Cost Optimization Strategies
Purpose: Comprehensive cost management strategies for evaluation framework composition, achieving 30-50% cost reduction while maintaining quality standards.
Key Principle: Smart evaluation spending - optimize cost without compromising critical quality requirements.
Cost Analysis Overview
Framework Cost Breakdown (Monthly Estimates for 10K evaluations)
yaml
framework_costs:
core_frameworks:
semantic_kernel: "$500-1500/month" # Azure AI Foundry consumption
ragas: "$50-200/month" # LLM API calls for judgment
deepeval: "$50-200/month" # Local compute + occasional API
core_total: "$600-1900/month"
enhanced_frameworks:
langsmith: "$200-800/month" # Platform subscription + usage
trulens: "$200-1000/month" # SaaS subscription tiers
enhanced_total: "$400-1800/month"
specialized_frameworks:
hf_evaluate: "$20-100/month" # Mostly local compute
llm_judge: "$100-500/month" # API costs for LLM evaluation
specialized_total: "$120-600/month"
total_without_optimization: "$1120-4300/month"
total_with_optimization: "$670-2150/month" # 30-50% savingsCost Driver Analysis
python
class CostDriverAnalyzer:
"""Analyze and categorize evaluation costs"""
def analyze_cost_drivers(self, evaluation_history: List[dict]) -> dict:
"""Break down costs by category"""
cost_breakdown = {
'api_calls': 0.0, # LLM API costs (GPT-4, Claude, etc.)
'cloud_services': 0.0, # Azure, AWS, GCP consumption
'platform_fees': 0.0, # SaaS subscription costs
'compute': 0.0, # Local/cloud compute resources
'storage': 0.0, # Data and trace storage
'data_transfer': 0.0 # Network costs
}
for evaluation in evaluation_history:
framework = evaluation['framework']
cost = evaluation['cost_usd']
# Categorize cost by framework type
if framework in ['semantic_kernel']:
cost_breakdown['cloud_services'] += cost
elif framework in ['ragas', 'llm_judge']:
cost_breakdown['api_calls'] += cost
elif framework in ['langsmith', 'trulens']:
cost_breakdown['platform_fees'] += cost
elif framework in ['deepeval', 'hf_evaluate']:
cost_breakdown['compute'] += cost
return {
'breakdown': cost_breakdown,
'total': sum(cost_breakdown.values()),
'top_driver': max(cost_breakdown.items(), key=lambda x: x[1])[0]
}Optimization Strategy #1: Intelligent Caching
Cache Architecture
python
class EvaluationCache:
"""Multi-level caching for evaluation results"""
def __init__(self):
self.memory_cache = {} # Fast in-memory cache
self.redis_cache = redis.Redis() # Distributed cache
self.persistent_storage = PostgreSQL() # Long-term storage
self.cache_ttl = {
'memory': 300, # 5 minutes
'redis': 3600, # 1 hour
'persistent': 86400 # 24 hours
}
def generate_cache_key(self, request: EvaluationRequest) -> str:
"""Generate semantic cache key"""
# Normalize request for caching
normalized_query = self._normalize_query(request.query)
context_hash = self._hash_context(request.context)
cache_key = f"{request.layer}:{normalized_query}:{context_hash}"
return hashlib.sha256(cache_key.encode()).hexdigest()[:16]
async def get_cached_result(
self,
cache_key: str
) -> Optional[EvaluationResult]:
"""Multi-level cache lookup"""
# Level 1: Memory cache (fastest)
if result := self.memory_cache.get(cache_key):
return result
# Level 2: Redis cache (fast, distributed)
if cached_data := await self.redis_cache.get(cache_key):
result = EvaluationResult.from_json(cached_data)
self.memory_cache[cache_key] = result # Promote to memory
return result
# Level 3: Persistent storage (slower, but comprehensive)
if cached_result := await self.persistent_storage.get(cache_key):
result = EvaluationResult.from_dict(cached_result)
# Promote to higher cache levels
await self.redis_cache.setex(cache_key, self.cache_ttl['redis'], result.to_json())
self.memory_cache[cache_key] = result
return result
return None
def _normalize_query(self, query: str) -> str:
"""Normalize query for better cache hits"""
import re
# Remove extra whitespace, normalize case
normalized = re.sub(r'\s+', ' ', query.lower().strip())
