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Overview

LiteLLM automatically calculates and tracks costs for all supported LLM providers. Track spending across models, users, teams, and API keys to manage budgets and optimize usage.

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Automatic Cost Calculation

Costs are calculated automatically for every request: 
from litellm import completion

response = completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Explain quantum computing"}]
)

# Cost information in response
print(f"Prompt tokens: {response.usage.prompt_tokens}")
print(f"Completion tokens: {response.usage.completion_tokens}")
print(f"Total cost: ${response._hidden_params['response_cost']}")

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Supported Cost Metrics

LiteLLM tracks costs for:
  • Completion/Chat - Input and output tokens
  • Embeddings - Per token or per request
  • Image Generation - Per image, resolution, quality
  • Audio (Speech) - Per character or per second
  • Audio (Transcription) - Per second
  • Fine-tuning - Training tokens
  • Realtime API - Session duration, audio input/output

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Provider Support

Cost tracking for 100+ providers:
  • OpenAI (GPT-4, GPT-3.5, etc.)
  • Anthropic (Claude)
  • Google (Gemini, Vertex AI)
  • Azure OpenAI
  • AWS Bedrock
  • Cohere
  • Replicate
  • Together AI
  • And many more…

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Accessing Cost Information

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Response Object

response = completion(
    model="gpt-4-turbo",
    messages=[{"role": "user", "content": "Hello!"}]
)

# Standard usage information
usage = response.usage
print(f"Prompt tokens: {usage.prompt_tokens}")
print(f"Completion tokens: {usage.completion_tokens}")
print(f"Total tokens: {usage.total_tokens}")

# Cost information
cost = response._hidden_params.get("response_cost", 0)
print(f"Request cost: ${cost:.6f}")

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Streaming Responses

For streaming, cost is available in the final chunk: 
response = completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Write a story"}],
    stream=True
)

total_cost = 0
for chunk in response:
    if hasattr(chunk, '_hidden_params'):
        total_cost = chunk._hidden_params.get('response_cost', 0)

print(f"Total streaming cost: ${total_cost:.6f}")

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Custom Pricing

Override default pricing for custom deployments: 
from litellm import completion

response = completion(
    model="custom-model",
    messages=[{"role": "user", "content": "Hello"}],
    input_cost_per_token=0.00001,  # $0.00001 per input token
    output_cost_per_token=0.00003  # $0.00003 per output token
)

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Custom Pricing with Router

from litellm import Router

router = Router(
    model_list=[
        {
            "model_name": "custom-gpt",
            "litellm_params": {
                "model": "openai/custom-deployment",
                "api_key": "sk-...",
                "input_cost_per_token": 0.00002,
                "output_cost_per_token": 0.00004
            }
        }
    ]
)

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Budget Management

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Set Budget Limits

Prevent overspending with budget limits: 
import litellm
from litellm import BudgetManager

# Initialize budget manager
budget = BudgetManager(
    project_name="my-project",
    max_budget=100.00,  # $100 limit
    budget_duration="monthly"  # monthly, daily, or total
)

litellm.budget_manager = budget

# Requests will fail if budget is exceeded
response = completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}]
)

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User-Level Budgets

Set budgets per user or API key: 
from litellm import Router

router = Router(
    model_list=[...],
    provider_budget_config={
        "openai": {"budget_limit": 100.0},  # $100/day for OpenAI
        "anthropic": {"budget_limit": 50.0},  # $50/day for Anthropic
        "google": {"budget_limit": 75.0}  # $75/day for Google
    }
)

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Cost Logging

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Custom Cost Logger

from litellm.integrations import CustomLogger
import litellm

class CostLogger(CustomLogger):
    def __init__(self):
        self.total_cost = 0
        super().__init__()
    
    def log_success_event(self, kwargs, response_obj, start_time, end_time):
        cost = kwargs.get("response_cost", 0)
        self.total_cost += cost
        
        print(f"Request cost: ${cost:.6f}")
        print(f"Total cost: ${self.total_cost:.6f}")
        print(f"Model: {kwargs.get('model')}")
        print(f"Tokens: {response_obj.usage.total_tokens}")

cost_logger = CostLogger()
litellm.callbacks = [cost_logger]

