Sample Stack: 100K Requests for $32/Month

Here's a concrete stack for a typical SaaS application handling 100K requests/month:

Compare that to using GPT-5 for all 100K requests: $588/month. The multi-model stack saves $556/month — enough to pay for the Pro tier of most SaaS tools.

How to Route Requests

The routing logic is simpler than you think. Here are three approaches, from simplest to most sophisticated:

Approach 1: Task-Type Routing (Simplest)

Route by endpoint or function — no AI needed:

// Simple task-type routing
function selectModel(taskType) {
  const routes = {
    'chat':        'gemini-2.0-flash',      // Budget
    'summarize':   'gemini-2.0-flash',      // Budget
    'extract':     'gemini-2.0-flash',      // Budget
    'classify':    'gemini-2.0-flash',      // Budget
    'code-review': 'gpt-5',                 // Mid-tier
    'analyze':     'gpt-5',                 // Mid-tier
    'write':       'gpt-5',                 // Mid-tier
    'legal':       'claude-opus-4.8',       // Premium
    'research':    'claude-opus-4.8',       // Premium
  };
  return routes[taskType] || 'gemini-2.0-flash';
}

Approach 2: Keyword/Rule-Based Routing

Check the request content for complexity signals:

// Rule-based complexity routing
function selectModel(request) {
  const text = request.toLowerCase();

  // Premium signals
  if (text.includes('analyze') && text.includes('legal')) return 'claude-opus-4.8';
  if (text.includes('reasoning') || text.includes('synthesize')) return 'claude-opus-4.8';

  // Mid-tier signals
  if (text.includes('code') || text.includes('review')) return 'gpt-5';
  if (text.includes('explain') && text.length > 500) return 'gpt-5';

  // Default to budget
  return 'gemini-2.0-flash';
}

Approach 3: Embedding-Based Classifier

For high-volume systems, train a lightweight classifier:

// Embedding-based routing (advanced)
async function selectModel(request) {
  // Use a cheap embedding model to classify
  const embedding = await getEmbedding(request);
  const complexity = await classifier.predict(embedding);

  if (complexity > 0.8) return 'claude-opus-4.8';
  if (complexity > 0.5) return 'gpt-5';
  return 'gemini-2.0-flash';
}

Start with Approach 1. Most teams don't need anything more sophisticated. You can always upgrade later.

Real Workload Breakdown

Let's walk through a concrete example — a developer tools SaaS with 100K monthly requests:

Workload Profile

Cost Calculation

Provider Diversification

A side benefit of multi-model stacks: you're not locked into one provider. If OpenAI has an outage, your budget tier (Gemini) and premium tier (Claude) still work. If Anthropic raises prices, you shift premium traffic to GPT-5.5.

Recommended provider distribution:

• Budget tier: Google (Gemini Flash) or DeepSeek — lowest prices, highest rate limits
• Mid tier: OpenAI (GPT-5) or Anthropic (Claude Sonnet) — best quality/cost balance
• Premium tier: Anthropic (Claude Opus 4.8) or OpenAI (GPT-5.5) — best reasoning

This gives you redundancy across 3 providers. If one has issues, 66-90% of your traffic is unaffected.

Implementation Checklist

1. Audit your requests — categorize by complexity (simple/moderate/complex)
2. Choose your budget model — Gemini Flash for most, DeepSeek Flash for output-heavy
3. Choose your mid-tier — GPT-5 for general, Claude Sonnet for code/analysis
4. Choose your premium (optional) — Claude Opus 4.8 for critical reasoning
5. Implement routing — start with task-type routing, upgrade if needed
6. Monitor and adjust — track costs per tier, shift traffic as needed

Common Mistakes to Avoid

1. Over-routing to premium

If more than 15% of requests hit your premium tier, your routing logic is too aggressive. Most "complex" tasks work fine on mid-tier models.

2. Ignoring output token costs

A model with cheap input but expensive output (like GPT-5 mini at $0.25/$2.00) costs more than it looks for chat workloads. Always check both input AND output pricing.

3. Not testing quality

Before routing a task type to a budget model, test it. Run 100 real requests through both models and compare. Budget models handle 80%+ of tasks well, but some edge cases need premium.

4. Overcomplicating the router

Start simple. Task-type routing handles most cases. Don't build an embedding-based classifier until you've proven simple routing isn't enough.

Find Where You're Overpaying

Already using AI APIs? You're probably overpaying for at least some requests.

Our Cost Leak Detector analyzes your current model and usage, then shows exactly which cheaper alternatives would save you money — with estimated monthly savings.

Bottom Line

A multi-model AI stack isn't complicated. It's 2-3 models, a simple router, and the discipline to match model capability to task complexity. The payoff is massive: 60-95% cost savings with no quality loss on most workloads.

Start with two models — a budget model (Gemini Flash) for simple tasks and a mid-tier model (GPT-5 or Claude Sonnet) for everything else. Add a premium model only when you have tasks that genuinely need it. You'll be surprised how far the budget tier can go.

Related: Cost Leak Detector · Cheap AI APIs Under $0.50 · Cost Calculator · Full Pricing (67 models)