# Remove common variations that don't affect evaluation
normalized = re.sub(r'\b(please|can you|could you)\b', '', normalized)
return normalizedCache Hit Rate Optimization
python
class CacheOptimizer:
"""Optimize cache performance for maximum cost savings"""
def __init__(self):
self.hit_rate_target = 0.7 # Target 70% cache hit rate
self.current_hit_rate = 0.0
async def optimize_cache_strategy(self, evaluation_patterns: dict) -> dict:
"""Analyze patterns and optimize caching strategy"""
optimization_recommendations = {
'semantic_clustering': self._analyze_query_clustering(evaluation_patterns),
'temporal_patterns': self._analyze_temporal_patterns(evaluation_patterns),
'layer_patterns': self._analyze_layer_patterns(evaluation_patterns),
'ttl_optimization': self._optimize_cache_ttl(evaluation_patterns)
}
return optimization_recommendations
def _analyze_query_clustering(self, patterns: dict) -> dict:
"""Identify semantically similar queries for better caching"""
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
queries = patterns['recent_queries']
# Vectorize queries
vectorizer = TfidfVectorizer(stop_words='english')
query_vectors = vectorizer.fit_transform(queries)
# Find similarity clusters
similarity_matrix = cosine_similarity(query_vectors)
# Identify high-similarity clusters (>0.8 similarity)
clusters = []
for i, row in enumerate(similarity_matrix):
similar_queries = [j for j, sim in enumerate(row) if sim > 0.8 and j != i]
if similar_queries:
clusters.append({
'representative_query': queries[i],
'similar_queries': [queries[j] for j in similar_queries],
'cache_opportunity': len(similar_queries)
})
return {
'clusters_found': len(clusters),
'estimated_hit_rate_improvement': sum(c['cache_opportunity'] for c in clusters) / len(queries),
'recommendations': 'Implement semantic query normalization'
}
# Example cache hit rate improvement
cache_performance_example = {
'before_optimization': {
'hit_rate': '45%',
'monthly_api_costs': '$1200',
'evaluation_latency': '2.3s average'
},
'after_optimization': {
'hit_rate': '72%',
'monthly_api_costs': '$420', # 65% reduction
'evaluation_latency': '0.8s average', # 65% faster
'cost_savings': '$780/month'
}
}Optimization Strategy #2: Adaptive Framework Selection
Budget-Aware Framework Router
python
class BudgetAwareRouter:
"""Smart framework selection based on budget constraints"""
def __init__(self, budget_manager: BudgetManager):
self.budget_manager = budget_manager
self.framework_cost_models = self._initialize_cost_models()
# Framework tiers by cost (cheapest to most expensive)
self.framework_tiers = {
'budget': ['hf_evaluate', 'deepeval'],
'standard': ['ragas', 'llm_judge'],
'premium': ['semantic_kernel', 'langsmith', 'trulens']
}
async def select_framework_by_budget(
self,
request: EvaluationRequest
) -> List[str]:
"""Select optimal frameworks within budget constraints"""
# Get current budget status
budget_status = await self.budget_manager.get_budget_status()
remaining_budget = budget_status['daily_remaining']
# Determine budget tier
if remaining_budget < self.budget_manager.daily_budget * 0.2:
# <20% budget remaining - use budget frameworks
selected_tier = 'budget'
max_frameworks = 1
elif remaining_budget < self.budget_manager.daily_budget * 0.5:
# <50% budget remaining - use standard frameworks
selected_tier = 'standard'
max_frameworks = 2
else:
# Sufficient budget - can use premium frameworks
selected_tier = 'premium'
max_frameworks = 3 if request.priority == 'critical' else 2
# Select frameworks from tier
available_frameworks = self.framework_tiers[selected_tier]
# Filter by layer compatibility
layer_compatible = [
fw for fw in available_frameworks
if self._is_layer_compatible(fw, request.layer)
]
# Select up to max_frameworks, prioritizing by quality score
selected = sorted(
layer_compatible,
key=lambda fw: self._get_quality_score(fw),
reverse=True
)[:max_frameworks]
return selected
def _estimate_total_cost(
self,
frameworks: List[str],
request: EvaluationRequest
) -> float:
"""Estimate total cost for framework combination"""
total_cost = 0.0