# Make requests
response = completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}]
)

print(f"Total accumulated cost: ${cost_logger.total_cost:.6f}")

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Database Cost Tracking

from litellm.integrations import CustomLogger
import sqlite3
from datetime import datetime

class DatabaseCostLogger(CustomLogger):
    def __init__(self, db_path="costs.db"):
        self.conn = sqlite3.connect(db_path)
        self.create_table()
        super().__init__()
    
    def create_table(self):
        self.conn.execute("""
            CREATE TABLE IF NOT EXISTS costs (
                id INTEGER PRIMARY KEY,
                timestamp TEXT,
                model TEXT,
                prompt_tokens INTEGER,
                completion_tokens INTEGER,
                total_tokens INTEGER,
                cost REAL,
                user_id TEXT
            )
        """)
        self.conn.commit()
    
    def log_success_event(self, kwargs, response_obj, start_time, end_time):
        usage = response_obj.usage
        cost = kwargs.get("response_cost", 0)
        user_id = kwargs.get("metadata", {}).get("user_id")
        
        self.conn.execute("""
            INSERT INTO costs (timestamp, model, prompt_tokens, 
                             completion_tokens, total_tokens, cost, user_id)
            VALUES (?, ?, ?, ?, ?, ?, ?)
        """, (
            datetime.now().isoformat(),
            kwargs.get("model"),
            usage.prompt_tokens,
            usage.completion_tokens,
            usage.total_tokens,
            cost,
            user_id
        ))
        self.conn.commit()

litellm.callbacks = [DatabaseCostLogger()]

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Cost Analytics

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Query Costs by Model

import sqlite3

conn = sqlite3.connect("costs.db")

# Total cost by model
result = conn.execute("""
    SELECT model, SUM(cost) as total_cost, COUNT(*) as request_count
    FROM costs
    GROUP BY model
    ORDER BY total_cost DESC
""").fetchall()

for model, cost, count in result:
    print(f"{model}: ${cost:.2f} ({count} requests)")

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Query Costs by User

# Total cost by user
result = conn.execute("""
    SELECT user_id, SUM(cost) as total_cost
    FROM costs
    WHERE user_id IS NOT NULL
    GROUP BY user_id
    ORDER BY total_cost DESC
    LIMIT 10
""").fetchall()

for user_id, cost in result:
    print(f"User {user_id}: ${cost:.2f}")

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Time-Based Analysis

# Daily costs
result = conn.execute("""
    SELECT DATE(timestamp) as date, SUM(cost) as daily_cost
    FROM costs
    GROUP BY DATE(timestamp)
    ORDER BY date DESC
    LIMIT 30
""").fetchall()

for date, cost in result:
    print(f"{date}: ${cost:.2f}")

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Cost Optimization

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Model Cost Comparison

from litellm import model_cost

# Get costs for different models
models = ["gpt-4", "gpt-3.5-turbo", "claude-3-opus", "claude-3-sonnet"]

for model in models:
    try:
        cost_info = model_cost.get(model)
        if cost_info:
            input_cost = cost_info.get("input_cost_per_token", 0)
            output_cost = cost_info.get("output_cost_per_token", 0)
            print(f"{model}:")
            print(f"  Input: ${input_cost * 1000000:.2f}/1M tokens")
            print(f"  Output: ${output_cost * 1000000:.2f}/1M tokens")
    except:
        print(f"{model}: Cost info not available")

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Prompt Optimization

Reduce costs by optimizing prompts: 
from litellm import token_counter

prompt = "Your long prompt here..."

