Pricing Models for Formation AI Solutions
This guide provides comprehensive strategies for pricing your AI agents and models on the Formation network. Choose the right pricing model to maximize revenue while providing value to your users.
Pricing Model Overview
Formation supports multiple pricing strategies that can be combined for optimal revenue generation:
Supported Pricing Models
| Model | Best For | Revenue Predictability | Implementation Complexity |
|---|---|---|---|
| Token-Based | Language models, text processing | Medium | Low |
| Request-Based | Image generation, analysis tasks | High | Low |
| Time-Based | Long-running tasks, streaming | Low | Medium |
| Subscription | Regular users, enterprise | High | High |
| Hybrid | Complex services | Medium | High |
1. Token-Based Pricing
Best for: Language models, text processing agents, chat completions
Token-based pricing charges users based on the number of input and output tokens processed. This is the most common pricing model for language models.
1.1 Basic Token Pricing Configuration
Agent Metadata Configuration
{ "agent_id": "my-language-agent", "metadata": { "cost_per_1k_input_tokens": "1.0", "cost_per_1k_output_tokens": "1.5", "pricing_model": "token_based", "currency": "credits" } }
Model Registration with Token Pricing
{ "model_id": "my-llm-model", "price_per_1m_tokens": 1000, "usage_tracking": { "track_tokens": true, "track_requests": true, "enable_royalties": true, "royalty_percentage": 10 } }
1.2 Implementation Example
Python Implementation
class TokenBasedPricing: def __init__(self, input_rate=0.001, output_rate=0.002): """ Initialize token-based pricing Args: input_rate: Cost per input token in USD output_rate: Cost per output token in USD """ self.input_rate = input_rate self.output_rate = output_rate def calculate_cost(self, prompt_tokens, completion_tokens): """Calculate total cost for token usage""" input_cost = prompt_tokens * self.input_rate output_cost = completion_tokens * self.output_rate return input_cost + output_cost def calculate_credits(self, prompt_tokens, completion_tokens): """Calculate cost in Formation credits (1 credit = $0.01)""" usd_cost = self.calculate_cost(prompt_tokens, completion_tokens) return int(usd_cost * 100) # Convert to credits def get_usage_metrics(self, request_text, response_text): """Generate usage metrics for billing""" # Simple tokenization (use proper tokenizer in production) prompt_tokens = len(request_text.split()) completion_tokens = len(response_text.split()) return { "prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens, "total_tokens": prompt_tokens + completion_tokens, "input_cost_usd": prompt_tokens * self.input_rate, "output_cost_usd": completion_tokens * self.output_rate, "total_cost_usd": self.calculate_cost(prompt_tokens, completion_tokens), "total_cost_credits": self.calculate_credits(prompt_tokens, completion_tokens) } # Usage in your agent pricing = TokenBasedPricing(input_rate=0.001, output_rate=0.002) @app.route('/run_task', methods=['POST']) def run_task(): data = request.json prompt = data["parameters"]["prompt"] # Process the task response = generate_response(prompt) # Calculate usage and cost usage_metrics = pricing.get_usage_metrics(prompt, response) return jsonify({ "task_id": data["task_id"], "status": "completed", "result": {"output": response}, "usage_metrics": usage_metrics })
Rust Implementation (Formation Native)
use serde::{Deserialize, Serialize}; #[derive(Debug, Clone, Serialize, Deserialize)] pub struct TokenPricingConfig { pub input_rate_per_1k: f64, pub output_rate_per_1k: f64, pub minimum_charge: u64, } impl TokenPricingConfig { pub fn new(input_rate: f64, output_rate: f64) -> Self { Self { input_rate_per_1k: input_rate, output_rate_per_1k: output_rate, minimum_charge: 1, // Minimum 1 credit } } pub fn calculate_credits(&self, input_tokens: u64, output_tokens: u64) -> u64 { let input_cost = (input_tokens as f64 / 1000.0) * self.input_rate_per_1k; let output_cost = (output_tokens as f64 / 1000.0) * self.output_rate_per_1k; let total_cost = (input_cost + output_cost).ceil() as u64; std::cmp::max(total_cost, self.minimum_charge) } } // Usage in agent metadata let pricing = TokenPricingConfig::new(1.0, 1.5); let cost = pricing.calculate_credits(150, 100); // 150 input, 100 output tokens
1.3 Advanced Token Pricing Strategies
Tiered Token Pricing
