Model Routing for Multi-Provider Stacks: Updating Strategy with Each Release

Table of Contents

1. Why Model Routing Matters Now
2. The Multi-Provider Stack Reality
3. Static vs. Dynamic Routing Patterns
4. Building Your Routing Framework
5. Cost and Performance Tradeoffs
6. Implementing Fallback and Failover
7. Observability and Monitoring
8. Updating Strategy with Model Releases
9. Real-World Implementation Examples
10. Building for 2027

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Why Model Routing Matters Now

Model routing is no longer optional. If you’re shipping AI products in 2024 or 2025, you’re almost certainly running multiple models from multiple providers. OpenAI’s GPT-4, Anthropic’s Claude, Google’s Gemini, open-source alternatives via Hugging Face, and proprietary models via AWS Bedrock or Azure OpenAI all have different cost profiles, latency characteristics, and capability strengths.

The challenge isn’t picking one model. The challenge is picking the right model for each specific request at runtime—and then updating that decision every time a new model releases or pricing changes.

This is model routing: the practice of directing requests to the optimal provider and model based on cost, latency, capability, and business rules. Done well, it cuts inference costs by 20–40%, improves response quality, and builds resilience into your AI infrastructure. Done poorly, it becomes a maintenance nightmare that locks you into stale architecture decisions.

We’ve worked with founders and operators at PADISO across Australian scale-ups and US venture-backed companies building AI products. The teams that scale fastest aren’t the ones who pick a single model and pray. They’re the ones who build routing logic that adapts as the model landscape shifts. This guide walks you through how to build that framework—and how to keep it current through 2027 and beyond.

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The Multi-Provider Stack Reality

Your AI stack today probably looks something like this:

• GPT-4 or GPT-4o for high-quality reasoning and complex tasks, but at $15–30 per million input tokens.
• Claude 3.5 Sonnet for creative writing, analysis, and coding, with different latency and cost characteristics.
• Gemini 2.0 for multimodal tasks and competitive pricing.
• Open-source models (Llama 3, Mistral) running on your own infrastructure or via providers like Replicate or Together AI for cost control.
• Specialized models like Anthropic’s prompt router or fine-tuned variants optimized for your domain.

Adding all these together doesn’t mean you use them all equally. It means you need a decision engine that routes each request to the model that delivers the best outcome for that specific use case.

Why? Because a simple customer support query doesn’t need GPT-4’s reasoning power—Claude 3.5 Haiku or a fine-tuned open-source model will do the job in 200ms for 1/10th the cost. But a complex financial analysis or code generation task might need the raw capability of GPT-4, even at higher cost. And a request that arrives at 2am on a Sunday might need to route to a different provider entirely if your primary provider is experiencing degradation.

This is where most teams fail. They either:

1. Over-provision: Use GPT-4 for everything because it’s the “safest” choice, burning cash unnecessarily.
2. Under-provision: Use the cheapest model everywhere, degrading quality and user trust.
3. Lock in: Pick one model, hardcode it, and then get stuck when pricing changes or a better alternative launches.

Model routing solves all three by making the routing decision explicit, measurable, and updatable.

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Static vs. Dynamic Routing Patterns

There are two fundamental approaches to model routing: static and dynamic. Both are valid; the choice depends on your maturity, infrastructure, and risk tolerance.

Static Routing

Static routing means you define routing rules upfront—often in configuration files or a simple decision tree—and they stay fixed until you explicitly change them. For example:

IF request.type == "customer_support" THEN route to Claude 3.5 Haiku
IF request.type == "code_generation" THEN route to GPT-4
IF request.type == "creative_writing" THEN route to Claude 3.5 Sonnet
IF request.complexity > 0.7 THEN route to GPT-4, else route to Claude 3.5 Haiku

Static routing is predictable, easy to audit, and simple to implement. It’s also brittle. If a new model releases that’s 30% cheaper and equally capable, you have to manually update your rules. If your primary provider experiences an outage, you need a pre-planned fallback.

Static routing works well for:

• Early-stage products with a small number of use cases.
• Highly regulated environments where routing decisions need to be auditable and fixed.
• Teams with limited infrastructure or ML ops maturity.

Dynamic Routing

Dynamic routing means your system evaluates routing decisions at runtime based on current conditions: model availability, cost, latency, quality metrics, and user context. Anthropic’s prompt router is a canonical example—it automatically selects between Claude models based on prompt characteristics and cost-quality tradeoffs without requiring manual rule updates.