# Count tokens before sending
token_count = token_counter(
    model="gpt-4",
    messages=[{"role": "user", "content": prompt}]
)

print(f"Prompt tokens: {token_count}")

# Estimate cost
input_cost_per_token = 0.00003  # GPT-4
estimated_cost = token_count * input_cost_per_token
print(f"Estimated input cost: ${estimated_cost:.6f}")

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Choose Cost-Effective Models

from litellm import Router

router = Router(
    model_list=[
        # Primary: Cheap model
        {
            "model_name": "smart",
            "litellm_params": {"model": "gpt-3.5-turbo"}
        },
        # Fallback: Expensive model
        {
            "model_name": "smart",
            "litellm_params": {"model": "gpt-4"}
        }
    ],
    routing_strategy="cost-based-routing"  # Prefer cheaper models
)

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Cost Alerting

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Threshold-Based Alerts

from litellm.integrations import CustomLogger
import litellm

class CostAlertLogger(CustomLogger):
    def __init__(self, daily_threshold=10.0):
        self.daily_threshold = daily_threshold
        self.daily_cost = 0
        self.alert_sent = False
        super().__init__()
    
    def log_success_event(self, kwargs, response_obj, start_time, end_time):
        cost = kwargs.get("response_cost", 0)
        self.daily_cost += cost
        
        if self.daily_cost > self.daily_threshold and not self.alert_sent:
            self.send_alert(self.daily_cost)
            self.alert_sent = True
    
    def send_alert(self, cost):
        print(f"⚠️ ALERT: Daily cost ${cost:.2f} exceeds threshold ${self.daily_threshold:.2f}")
        # Send email, Slack notification, etc.

litellm.callbacks = [CostAlertLogger(daily_threshold=10.0)]

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Integration with Observability Platforms

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Langfuse Integration

import litellm

litellm.success_callback = ["langfuse"]
litellm.failure_callback = ["langfuse"]

# Set Langfuse credentials
import os
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-..."
os.environ["LANGFUSE_SECRET_KEY"] = "sk-..."
os.environ["LANGFUSE_HOST"] = "https://cloud.langfuse.com"

# Costs automatically tracked in Langfuse
response = completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}]
)

​
DataDog Integration

import litellm

litellm.success_callback = ["datadog"]

# Set DataDog credentials
import os
os.environ["DD_API_KEY"] = "..."
os.environ["DD_SITE"] = "datadoghq.com"

# Costs sent as metrics to DataDog
response = completion(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello"}]
)

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Best Practices

Cost Management Tips

  1. Monitor daily - Track costs in real-time
  2. Set budgets - Use budget limits to prevent overruns
  3. Optimize prompts - Reduce token usage
  4. Cache responses - Avoid redundant API calls
  5. Use cheaper models - Balance cost vs. quality
  6. Track by user - Identify high-cost users
  7. Alert on thresholds - Get notified of unusual spending
  8. Analyze trends - Review cost patterns weekly

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Cost Calculation Details

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Token-Based Pricing

Most models charge per token: 
# Example calculation for GPT-4
input_tokens = 100
output_tokens = 50

input_cost = input_tokens * 0.00003  # $0.03/1K tokens
output_cost = output_tokens * 0.00006  # $0.06/1K tokens
total_cost = input_cost + output_cost

print(f"Total cost: ${total_cost:.6f}")  # $0.006000

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Image Generation Pricing

from litellm import image_generation

response = image_generation(
    model="dall-e-3",
    prompt="A sunset over mountains",
    size="1024x1024",
    quality="hd",
    n=1
)

# Cost based on size, quality, and number of images
cost = response._hidden_params.get("response_cost")
print(f"Image generation cost: ${cost}")

​
Audio Pricing

# Speech (TTS)
from litellm import speech

response = speech(
    model="tts-1",
    input="Hello, world!",
    voice="alloy"
)

# Cost based on character count
cost = response._hidden_params.get("response_cost")

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