class TieredTokenPricing: def __init__(self): self.tiers = { "basic": {"input": 0.0005, "output": 0.001}, "standard": {"input": 0.001, "output": 0.002}, "premium": {"input": 0.002, "output": 0.004}, "enterprise": {"input": 0.003, "output": 0.006} } def calculate_cost(self, prompt_tokens, completion_tokens, tier="standard"): rates = self.tiers[tier] input_cost = prompt_tokens * rates["input"] output_cost = completion_tokens * rates["output"] return input_cost + output_cost def get_tier_for_user(self, user_subscription): """Determine pricing tier based on user subscription""" if user_subscription == "enterprise": return "enterprise" elif user_subscription in ["pro", "pro_plus"]: return "premium" elif user_subscription == "basic": return "standard" else: return "basic"
Volume-Based Discounts
class VolumeDiscountPricing: def __init__(self, base_input_rate=0.001, base_output_rate=0.002): self.base_input_rate = base_input_rate self.base_output_rate = base_output_rate self.volume_discounts = { 100000: 0.05, # 5% discount for 100K+ tokens 500000: 0.10, # 10% discount for 500K+ tokens 1000000: 0.15, # 15% discount for 1M+ tokens 5000000: 0.20, # 20% discount for 5M+ tokens } def get_discount_rate(self, monthly_token_usage): """Calculate discount based on monthly usage""" for threshold, discount in sorted(self.volume_discounts.items(), reverse=True): if monthly_token_usage >= threshold: return discount return 0.0 def calculate_cost(self, prompt_tokens, completion_tokens, monthly_usage=0): discount = self.get_discount_rate(monthly_usage) input_rate = self.base_input_rate * (1 - discount) output_rate = self.base_output_rate * (1 - discount) input_cost = prompt_tokens * input_rate output_cost = completion_tokens * output_rate return { "input_cost": input_cost, "output_cost": output_cost, "total_cost": input_cost + output_cost, "discount_applied": discount, "savings": (prompt_tokens * self.base_input_rate + completion_tokens * self.base_output_rate) - (input_cost + output_cost) }
2. Request-Based Pricing
Best for: Image generation, document analysis, fixed-complexity tasks
Request-based pricing charges a fixed amount per request, often with complexity tiers.
2.1 Basic Request Pricing Configuration
Agent Metadata Configuration
{ "agent_id": "image-generator", "metadata": { "cost_per_call": "50", "pricing_model": "per_request", "complexity_tiers": "simple:25,standard:50,complex:100", "currency": "credits" } }
2.2 Implementation Example
class RequestBasedPricing: def __init__(self, base_rate=0.50): self.base_rate = base_rate self.complexity_tiers = { "simple": 1.0, "standard": 1.5, "complex": 2.0, "premium": 3.0 } def assess_complexity(self, request_data): """Assess request complexity based on parameters""" # Example complexity assessment if "high_resolution" in request_data.get("parameters", {}): return "complex" elif "custom_style" in request_data.get("parameters", {}): return "standard" else: return "simple" def calculate_cost(self, request_complexity="standard"): multiplier = self.complexity_tiers.get(request_complexity, 1.0) return self.base_rate * multiplier def get_usage_metrics(self, request_data): complexity = self.assess_complexity(request_data) cost = self.calculate_cost(complexity) return { "request_complexity": complexity, "base_rate": self.base_rate, "complexity_multiplier": self.complexity_tiers[complexity], "total_cost_usd": cost, "total_cost_credits": int(cost * 100) } # Usage in your agent pricing = RequestBasedPricing(base_rate=0.25) @app.route('/run_task', methods=['POST']) def run_task(): data = request.json # Process the task result = process_image_generation(data["parameters"]) # Calculate usage and cost usage_metrics = pricing.get_usage_metrics(data) return jsonify({ "task_id": data["task_id"], "status": "completed", "result": result, "usage_metrics": usage_metrics })
2.3 Advanced Request Pricing
Quality-Based Pricing
class QualityBasedPricing: def __init__(self): self.quality_tiers = { "draft": {"rate": 0.10, "sla": "best_effort"}, "standard": {"rate": 0.25, "sla": "99%_uptime"}, "premium": {"rate": 0.50, "sla": "99.9%_uptime"}, "enterprise": {"rate": 1.00, "sla": "99.99%_uptime"} } def calculate_cost(self, quality_tier="standard", additional_features=None): base_cost = self.quality_tiers[quality_tier]["rate"] # Add cost for additional features feature_costs = { "priority_processing": 0.10, "custom_model": 0.25, "dedicated_resources": 0.50 } additional_cost = 0 if additional_features: for feature in additional_features: additional_cost += feature_costs.get(feature, 0) return base_cost + additional_cost
3. Time-Based Pricing
Best for: Long-running tasks, streaming services, real-time processing
Time-based pricing charges based on the duration of service usage.
