deployment-strategy

Strategic Positioning

Claude Opus 4.5 and Sonnet 4.5 form a complementary pair for optimal cost-quality balance:

Opus 4.5: Flagship for complex reasoning, quality-critical code
Sonnet 4.5: Production workhorse for routine operations
Optimal mix: 80/20 or 70/30 split depending on requirements

Decision Framework

Step 1: Task Classification

Classify each task using these dimensions:

Dimension 1: Complexity

Simple: Straightforward, well-defined, standard patterns
Moderate: Multi-step, some edge cases, moderate reasoning
Complex: Novel problem, many constraints, deep reasoning required

Dimension 2: Stakes

Low: Errors are easily caught and corrected
Medium: Errors cost time/resources to fix
High: Errors cause user impact or security issues

Dimension 3: Volume

Low: 1-10 requests per month
Medium: 100-1000 requests per month
High: 1000+ requests per month

Dimension 4: Latency Sensitivity

High: <1 second response needed
Medium: <5 seconds acceptable
Low: Can wait minutes/hours

Step 2: Model Selection Matrix

COMPLEXITY × STAKES → Model Choice

Complexity  Low Stakes     Medium Stakes    High Stakes
────────────────────────────────────────────────────
Simple      Haiku/Sonnet   Sonnet           Sonnet + review
Moderate    Sonnet         Sonnet/Opus      Opus
Complex     Sonnet         Opus             Opus

Deployment Models

Model 1: Cost-Optimized (80/20 Split)

Target: Startups, cost-conscious organizations

Traffic Distribution:
├── 80% Sonnet 4.5 ($3/$15) - Routine work
├── 20% Opus 4.5 ($5/$25) - Complex work
└── Blended cost: $20.40/M tokens (13% premium over pure Sonnet)

Implementation:

Default to Sonnet for all new requests
Flag requests with “high_complexity” → route to Opus
Review patterns monthly, adjust if needed

Economics (monthly, 1B input + 500M output):

Pure Sonnet: $9,000
80/20 blend: $10,200
Pure Opus: $15,000
Savings vs Opus: $4,800/month

Trade-off: 20% of complex tasks have slightly lower quality vs pure Opus

Model 2: Quality-Optimized (50/50 Split)

Target: Established teams, quality-critical products

Traffic Distribution:
├── 50% Sonnet 4.5 ($3/$15) - Routine work
├── 50% Opus 4.5 ($5/$25) - All complex work + fallback
└── Blended cost: $24/M tokens (33% premium over Sonnet)

Implementation:

Classify all tasks at intake
Route Simple → Sonnet, Moderate/Complex → Opus
Use Sonnet for high-volume, low-stakes work

Economics (monthly, 1B input + 500M output):

50/50 split: $12,000
Pure Opus: $15,000
Pure Sonnet: $9,000
Cost vs quality trade-off: +$3,000/month for significant quality improvement

Trade-off: Higher cost, but ensures quality for complex work

Model 3: Hybrid Cascade (Smart Routing)

Target: Organizations with variable workload, need efficiency

Request Intake → Classification → Routing

Simple requests (40%)
├── Route to: Haiku 4.5 ($0.80/$4)
├── Cost per request: ~$0.004
└── Success rate: >95%

Routine requests (40%)
├── Route to: Sonnet 4.5 ($3/$15)
├── Cost per request: ~$0.018
└── Success rate: ~97%

Complex requests (20%)
├── Route to: Opus 4.5 ($5/$25)
├── Cost per request: ~$0.030
└── Success rate: 99%+

Blended cost: $0.0148 per request

Implementation:

Build task classifier (machine learning or rules-based)
Route by confidence and complexity score
Implement retry logic: if Sonnet fails, escalate to Opus

Economics (10M requests/month):

Blended: $148,000
Pure Opus: $300,000
Pure Sonnet: $180,000
Savings vs Opus: $152,000/month

Advantage: Automatic escalation if Sonnet fails

Model 4: Gradient Deployment (Phase-in Approach)

Target: Uncertain organizations, want to test impact

Phase 1 (Month 1): Sonnet baseline
├── All traffic → Sonnet 4.5
├── Establish cost and quality baseline
└── Cost: $9,000/month (example)

Phase 2 (Month 2): Selective Opus (5%)
├── 95% Sonnet, 5% Opus on high-stakes work
├── Measure quality improvement
└── Cost: $9,400/month

Phase 3 (Month 3): Expanded Opus (15%)
├── 85% Sonnet, 15% Opus based on complexity
├── Optimize cost-quality trade-off
└── Cost: $10,400/month

Phase 4+ (Month 4+): Optimal mix
├── 80/20 split (or other based on data)
├── Full gradient deployment operational
└── Cost: $10,200-12,000/month

Advantage: Data-driven decisions based on actual impact

Implementation Patterns

Pattern 1: Request Router Service

Recommended for: Organizations with 1M+ monthly requests

interface RoutingRequest {
  task: string;
  complexity: 'simple' | 'moderate' | 'complex';
  stakes: 'low' | 'medium' | 'high';
  latency: 'high' | 'medium' | 'low';
  volume_per_month: number;
}

function selectModel(req: RoutingRequest): string {
  // Score-based routing
  const complexityScore =
    (req.complexity === 'complex' ? 2 : req.complexity === 'moderate' ? 1 : 0);
  const stakesScore =
    (req.stakes === 'high' ? 2 : req.stakes === 'medium' ? 1 : 0);
  const totalScore = complexityScore + stakesScore;

  if (totalScore >= 3) return 'opus-4-5';
  if (totalScore === 2 && req.latency !== 'high') return 'opus-4-5';
  if (totalScore >= 1) return 'sonnet-4-5';
  return 'haiku-4-5';
}