Dynamic routing requires more infrastructure but delivers better outcomes:

• Cost optimization: Automatically routes to cheaper models when they’re equally capable.
• Quality preservation: Routes to higher-capability models when complexity demands it.
• Resilience: Automatically fails over to alternative providers without manual intervention.
• Adaptation: Learns from historical performance and adjusts routing over time.

Dynamic routing works well for:

• Mature products with high request volumes where cost optimization matters.
• Teams with observability infrastructure and ML ops expertise.
• Businesses where model release cycles are frequent and you need to adapt quickly.

Hybrid Approach

Most production systems use a hybrid: static rules for high-stakes decisions (compliance, security), dynamic routing for cost and performance optimization. For example:

IF request.requires_audit_trail THEN route to [approved_models] (static)
ELSE use dynamic routing to pick the best model from [approved_models] (dynamic)

This gives you the auditability and safety of static routing with the efficiency of dynamic routing.

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Building Your Routing Framework

A production-grade routing framework has five layers:

Layer 1: Request Classification

You need to classify each incoming request by:

• Task type: Customer support, code generation, analysis, creative writing, etc.
• Complexity: Simple (factual lookup), moderate (reasoning), high (multi-step reasoning).
• Latency requirement: Real-time (< 500ms), standard (< 2s), batch (no constraint).
• Cost sensitivity: Critical (cost per request matters), standard, flexible.
• Quality requirement: Acceptable (speed > quality), balanced, critical (quality > speed).

Classification can be rule-based (if request contains “generate code”, classify as code_generation) or ML-based (use a lightweight classifier to infer task type). Start with rules; add ML-based classification only if you have enough historical data to train reliably.

Layer 2: Capability Matching

Next, match the request’s requirements to available models:

• GPT-4 / GPT-4o: High reasoning, strong coding, multimodal, $15–30 per million tokens.
• Claude 3.5 Sonnet: Strong reasoning and creativity, lower latency than GPT-4, $3 per million tokens.
• Claude 3.5 Haiku: Fast, cheap, suitable for simple tasks, $0.80 per million tokens.
• Gemini 2.0: Competitive on cost and capability, strong multimodal, $0.075–2.50 per million tokens.
• Open-source (Llama 3, Mistral): Lowest cost if self-hosted, variable latency, suitable for non-sensitive tasks.

Build a capability matrix:

Model	Reasoning	Coding	Latency	Cost	Multimodal
GPT-4	9/10	9/10	2–4s	High	Yes
Claude 3.5 Sonnet	8/10	8/10	1–2s	Medium	No
Claude 3.5 Haiku	6/10	6/10	0.5–1s	Low	No
Gemini 2.0	7/10	7/10	1–2s	Low	Yes
Llama 3	6/10	7/10	0.5–2s	Very Low	No

Your routing logic then says: “For this request, I need reasoning >= 7 and latency < 1s. Which models qualify?” Then pick the cheapest qualified model.

Layer 3: Cost Calculation

Cost isn’t just per-token pricing. It includes:

• Token cost: Input tokens × input rate + output tokens × output rate.
• Latency cost: If latency matters to your user experience, a slower model might cost more in total value.
• Error cost: If a model’s error rate is higher, you might need to re-run requests, multiplying cost.
• Opportunity cost: If a user abandons your product because response is too slow, that’s a real cost.

For most teams starting out, focus on token cost and latency. As you mature, add error rates and user experience metrics.

Layer 4: Availability and Fallback

Every provider has outages. Your routing logic needs to handle:

• Provider degradation: If OpenAI’s API is slow, route to Claude or Gemini instead.
• Rate limiting: If you’ve hit quota on one provider, route to another.
• Explicit failures: If a request fails on GPT-4, retry on Claude with the same prompt.

Implement this with a fallback chain:

Try GPT-4
  If fails → Try Claude 3.5 Sonnet
    If fails → Try Gemini 2.0
      If fails → Try open-source fallback
        If fails → Return cached response or error

The key is to define fallback chains before you need them, not during an outage.

Layer 5: Observability and Feedback

You need to measure:

• Cost per request: Actual spend by model and use case.
• Latency by model: P50, P95, P99 response times.
• Quality by model: User satisfaction, error rates, downstream business metrics.
• Routing decisions: Which model was selected and why.

Log all of this. You’ll use it to update your routing strategy when models release.

For observability infrastructure, teams at PADISO often implement this via LiteLLM Proxy docs, which provides built-in logging and routing, or via custom middleware that logs to your observability stack (DataDog, Honeycomb, etc.).