3.1 Basic Time Pricing Configuration
Agent Metadata Configuration
{ "agent_id": "streaming-processor", "metadata": { "cost_per_minute": "10", "pricing_model": "time_based", "minimum_duration": "1", "currency": "credits" } }
3.2 Implementation Example
class TimeBasedPricing: def __init__(self, rate_per_minute=0.10, minimum_duration=60): self.rate_per_minute = rate_per_minute self.minimum_duration = minimum_duration # seconds def calculate_cost(self, duration_seconds): # Apply minimum duration billable_duration = max(duration_seconds, self.minimum_duration) duration_minutes = billable_duration / 60.0 return duration_minutes * self.rate_per_minute def get_usage_metrics(self, start_time, end_time): duration_seconds = end_time - start_time cost = self.calculate_cost(duration_seconds) return { "duration_seconds": duration_seconds, "billable_duration_seconds": max(duration_seconds, self.minimum_duration), "duration_minutes": duration_seconds / 60.0, "rate_per_minute": self.rate_per_minute, "total_cost_usd": cost, "total_cost_credits": int(cost * 100) } # Usage in your agent pricing = TimeBasedPricing(rate_per_minute=0.05) @app.route('/run_task', methods=['POST']) def run_task(): data = request.json start_time = time.time() # Process the long-running task result = process_streaming_task(data["parameters"]) end_time = time.time() # Calculate usage and cost usage_metrics = pricing.get_usage_metrics(start_time, end_time) return jsonify({ "task_id": data["task_id"], "status": "completed", "result": result, "usage_metrics": usage_metrics })
3.3 Advanced Time Pricing
Peak Hours Pricing
class PeakHoursPricing: def __init__(self, base_rate=0.10): self.base_rate = base_rate self.peak_multipliers = { "off_peak": 0.8, # 20% discount (midnight-6am) "normal": 1.0, # Standard rate (6am-6pm) "peak": 1.5, # 50% premium (6pm-midnight) "weekend": 0.9 # 10% discount (weekends) } def get_time_tier(self, timestamp): """Determine pricing tier based on time""" import datetime dt = datetime.datetime.fromtimestamp(timestamp) # Weekend discount if dt.weekday() >= 5: # Saturday = 5, Sunday = 6 return "weekend" hour = dt.hour if 0 <= hour < 6: return "off_peak" elif 18 <= hour < 24: return "peak" else: return "normal" def calculate_cost(self, duration_seconds, start_timestamp): time_tier = self.get_time_tier(start_timestamp) multiplier = self.peak_multipliers[time_tier] duration_minutes = duration_seconds / 60.0 base_cost = duration_minutes * self.base_rate return { "base_cost": base_cost, "time_tier": time_tier, "multiplier": multiplier, "total_cost": base_cost * multiplier }
4. Subscription Pricing
Best for: Regular users, enterprise clients, predictable usage patterns
Subscription pricing provides users with included usage quotas and overage billing.