for framework in frameworks:
cost_model = self.framework_cost_models[framework]
framework_cost = cost_model.estimate_cost(request)
total_cost += framework_cost
return total_cost
class DynamicBudgetAllocation:
"""Dynamically allocate budget based on evaluation priorities"""
def __init__(self, total_daily_budget: float):
self.total_daily_budget = total_daily_budget
self.allocation_strategy = {
'critical': 0.4, # 40% of budget for critical evaluations
'high': 0.3, # 30% for high priority
'medium': 0.2, # 20% for medium priority
'low': 0.1 # 10% for low priority
}
def get_priority_budget(self, priority: str) -> float:
"""Get allocated budget for priority level"""
return self.total_daily_budget * self.allocation_strategy[priority]
async def reallocate_budget(self, usage_patterns: dict) -> dict:
"""Dynamically reallocate budget based on usage"""
# Analyze actual vs. planned usage
actual_usage = {
priority: usage_patterns[f'{priority}_usage']
for priority in self.allocation_strategy.keys()
}
# Reallocate unused budget from lower to higher priorities
reallocation = {}
for priority in ['low', 'medium', 'high', 'critical']:
allocated = self.allocation_strategy[priority] * self.total_daily_budget
used = actual_usage[priority]
if used < allocated * 0.8: # Used <80% of allocation
surplus = allocated - used
# Reallocate surplus to higher priorities
if priority != 'critical':
higher_priorities = list(self.allocation_strategy.keys())[
list(self.allocation_strategy.keys()).index(priority) + 1:
]
surplus_per_higher = surplus / len(higher_priorities)
for higher_priority in higher_priorities:
if higher_priority not in reallocation:
reallocation[higher_priority] = 0
reallocation[higher_priority] += surplus_per_higher
return reallocationOptimization Strategy #3: Progressive Evaluation Depth
Adaptive Quality-Cost Tradeoffs
python
class ProgressiveEvaluationEngine:
"""Implement progressive evaluation depth based on importance and budget"""
def __init__(self):
self.evaluation_tiers = {
'basic': {
'frameworks': 1,
'metrics': ['accuracy', 'basic_safety'],
'cost_multiplier': 1.0,
'confidence': 0.7
},
'standard': {
'frameworks': 2,
'metrics': ['accuracy', 'relevance', 'safety', 'coherence'],
'cost_multiplier': 2.0,
'confidence': 0.85
},
'comprehensive': {
'frameworks': 3,
'metrics': ['accuracy', 'relevance', 'safety', 'coherence', 'bias', 'hallucination'],
'cost_multiplier': 3.5,
'confidence': 0.95
}
}
async def evaluate_progressively(
self,
request: EvaluationRequest,
budget_constraint: float
) -> EvaluationResult:
"""Progressive evaluation - start basic, upgrade if budget allows"""
# Start with basic evaluation
basic_result = await self._evaluate_tier('basic', request)
# Check if we can afford standard evaluation
if budget_constraint >= self._estimate_tier_cost('standard', request):
standard_result = await self._evaluate_tier('standard', request)
# If basic and standard disagree significantly, might need comprehensive
if self._results_disagree(basic_result, standard_result):
if budget_constraint >= self._estimate_tier_cost('comprehensive', request):
comprehensive_result = await self._evaluate_tier('comprehensive', request)
return comprehensive_result
else:
# Flag for manual review due to disagreement
standard_result.metadata['requires_manual_review'] = True
return standard_result
else:
return standard_result
else:
return basic_result
def _results_disagree(
self,
result1: EvaluationResult,
result2: EvaluationResult,
threshold: float = 0.15
) -> bool:
"""Check if evaluation results disagree significantly"""
score_diff = abs(result1.overall_score - result2.overall_score)
return score_diff > threshold
# Example progressive evaluation savings
progressive_evaluation_example = {
'traditional_approach': {
'all_requests_comprehensive': True,
'average_cost_per_evaluation': '$0.15',
'monthly_cost_10k_evaluations': '$1500'
},
'progressive_approach': {
'basic_evaluations': '60%', # $0.05 each
'standard_evaluations': '30%', # $0.10 each
'comprehensive_evaluations': '10%', # $0.15 each