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Cost and Performance Tradeoffs

Model routing is fundamentally about tradeoffs. You can’t optimize for cost, latency, and quality simultaneously. You have to pick your priority and build routing rules around it.

Cost-Optimized Routing

If your primary constraint is cost (common for B2B SaaS, content generation, customer support):

1. Tier your models by cost: Group models into cheap ($< $1 per million tokens), medium ($1–10), and expensive ($> $10).
2. Classify requests by quality requirement: Does this request need GPT-4 quality, or is Claude 3.5 Haiku acceptable?
3. Route to the cheapest model that meets the quality bar: For customer support, route to Haiku. For code generation, route to Sonnet. For complex reasoning, route to GPT-4.
4. Monitor quality metrics: Track user satisfaction, error rates, and downstream outcomes by model. If Haiku is producing poor results for a use case, bump it up to Sonnet.

Expected outcome: 30–50% reduction in inference costs with minimal quality loss.

Latency-Optimized Routing

If your primary constraint is latency (common for real-time chat, search, interactive tools):

1. Measure baseline latency for each model under production load.
2. Identify latency-critical paths: Which requests absolutely need to be < 500ms? Which can tolerate 2–3s?
3. Route latency-critical requests to the fastest models, even if they’re not the cheapest. Claude 3.5 Haiku is often faster than GPT-4.
4. Use batch processing for non-critical requests: If a request can wait, process it asynchronously and route to whatever model is available.
5. Implement caching: For common requests, cache responses. This is faster than any model.

Expected outcome: P95 latency < 500ms for user-facing requests, 20–30% cost reduction via caching.

Quality-Optimized Routing

If your primary constraint is quality (common for financial analysis, legal documents, high-stakes decisions):

1. Route to the highest-capability models (GPT-4, Claude 3.5 Sonnet) regardless of cost.
2. Implement quality checks: Use a lightweight model to evaluate output quality. If quality is low, re-run on a higher-capability model.
3. Use ensemble routing: Route the same request to multiple models and combine results (voting, averaging, or weighted scoring).
4. Monitor user feedback: Track how often users accept, reject, or edit model outputs. Use this to refine routing.

Expected outcome: 95%+ user satisfaction on quality, higher cost per request, but lower total cost when you account for reduced error handling and rework.

Balanced Routing

Most teams need a balanced approach:

• 70% of requests: Route to cheap, fast models (Haiku, Gemini 2.0). These are simple tasks where quality is less critical.
• 20% of requests: Route to mid-tier models (Claude 3.5 Sonnet). These need better quality or reasoning.
• 10% of requests: Route to premium models (GPT-4). These are high-stakes or highly complex.

This distribution delivers good quality at reasonable cost. Adjust the percentages based on your specific metrics.

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Implementing Fallback and Failover

Failure is inevitable. Your routing system needs to handle it gracefully.

Fallback Strategy

A fallback is a pre-planned alternative when your primary routing choice fails. Define fallback chains for each use case:

For customer support:

Primary: Claude 3.5 Haiku
Fallback 1: Claude 3.5 Sonnet (if Haiku fails)
Fallback 2: GPT-4 (if Sonnet fails)
Fallback 3: Cached response from similar past request
Fallback 4: Human escalation

For code generation:

Primary: GPT-4
Fallback 1: Claude 3.5 Sonnet (if GPT-4 is rate-limited)
Fallback 2: Gemini 2.0 (if both are unavailable)
Fallback 3: Open-source Llama 3 (if all else fails)
Fallback 4: Return error and queue for manual review

The key is to define fallbacks before you need them. Don’t wait for an outage to figure out what to do.

Failover Strategy

Failover is automatic switching to an alternative provider when the primary provider is degraded. Implement this via:

1. Health checks: Ping each provider’s API every 10–30 seconds to check availability.
2. Error rate monitoring: If a provider’s error rate spikes above a threshold (e.g., 5%), mark it as degraded.
3. Latency monitoring: If a provider’s P95 latency exceeds your SLA, route around it.
4. Automatic fallback: When the primary provider is degraded, automatically route to the fallback without manual intervention.

For example, using Amazon Bedrock Intelligent Prompt Routing, you can configure automatic failover across AWS-managed models. For multi-provider stacks, tools like LiteLLM Proxy docs handle fallback and failover across OpenAI, Anthropic, Google, and open-source providers.