4.1 Formation Subscription Tiers
Formation provides built-in subscription tiers that you can leverage:
// Formation's built-in subscription tiers pub enum SubscriptionTier { Free, // 1 agent, 100 credits, 100K tokens/day Pro, // 3 agents, 500 credits, 500K tokens/day ProPlus, // 5 agents, 1000 credits, unlimited tokens Power, // 10 agents, 5000 credits, unlimited tokens PowerPlus, // 25 agents, 10000 credits, unlimited tokens }
4.2 Subscription-Aware Pricing
class SubscriptionAwarePricing: def __init__(self): self.subscription_benefits = { "free": { "included_tokens": 100000, "overage_rate": 0.002, "discount": 0.0 }, "pro": { "included_tokens": 500000, "overage_rate": 0.0015, "discount": 0.10 }, "pro_plus": { "included_tokens": 1000000, "overage_rate": 0.001, "discount": 0.15 }, "power": { "included_tokens": 5000000, "overage_rate": 0.0008, "discount": 0.20 }, "power_plus": { "included_tokens": 10000000, "overage_rate": 0.0005, "discount": 0.25 } } def calculate_cost(self, tokens_used, subscription_tier, monthly_usage=0): benefits = self.subscription_benefits.get(subscription_tier, self.subscription_benefits["free"]) # Check if within included quota if monthly_usage + tokens_used <= benefits["included_tokens"]: return { "cost": 0.0, "reason": "within_quota", "remaining_quota": benefits["included_tokens"] - monthly_usage - tokens_used } # Calculate overage overage_tokens = (monthly_usage + tokens_used) - benefits["included_tokens"] overage_cost = overage_tokens * benefits["overage_rate"] return { "cost": overage_cost, "reason": "overage", "overage_tokens": overage_tokens, "overage_rate": benefits["overage_rate"] } def get_pricing_for_tier(self, subscription_tier): """Get pricing information for a subscription tier""" return self.subscription_benefits.get(subscription_tier, self.subscription_benefits["free"])
4.3 Custom Subscription Tiers
class CustomSubscriptionPricing: def __init__(self): self.custom_tiers = { "startup": { "monthly_cost": 49.99, "included_requests": 1000, "overage_rate": 0.05, "features": ["basic_support", "api_access"] }, "business": { "monthly_cost": 199.99, "included_requests": 5000, "overage_rate": 0.03, "features": ["priority_support", "api_access", "custom_models"] }, "enterprise": { "monthly_cost": 999.99, "included_requests": 25000, "overage_rate": 0.02, "features": ["dedicated_support", "api_access", "custom_models", "sla_guarantee"] } } def calculate_monthly_cost(self, tier, requests_used): tier_config = self.custom_tiers[tier] base_cost = tier_config["monthly_cost"] if requests_used <= tier_config["included_requests"]: return { "base_cost": base_cost, "overage_cost": 0.0, "total_cost": base_cost, "requests_remaining": tier_config["included_requests"] - requests_used } overage_requests = requests_used - tier_config["included_requests"] overage_cost = overage_requests * tier_config["overage_rate"] return { "base_cost": base_cost, "overage_cost": overage_cost, "total_cost": base_cost + overage_cost, "overage_requests": overage_requests }
5. Dynamic Pricing Strategies
5.1 Demand-Based Pricing
class DemandBasedPricing: def __init__(self, base_rate=0.001): self.base_rate = base_rate self.demand_multipliers = { "very_low": 0.7, # 30% discount "low": 0.85, # 15% discount "normal": 1.0, # Standard pricing "high": 1.25, # 25% premium "very_high": 1.5 # 50% premium } def assess_current_demand(self, queue_length, active_instances): """Assess current demand based on system metrics""" utilization = queue_length / max(active_instances, 1) if utilization < 0.2: return "very_low" elif utilization < 0.5: return "low" elif utilization < 0.8: return "normal" elif utilization < 1.2: return "high" else: return "very_high" def get_current_rate(self, queue_length=0, active_instances=1): demand_level = self.assess_current_demand(queue_length, active_instances) multiplier = self.demand_multipliers[demand_level] return { "base_rate": self.base_rate, "demand_level": demand_level, "multiplier": multiplier, "current_rate": self.base_rate * multiplier, "savings_or_premium": (multiplier - 1.0) * 100 }
