perform better on their specialized tasks.