'average_cost_per_evaluation': '$0.075',
'monthly_cost_10k_evaluations': '$750',
'cost_savings': '50%'
}
}Optimization Strategy #4: Batch Processing and Scheduling
Intelligent Batch Optimization
python
class BatchOptimizationEngine:
"""Optimize evaluation batching for cost and performance"""
def __init__(self):
self.batch_size_limits = {
'semantic_kernel': 50, # Azure API limits
'ragas': 100, # Good batch size for RAGAS
'deepeval': 200, # Local processing, larger batches OK
'langsmith': 25, # Platform optimization
'trulens': 75 # Balanced batch size
}
self.batch_cost_discounts = {
'semantic_kernel': 0.15, # 15% discount for batches >25
'ragas': 0.10, # 10% discount for batches >50
'langsmith': 0.20 # 20% discount for batches >10
}
async def optimize_batch_schedule(
self,
evaluation_queue: List[EvaluationRequest],
time_constraints: dict
) -> List[EvaluationBatch]:
"""Create optimal batching schedule"""
# Group by framework compatibility
framework_groups = self._group_by_framework_compatibility(evaluation_queue)
# Create optimal batches
optimized_batches = []
for framework, requests in framework_groups.items():
max_batch_size = self.batch_size_limits.get(framework, 50)
# Create batches of optimal size
for i in range(0, len(requests), max_batch_size):
batch_requests = requests[i:i + max_batch_size]
batch = EvaluationBatch(
framework=framework,
requests=batch_requests,
estimated_cost=self._calculate_batch_cost(framework, batch_requests),
estimated_duration=self._estimate_batch_duration(framework, batch_requests),
priority=max(req.priority for req in batch_requests)
)
optimized_batches.append(batch)
# Schedule batches by priority and cost efficiency
scheduled_batches = sorted(
optimized_batches,
key=lambda b: (b.priority, -b.cost_efficiency_score)
)
return scheduled_batches
def _calculate_batch_cost(
self,
framework: str,
requests: List[EvaluationRequest]
) -> float:
"""Calculate batch cost with volume discounts"""
base_cost = sum(
self._estimate_single_request_cost(framework, req)
for req in requests
)
# Apply batch discount if applicable
if len(requests) >= 10 and framework in self.batch_cost_discounts:
discount = self.batch_cost_discounts[framework]
discounted_cost = base_cost * (1 - discount)
return discounted_cost
return base_cost
class OffPeakScheduler:
"""Schedule non-urgent evaluations during off-peak hours for cost savings"""
def __init__(self):
# Define peak and off-peak hours (UTC)
self.peak_hours = [8, 9, 10, 11, 12, 13, 14, 15, 16, 17] # Business hours
self.off_peak_multiplier = 0.7 # 30% discount during off-peak
def is_off_peak_time(self, timestamp: datetime = None) -> bool:
"""Check if current time is off-peak"""
if timestamp is None:
timestamp = datetime.utcnow()
return timestamp.hour not in self.peak_hours
async def schedule_evaluation(
self,
request: EvaluationRequest
) -> EvaluationSchedule:
"""Schedule evaluation optimally"""
if request.priority in ['critical', 'high']:
# Execute immediately regardless of time
return EvaluationSchedule(
execute_at=datetime.utcnow(),
cost_multiplier=1.0,
reason='high_priority_immediate'
)
# For medium/low priority, consider off-peak scheduling
if self.is_off_peak_time():
return EvaluationSchedule(
execute_at=datetime.utcnow(),
cost_multiplier=self.off_peak_multiplier,
reason='off_peak_discount'
)
else:
# Schedule for next off-peak period
next_off_peak = self._next_off_peak_time()
return EvaluationSchedule(
execute_at=next_off_peak,
cost_multiplier=self.off_peak_multiplier,
reason='scheduled_off_peak'
)Budget Monitoring and Alerting
Real-Time Budget Dashboard
python
class EvaluationBudgetDashboard:
"""Real-time monitoring and alerting for evaluation costs"""
def __init__(self, budget_limits: dict):
self.budget_limits = budget_limits
self.alert_thresholds = {
'warning': 0.75, # 75% of budget
'critical': 0.90, # 90% of budget
'emergency': 0.95 # 95% of budget
}
async def get_budget_status(self) -> dict:
"""Get current budget status across all categories"""
current_spend = await self._get_current_spend()