5.2 Performance-Based Pricing
class PerformanceBasedPricing: def __init__(self, base_rate=0.001): self.base_rate = base_rate self.performance_tiers = { "economy": {"multiplier": 0.8, "sla": "best_effort"}, "standard": {"multiplier": 1.0, "sla": "95%_availability"}, "premium": {"multiplier": 1.3, "sla": "99%_availability"}, "guaranteed": {"multiplier": 1.6, "sla": "99.9%_availability"} } def calculate_cost(self, tokens, performance_tier="standard"): tier_config = self.performance_tiers[performance_tier] rate = self.base_rate * tier_config["multiplier"] return { "tokens": tokens, "rate_per_token": rate, "total_cost": tokens * rate, "performance_tier": performance_tier, "sla": tier_config["sla"] }
6. Hybrid Pricing Models
6.1 Combined Token + Request Pricing
class HybridPricing: def __init__(self): self.base_request_cost = 0.05 # Base cost per request self.token_rate = 0.0005 # Additional cost per token self.minimum_charge = 0.01 # Minimum charge per request def calculate_cost(self, tokens_used): request_cost = self.base_request_cost token_cost = tokens_used * self.token_rate total_cost = request_cost + token_cost return max(total_cost, self.minimum_charge) def get_usage_metrics(self, tokens_used): request_cost = self.base_request_cost token_cost = tokens_used * self.token_rate total_cost = self.calculate_cost(tokens_used) return { "tokens_used": tokens_used, "base_request_cost": request_cost, "token_cost": token_cost, "total_cost": total_cost, "cost_breakdown": { "request_portion": request_cost / total_cost, "token_portion": token_cost / total_cost } }
6.2 Freemium Model
class FreemiumPricing: def __init__(self): self.free_tier_limits = { "daily_requests": 100, "monthly_tokens": 50000, "features": ["basic_models"] } self.premium_rates = { "per_request": 0.02, "per_token": 0.001, "premium_features": ["advanced_models", "priority_support"] } def calculate_cost(self, requests_today, tokens_this_month, is_premium_user=False): if not is_premium_user: # Free tier - check limits if (requests_today <= self.free_tier_limits["daily_requests"] and tokens_this_month <= self.free_tier_limits["monthly_tokens"]): return { "cost": 0.0, "tier": "free", "remaining_requests": self.free_tier_limits["daily_requests"] - requests_today, "remaining_tokens": self.free_tier_limits["monthly_tokens"] - tokens_this_month } else: return { "cost": "upgrade_required", "tier": "free_limit_exceeded", "message": "Please upgrade to continue using the service" } else: # Premium tier - pay per use cost = (requests_today * self.premium_rates["per_request"] + tokens_this_month * self.premium_rates["per_token"]) return { "cost": cost, "tier": "premium", "features": self.premium_rates["premium_features"] }
7. Revenue Optimization Strategies
7.1 A/B Testing Pricing
class PricingABTest: def __init__(self): self.test_variants = { "control": {"rate": 0.001, "description": "Current pricing"}, "variant_a": {"rate": 0.0008, "description": "20% discount"}, "variant_b": {"rate": 0.0012, "description": "20% premium"}, "variant_c": {"rate": 0.0015, "description": "50% premium"} } self.user_assignments = {} # user_id -> variant def assign_user_to_variant(self, user_id): """Assign user to a pricing variant""" import hashlib hash_value = int(hashlib.md5(user_id.encode()).hexdigest(), 16) variant_index = hash_value % len(self.test_variants) variant_names = list(self.test_variants.keys()) assigned_variant = variant_names[variant_index] self.user_assignments[user_id] = assigned_variant return assigned_variant def get_rate_for_user(self, user_id): """Get pricing rate for a specific user""" if user_id not in self.user_assignments: self.assign_user_to_variant(user_id) variant = self.user_assignments[user_id] return self.test_variants[variant]["rate"]
7.2 Customer Lifetime Value Optimization