Testing Failover

Test your fallback chains regularly. Use chaos engineering principles:

1. Simulate provider outages: Temporarily disable a provider in your routing logic and measure how your system behaves.
2. Measure fallback latency: How long does it take to fail over? Is it acceptable?
3. Monitor fallback cost: Using fallback models might be more expensive. Is that acceptable during an outage?
4. Audit fallback quality: Do fallback models produce acceptable quality? If not, adjust the fallback chain.

Run these tests monthly. Document the results. Update your fallback chains based on what you learn.

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Observability and Monitoring

You can’t optimize what you don’t measure. Build observability into your routing system from day one.

Metrics to Track

Cost metrics:

• Cost per request by model and use case.
• Total monthly spend by provider.
• Cost trend over time (are you optimizing or drifting higher?).
• Cost per unit of business value (cost per customer support ticket resolved, cost per code generation request, etc.).

Performance metrics:

• Latency (P50, P95, P99) by model and use case.
• Time to first token (for streaming responses).
• Error rate by model and use case.
• Fallback rate (how often do you fall back to alternative models?).

Quality metrics:

• User satisfaction by model (thumbs up/down, star ratings, NPS).
• Downstream business metrics (customer retention, conversion, churn).
• Error analysis (which types of errors occur most frequently?).

Routing metrics:

• Routing distribution (what % of requests go to each model?).
• Routing decision latency (how long does routing logic take?).
• Routing accuracy (did the chosen model produce acceptable output?).

Implementation

Log all routing decisions and outcomes. Send logs to a centralized observability platform. Build dashboards that show:

1. Real-time routing distribution: A pie chart showing what % of requests are routed to each model right now.
2. Cost trend: A line chart showing total cost per day/week/month.
3. Quality by model: A table showing user satisfaction, error rate, and downstream metrics for each model.
4. Latency distribution: A histogram showing P50, P95, P99 latency for each model.

Review these dashboards weekly. Use them to inform routing decisions.

For teams building on AWS, Databricks AI Gateway provides governance and observability for multi-model traffic. For OpenAI and multi-provider stacks, OpenAI’s routing guide documents best practices, and tools like LiteLLM provide built-in observability.

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Updating Strategy with Model Releases

This is where most teams fail. They build a routing framework, it works fine for 6 months, and then Claude 3.5 Sonnet releases and costs drop by 50%. Or GPT-5 launches and suddenly the capability bar shifts. Or a new open-source model becomes viable.

You need a repeatable process for updating your routing strategy when models release.

The Release Cycle

Major model releases happen every 3–6 months. Minor releases and price changes happen more frequently. Plan for this:

1. Monitor release announcements: Follow OpenAI, Anthropic, Google, Meta, and other major model providers. Subscribe to their release notes.
2. Test new models immediately: When a new model releases, add it to your test suite. Run your standard benchmark workloads and measure cost, latency, and quality.
3. Update your capability matrix: Revise your capability matrix with new models’ actual performance.
4. Recalculate routing rules: Based on new capabilities and pricing, should your routing rules change?
5. A/B test new routing rules: Before rolling out new routing rules to all traffic, test them on a subset of requests (5–10%) and measure outcomes.
6. Roll out gradually: If new routing rules perform well, gradually increase the percentage of traffic routed through them.
7. Monitor and adjust: Watch your metrics closely for the first week. If quality drops or cost spikes unexpectedly, roll back and investigate.

The Decision Framework

When a new model releases, ask:

1. Is it cheaper than my current model for this use case? If yes, test it.
2. Is it faster? If yes, test it.
3. Is it higher quality? If yes, test it.
4. Can I use it as a fallback? Even if it’s not better than my primary model, it might be a good fallback.
5. Does it enable new capabilities? (e.g., multimodal, longer context window). If yes, consider new use cases.

Build a simple scoring system:

Score = (cost_improvement × 0.3) + (latency_improvement × 0.3) + (quality_improvement × 0.4)

If Score > 0.1 (10% improvement), test the model.
If Score > 0.2 (20% improvement), roll out to 10% of traffic.
If Score > 0.3 (30% improvement), roll out to 50% of traffic.

Adjust the weights based on your priorities. If cost matters more than quality, increase the cost_improvement weight. If latency is critical, increase the latency_improvement weight.