status = {}
for category, limit in self.budget_limits.items():
spent = current_spend.get(category, 0.0)
remaining = limit - spent
utilization = spent / limit if limit > 0 else 0
# Determine alert level
alert_level = 'normal'
for level, threshold in self.alert_thresholds.items():
if utilization >= threshold:
alert_level = level
status[category] = {
'limit': limit,
'spent': spent,
'remaining': remaining,
'utilization': utilization,
'alert_level': alert_level,
'projected_monthly': spent * 30, # Rough projection
}
return status
async def check_budget_alerts(self) -> List[BudgetAlert]:
"""Check for budget alerts and generate notifications"""
alerts = []
budget_status = await self.get_budget_status()
for category, status in budget_status.items():
if status['alert_level'] != 'normal':
alert = BudgetAlert(
category=category,
level=status['alert_level'],
message=f"{category} budget at {status['utilization']:.1%} utilization",
current_spend=status['spent'],
budget_limit=status['limit'],
recommendation=self._get_budget_recommendation(status),
timestamp=datetime.utcnow()
)
alerts.append(alert)
return alerts
def _get_budget_recommendation(self, status: dict) -> str:
"""Generate cost optimization recommendation"""
if status['alert_level'] == 'warning':
return "Consider enabling aggressive caching and using budget-tier frameworks"
elif status['alert_level'] == 'critical':
return "Switch to budget frameworks only, enable maximum caching"
elif status['alert_level'] == 'emergency':
return "Halt non-critical evaluations, manual approval required"
return "No action needed"
# Example budget monitoring
budget_monitoring_example = {
'daily_limits': {
'total_evaluation': 100.0, # $100/day total
'api_costs': 60.0, # $60/day for API calls
'cloud_services': 30.0, # $30/day for cloud
'platform_fees': 10.0 # $10/day for SaaS
},
'current_status': {
'total_evaluation': {
'spent': 73.50,
'remaining': 26.50,
'utilization': '73.5%',
'alert_level': 'normal'
},
'api_costs': {
'spent': 52.30,
'remaining': 7.70,
'utilization': '87.2%',
'alert_level': 'critical',
'recommendation': 'Switch to budget frameworks, enable caching'
}
}
}Implementation Checklist
Phase 1: Basic Cost Optimization (Week 1-2)
[ ] Implement evaluation caching system
- [ ] Multi-level cache (memory, Redis, persistent)
- [ ] Semantic query normalization
- [ ] Cache hit rate monitoring
- [ ] Target: 60%+ cache hit rate
[ ] Deploy budget-aware framework selection
- [ ] Framework tier classification
- [ ] Real-time budget tracking
- [ ] Automatic tier downgrade when budget low
- [ ] Target: Stay within daily budget 95%+ of time
[ ] Basic batch optimization
- [ ] Batch similar requests by framework
- [ ] Implement volume discounts where available
- [ ] Target: 15-25% cost reduction through batching
Phase 2: Advanced Optimization (Week 3-4)
[ ] Progressive evaluation implementation
- [ ] Multi-tier evaluation depth
- [ ] Disagreement detection and escalation
- [ ] Confidence-based quality assurance
- [ ] Target: 30% cost reduction while maintaining quality
[ ] Off-peak scheduling system
- [ ] Peak/off-peak hour detection
- [ ] Priority-based scheduling logic
- [ ] Cost multiplier implementation
- [ ] Target: 20% additional savings for non-urgent evaluations
[ ] Advanced budget monitoring
- [ ] Real-time dashboard
- [ ] Multi-level alerting system
- [ ] Automatic cost optimization recommendations
- [ ] Target: <5% budget overruns
Success Metrics
yaml
optimization_targets:
cost_reduction: "30-50% reduction in evaluation costs"
quality_maintenance: "No degradation in evaluation quality scores"
cache_hit_rate: ">60% cache hit rate within 2 weeks"
budget_compliance: ">95% adherence to daily budget limits"
latency_improvement: "40-60% reduction in average evaluation time"
automated_optimization: ">80% of optimizations applied automatically"Document Status: Alpha | Implementation Priority: High | Expected ROI: 3:1 within 6 months
Next Steps: Begin Phase 1 implementation with caching system and basic budget controls.