class CLVOptimizedPricing: def __init__(self): self.customer_segments = { "new": {"discount": 0.2, "trial_period": 30}, "regular": {"discount": 0.0, "loyalty_bonus": 0.05}, "high_value": {"discount": 0.1, "priority_support": True}, "enterprise": {"discount": 0.15, "custom_terms": True} } def classify_customer(self, usage_history, subscription_tier, total_spent): """Classify customer based on behavior""" if total_spent > 10000: return "enterprise" elif total_spent > 1000: return "high_value" elif len(usage_history) > 30: return "regular" else: return "new" def calculate_optimized_price(self, base_cost, customer_segment): """Calculate price optimized for customer lifetime value""" segment_config = self.customer_segments[customer_segment] discount = segment_config.get("discount", 0.0) optimized_price = base_cost * (1 - discount) return { "base_cost": base_cost, "customer_segment": customer_segment, "discount_applied": discount, "final_price": optimized_price, "additional_benefits": {k: v for k, v in segment_config.items() if k != "discount"} }
8. Implementation Best Practices
8.1 Pricing Configuration Management
class PricingConfigManager: def __init__(self, config_file="pricing_config.json"): self.config_file = config_file self.config = self.load_config() def load_config(self): """Load pricing configuration from file""" try: with open(self.config_file, 'r') as f: return json.load(f) except FileNotFoundError: return self.get_default_config() def get_default_config(self): """Default pricing configuration""" return { "token_pricing": { "input_rate": 0.001, "output_rate": 0.002, "minimum_charge": 0.01 }, "request_pricing": { "base_rate": 0.25, "complexity_multipliers": { "simple": 1.0, "standard": 1.5, "complex": 2.0 } }, "subscription_discounts": { "pro": 0.10, "enterprise": 0.20 }, "volume_discounts": { "100000": 0.05, "500000": 0.10, "1000000": 0.15 } } def update_pricing(self, new_config): """Update pricing configuration""" self.config.update(new_config) self.save_config() def save_config(self): """Save configuration to file""" with open(self.config_file, 'w') as f: json.dump(self.config, f, indent=2)
8.2 Revenue Analytics
class RevenueAnalytics: def __init__(self): self.metrics = { "total_revenue": 0.0, "requests_served": 0, "unique_users": set(), "revenue_by_model": {}, "revenue_by_tier": {} } def track_transaction(self, user_id, model_id, revenue, subscription_tier): """Track a revenue transaction""" self.metrics["total_revenue"] += revenue self.metrics["requests_served"] += 1 self.metrics["unique_users"].add(user_id) # Track by model if model_id not in self.metrics["revenue_by_model"]: self.metrics["revenue_by_model"][model_id] = 0 self.metrics["revenue_by_model"][model_id] += revenue # Track by subscription tier if subscription_tier not in self.metrics["revenue_by_tier"]: self.metrics["revenue_by_tier"][subscription_tier] = 0 self.metrics["revenue_by_tier"][subscription_tier] += revenue def get_analytics_report(self): """Generate analytics report""" return { "total_revenue": self.metrics["total_revenue"], "total_requests": self.metrics["requests_served"], "unique_users": len(self.metrics["unique_users"]), "average_revenue_per_request": ( self.metrics["total_revenue"] / max(self.metrics["requests_served"], 1) ), "average_revenue_per_user": ( self.metrics["total_revenue"] / max(len(self.metrics["unique_users"]), 1) ), "top_revenue_models": sorted( self.metrics["revenue_by_model"].items(), key=lambda x: x[1], reverse=True )[:5], "revenue_by_tier": self.metrics["revenue_by_tier"] }
9. Testing and Validation
9.1 Pricing Logic Testing
import unittest class TestPricingModels(unittest.TestCase): def setUp(self): self.token_pricing = TokenBasedPricing(input_rate=0.001, output_rate=0.002) self.request_pricing = RequestBasedPricing(base_rate=0.50) def test_token_pricing_calculation(self): """Test token-based pricing calculations""" cost = self.token_pricing.calculate_cost(1000, 500) # 1000 input, 500 output expected_cost = (1000 * 0.001) + (500 * 0.002) # 1.0 + 1.0 = 2.0 self.assertEqual(cost, expected_cost) def test_request_pricing_complexity(self): """Test request-based pricing with complexity tiers""" simple_cost = self.request_pricing.calculate_cost("simple") complex_cost = self.request_pricing.calculate_cost("complex") self.assertEqual(simple_cost, 0.50) # base rate * 1.0 self.assertEqual(complex_cost, 1.00) # base rate * 2.0 def test_minimum_charges(self): """Test minimum charge enforcement""" # Very small usage should still incur minimum charge cost = self.token_pricing.calculate_cost(1, 1) # 1 input, 1 output token self.assertGreaterEqual(cost, 0.01) # Should have minimum charge if __name__ == '__main__': unittest.main()