Automation

Don’t do this manually. Automate the testing and evaluation:

1. Automated benchmarking: When a new model releases, automatically run your standard workloads against it and log results.
2. Automated A/B testing: Automatically route 5% of traffic to the new model and compare outcomes to the control group.
3. Automated alerts: If a new model outperforms your current model by > 20%, send an alert to your team.
4. Automated routing updates: If a new model passes your evaluation criteria, automatically update your routing rules to include it.

This requires infrastructure, but it’s worth the investment. Teams using AWS multi-LLM routing strategies often implement this via Lambda functions that run benchmarks on a schedule.

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Real-World Implementation Examples

Let’s walk through three realistic scenarios.

Scenario 1: Customer Support Chatbot

Requirements:

• Fast response (< 1s)
• Good quality (90%+ user satisfaction)
• Low cost (< $0.01 per request)

Initial routing:

IF request is simple_lookup THEN route to Claude 3.5 Haiku
ELSE IF request is moderate_complexity THEN route to Claude 3.5 Sonnet
ELSE route to GPT-4

Metrics after 1 month:

• Cost: $0.008 per request
• Latency: P95 = 400ms
• User satisfaction: 92%
• Routing distribution: 70% Haiku, 25% Sonnet, 5% GPT-4

When Gemini 2.0 releases:

• Test Gemini 2.0 on your benchmark workloads.
• Find that it’s 40% cheaper than Haiku with similar quality.
• A/B test: Route 10% of simple_lookup requests to Gemini 2.0.
• After 1 week: Gemini 2.0 performs well. User satisfaction is 91% (acceptable).
• Roll out: Update routing to use Gemini 2.0 for simple_lookup requests.
• New cost: $0.005 per request (37% reduction).

Scenario 2: Code Generation

Requirements:

• High quality (95%+ of generated code compiles and passes tests)
• Fast enough (< 3s)
• Cost is secondary

Initial routing:

IF request is simple_code THEN route to Claude 3.5 Sonnet
ELSE route to GPT-4

Metrics after 1 month:

• Cost: $0.12 per request
• Latency: P95 = 2.5s
• Code quality: 96% (compiles and passes tests)
• Routing distribution: 30% Sonnet, 70% GPT-4

When open-source Llama 3.2 becomes available via your infrastructure:

• Test Llama 3.2 on your code generation benchmarks.
• Find that it’s 90% cheaper but quality drops to 82% (code doesn’t compile 18% of the time).
• Decision: Use Llama 3.2 as a fallback for simple_code, but don’t replace Sonnet for primary routing.
• Updated routing:

  IF request is simple_code THEN try Llama 3.2
    IF fails or quality is low THEN fallback to Claude 3.5 Sonnet
  ELSE route to GPT-4

• New cost: $0.10 per request (17% reduction) with same quality.

Scenario 3: Financial Analysis

Requirements:

• Extremely high quality (99%+ accuracy on financial calculations)
• Compliance and auditability (must be able to explain which model was used)
• Cost is not a primary concern

Initial routing:

ALWAYS route to GPT-4
Log routing decision and reasoning for audit trail

Metrics after 1 month:

• Cost: $0.50 per request
• Accuracy: 99.2%
• Audit trail: Complete

When Claude 3.5 Sonnet is released:

• Test Claude 3.5 Sonnet on financial benchmarks.
• Find that accuracy is 98.8% (slightly lower than GPT-4, but still acceptable).
• Cost is 90% lower.
• Decision: Implement hybrid routing:

  IF request involves complex multi-step reasoning THEN route to GPT-4
  ELSE IF request is straightforward analysis THEN route to Claude 3.5 Sonnet
  Log routing decision for audit trail

• New cost: $0.35 per request (30% reduction) with acceptable accuracy.

These scenarios show the pattern: test new models, measure outcomes, update routing rules, monitor results, and iterate.

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Building for 2027

Model releases will accelerate. By 2027, you might see new models every 2–3 months instead of every 6 months. Your routing framework needs to be built for this pace of change.

Design Principles

1. Make routing decisions explicit and auditable.

Don’t hardcode model names in your application code. Use a routing service or configuration layer that you can update without code changes. This might be a dedicated routing service (like LiteLLM), a configuration file (YAML or JSON), or a feature flag system.

2. Separate routing logic from business logic.

Your application should say, “I need a model that can do X.” Your routing layer says, “Based on current conditions, use model Y.” This separation lets you update routing without touching your application code.

3. Measure everything.

You can’t optimize what you don’t measure. Build observability into your routing system from day one. Log every routing decision, every outcome, every cost.