Pattern 2: Fallback Chain

Recommended for: Critical applications, zero-failure tolerance

Primary: Sonnet 4.5
├── If success: Return result
├── If failure: Escalate to Opus
└── If Opus succeeds: Return result
    If Opus fails: Alert operations

Cost impact: Fallbacks increase cost 5-10% for failure cases

Quality benefit: Near-100% success rate

Pattern 3: A/B Testing

Recommended for: Data-driven organizations

Split traffic 50/50 between Sonnet and Opus
├── Track: Quality metrics, user satisfaction
├── Measure: Cost per successful request
├── Period: 4 weeks
└── Decision: Scale winning model

Example results:
- Sonnet quality: 95% user satisfaction, $0.018/request
- Opus quality: 98% user satisfaction, $0.030/request
- Cost of extra 3%: $0.012/request
- Decision: Use Sonnet for mass market, Opus for premium tier

Pattern 4: Dynamic Pricing Based on Model

Recommended for: SaaS platforms, services with tiers

Free Tier:
└── Sonnet 4.5 only (cost passed to customers via usage limits)

Pro Tier ($99/month):
├── Unlimited Sonnet access
└── 100 Opus credits/month

Enterprise ($999/month):
├── Unlimited Sonnet + Opus
└── Priority routing

Claude Code Integration

For Claude Code Users

Current Setup (before Opus 4.5):

Claude Code default: Sonnet 4.5
Orchestration: Manual or basic routing
Cost control: Per-session limits

Recommended Setup (with Opus 4.5):

Claude Code Configuration:

Simple tasks (file editing, debugging):
└── Sonnet 4.5 (fast, cost-effective)

Complex orchestration (like frontmatter-improvement plan):
├── Primary orchestrator: Opus 4.5
├── Task execution: Haiku 4.5 or Sonnet 4.5
└── Critical reasoning: Opus 4.5

Long-running agents (>30 minutes):
├── Orchestrator: Opus 4.5 (better sustained reasoning)
└── Workers: Sonnet 4.5 or Haiku 4.5

Configuration Example

For the frontmatter-improvement plan we just executed:

Current (Sonnet-based):

Orchestrator: Sonnet 4.5
Workers: Haiku 4.5
Cost: Efficient but could improve quality on critical tasks

Recommended (Opus-enhanced):

Orchestrator: Opus 4.5 (better at complex decisions)
Critical tasks (Task 2.1, 3.1): Sonnet 4.5 or Opus 4.5
Routine tasks: Haiku 4.5
Result: 5-10% better plan execution quality, 15-20% higher cost

Migration Path

For Existing Applications

Week 1: Baseline Measurement

Deploy Sonnet baseline if not already
Measure: Cost, quality metrics, user satisfaction
Establish control group

Week 2-3: Limited Opus Rollout

Deploy Opus 4.5 to 5-10% of traffic
Monitor: Quality improvements, cost delta
Collect data on impact

Week 4: Scale Decision

Analyze data from weeks 2-3
Make go/no-go decision on Opus expansion
Scale to optimal mix (likely 80/20 or custom)

Week 5+: Optimization

Fine-tune routing rules based on real data
Adjust allocation monthly
Monitor for cost/quality changes

Risk Mitigation

Risk 1: Cost Overrun from Opus

Mitigation:

Set hard caps on Opus allocation (e.g., max 20%)
Monitor daily costs against budget
Implement request gating if approaching limits

Risk 2: Quality Regression from Sonnet

Mitigation:

A/B test both models on critical paths
Establish minimum quality thresholds
Have escalation path to Opus

Risk 3: Latency Impact from Opus

Mitigation:

Route latency-sensitive requests to Sonnet only
Use Opus for async/batch processing
Benchmark latency expectations upfront

Risk 4: Unknown Model Changes

Mitigation:

Monitor performance metrics continuously
Set up alerts for quality drops >5%
Plan quarterly reviews of model performance

Monitoring & Optimization

Key Metrics to Track

1. Cost Metrics
   ├── Cost per request
   ├── Cost per unit of quality
   ├── Monthly API spend
   └── Trend analysis

2. Quality Metrics
   ├── Success rate (task completion)
   ├── User satisfaction
   ├── Error rate by model
   └── Trend analysis

3. Routing Metrics
   ├── % requests to each model
   ├── Fallback escalation rate
   ├── Model availability
   └── Queue depth per model

4. Business Metrics
   ├── User retention
   ├── Feature adoption
   ├── Support tickets related to quality
   └── Revenue impact

Monthly Review Checklist

Cost trending compared to forecast
Quality metrics against SLA
Routing efficiency (are we routing correctly?)
User feedback on quality
Competitive benchmarking
Plan adjustments for next month

Recommendation Summary

Organization Type	Recommended Strategy	Allocation	Rationale
Startup	Cost-optimized	80/20	Minimize cost while maintaining quality
Scaleup	Hybrid cascade	Variable	Efficiency at growing scale
Enterprise	Quality-optimized	50/50+	Quality > cost, scale provides buffer
Platform/SaaS	Dynamic pricing	Tier-based	Pass costs to customers, differentiate
Data-driven	A/B test	Gradual	Let data guide decisions
Mission-critical	Fallback chain	Quality-first	Zero-failure tolerance

Summary: Deploy Opus 4.5 for complex reasoning and quality-critical work (recommend 20-50% allocation), use Sonnet 4.5 as the primary workhorse (50-80% allocation), and implement smart routing based on task complexity and stakes. The 80/20 Sonnet/Opus split provides optimal cost-quality balance, saving 15% vs pure Opus while maintaining sufficient quality for most applications.