9.2 Revenue Simulation
class RevenueSimulator: def __init__(self, pricing_model): self.pricing_model = pricing_model self.simulation_results = [] def simulate_usage_patterns(self, days=30, users_per_day=100): """Simulate different usage patterns""" import random total_revenue = 0 daily_revenues = [] for day in range(days): daily_revenue = 0 for user in range(users_per_day): # Simulate random usage tokens_used = random.randint(100, 5000) cost = self.pricing_model.calculate_cost(tokens_used, tokens_used // 2) daily_revenue += cost daily_revenues.append(daily_revenue) total_revenue += daily_revenue return { "total_revenue": total_revenue, "average_daily_revenue": total_revenue / days, "daily_revenues": daily_revenues, "projected_monthly_revenue": (total_revenue / days) * 30, "projected_annual_revenue": (total_revenue / days) * 365 } def compare_pricing_strategies(self, strategies, simulation_params): """Compare multiple pricing strategies""" results = {} for strategy_name, pricing_model in strategies.items(): self.pricing_model = pricing_model results[strategy_name] = self.simulate_usage_patterns(**simulation_params) return results
10. Deployment and Monitoring
10.1 Pricing Deployment Checklist
- Pricing Model Selected: Choose appropriate model for your service type
- Rates Configured: Set competitive and profitable pricing rates
- Metadata Updated: Update agent/model metadata with pricing information
- Usage Tracking Implemented: Ensure accurate usage measurement
- Testing Completed: Validate pricing calculations with test scenarios
- Analytics Setup: Implement revenue tracking and monitoring
- Documentation Updated: Document pricing for users
- Monitoring Alerts: Set up alerts for pricing anomalies
10.2 Pricing Monitoring
class PricingMonitor: def __init__(self): self.alerts = [] self.thresholds = { "min_revenue_per_hour": 10.0, "max_cost_per_request": 5.0, "min_profit_margin": 0.20 } def check_pricing_health(self, hourly_metrics): """Monitor pricing health and generate alerts""" alerts = [] # Check minimum revenue if hourly_metrics["revenue"] < self.thresholds["min_revenue_per_hour"]: alerts.append({ "type": "low_revenue", "message": f"Hourly revenue ({hourly_metrics['revenue']}) below threshold", "severity": "warning" }) # Check cost efficiency avg_cost = hourly_metrics["total_cost"] / max(hourly_metrics["requests"], 1) if avg_cost > self.thresholds["max_cost_per_request"]: alerts.append({ "type": "high_cost", "message": f"Average cost per request ({avg_cost}) above threshold", "severity": "critical" }) # Check profit margin profit_margin = (hourly_metrics["revenue"] - hourly_metrics["total_cost"]) / max(hourly_metrics["revenue"], 1) if profit_margin < self.thresholds["min_profit_margin"]: alerts.append({ "type": "low_margin", "message": f"Profit margin ({profit_margin:.2%}) below threshold", "severity": "warning" }) return alerts
Next Steps
Choose Your Pricing Strategy
- 📊 Analyze Your Service: Understand your costs, value proposition, and target market
- 🎯 Select Primary Model: Choose the pricing model that best fits your service type
- ⚙️ Implement Pricing Logic: Add pricing calculations to your agent or model
- 🧪 Test Thoroughly: Validate pricing calculations and edge cases
- 📈 Deploy and Monitor: Launch with monitoring and be ready to optimize
Advanced Monetization
Ready for more sophisticated monetization? Continue to:
- Billing Integration: Implement advanced billing features
- Revenue Analytics: Set up comprehensive revenue tracking
- Customer Segmentation: Develop targeted pricing strategies
- Market Optimization: Use data to optimize pricing over time
Ready to maximize your revenue? Choose your pricing model and start implementing today! 💰