4. Automate testing and rollout.

Manual testing and rollout is too slow. Build automation that tests new models, compares them to your current models, and rolls them out if they meet your criteria.

5. Plan for multi-modal and long-context.

By 2027, most models will be multimodal (text, image, video, audio) and have very long context windows (100K+ tokens). Your routing logic should account for these capabilities.

6. Plan for cost volatility.

Model pricing will continue to drop. Your routing logic should be cost-aware and automatically adapt as prices change. Don’t assume pricing is stable.

Infrastructure Recommendations

For teams building on AWS, use Amazon Bedrock Intelligent Prompt Routing as your foundation. It provides managed routing, fallback, and observability for AWS-managed models.

For multi-provider stacks (OpenAI, Anthropic, Google, open-source), use LiteLLM Proxy docs. It’s open-source, well-maintained, and provides routing, fallback, logging, and cost tracking across all major providers.

For teams wanting more control, build a custom routing service using the patterns described in this guide. Use IETF’s multi-provider inference API draft as a reference for API design.

Skill Development

Your engineering team needs to develop these skills:

1. Model evaluation: How to benchmark models objectively and measure quality.
2. Cost analysis: How to calculate true cost of inference including latency and error rates.
3. Observability: How to instrument code and build dashboards.
4. Infrastructure: How to manage multiple API keys, rate limits, and quotas across providers.
5. Experimentation: How to run A/B tests and measure statistical significance.

These are new skills for most teams. Plan for a 2–3 month ramp-up period.

Organizational Readiness

Model routing isn’t just a technical problem. It’s an organizational one. You need:

1. Clear ownership: Who owns routing decisions? Is it the ML team, the platform team, or a dedicated routing team?
2. Clear decision criteria: What metrics matter? Cost? Quality? Latency? Who decides the tradeoffs?
3. Clear process: How do you test new models? How do you roll out changes? Who approves them?
4. Clear communication: When you change routing, how do you communicate the change to stakeholders?

Document these clearly. Update them as you learn.

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Summary and Next Steps

Model routing is a core capability for AI-driven products in 2024 and beyond. Done well, it cuts costs by 30–50%, improves quality, and builds resilience. Done poorly, it becomes a maintenance nightmare.

Here’s your action plan:

Week 1: Audit Your Current Setup

1. Inventory your models: What models are you currently using? For what use cases?
2. Measure current costs: How much are you spending per request? Per use case?
3. Measure current quality: How satisfied are users? What’s your error rate?
4. Identify pain points: Where are you over-provisioning? Where are you under-provisioning?

Week 2–3: Build Your Framework

1. Define your routing layers: Request classification, capability matching, cost calculation, availability checks, observability.
2. Build your capability matrix: Which models are good at what?
3. Define your fallback chains: What happens when a model fails?
4. Set up observability: Log all routing decisions and outcomes.

If you’re building on AWS, start with Amazon Bedrock Intelligent Prompt Routing. If you’re using OpenAI and Anthropic, start with LiteLLM Proxy docs. If you’re building custom, reference AWS’s multi-LLM routing strategies and OpenAI’s routing guide.

Week 4: Test and Optimize

1. Implement your framework: Deploy your routing logic to production.
2. A/B test: Route a small percentage of traffic through your new routing logic. Measure outcomes.
3. Optimize: Based on results, adjust your routing rules.
4. Monitor: Watch your metrics closely for the first month.

Ongoing: Update Strategy with Releases

1. Monitor releases: Subscribe to model release announcements.
2. Test new models: When models release, add them to your test suite.
3. Update routing rules: If new models are better, update your rules.
4. Automate: Build automation to test and roll out new models.

If you need help, PADISO’s AI Advisory Services can help you design and implement a routing framework tailored to your stack and business model. We’ve worked with founders and operators building AI products across Australia and the US, and we’ve seen what works and what doesn’t.

For teams scaling AI operations, PADISO’s Platform Development services can help you build the infrastructure to support multi-provider routing at scale. For teams needing fractional technical leadership, PADISO’s Fractional CTO services provide hands-on guidance on AI architecture and strategy.

Model routing isn’t a one-time project. It’s an ongoing capability that you’ll refine continuously. Start simple, measure everything, and iterate. By 2027, you’ll have built a system that automatically adapts to new models and pricing—and your costs will reflect that efficiency.

The teams that win at AI aren’t the ones who pick the “best” model. They’re the ones who build systems that adapt as the landscape shifts. Build that system now, and you’ll be ahead of 95% of your competition by 2027.