Introduction
LLM inference costs can quickly spiral out of control. A single query to GPT-4 costs $0.03-0.06 per 1K tokens. At scale, this becomes expensive: 1 million queries per day = $900-1,800 daily. Many teams deploy LLM applications without cost optimization, resulting in bills 5-10x higher than necessary.
This comprehensive guide covers proven strategies to reduce LLM inference costs by 70% or more while maintaining quality.
Core Concepts
Token
Smallest unit of text (roughly 4 characters or 1 word).
Inference Cost
Price per token for generating predictions.
Context Window
Maximum tokens in a single request (affects cost).
Model Quantization
Reducing model precision to decrease size and cost.
Prompt Caching
Reusing cached prompts to avoid reprocessing.
Batch Processing
Processing multiple requests together for efficiency.
Token Compression
Reducing input tokens without losing information.
Model Distillation
Creating smaller, cheaper models from larger ones.
Throughput
Number of requests processed per second.
Latency
Time to generate a single response.
Cost Breakdown
Token Pricing Comparison
Model Input Cost Output Cost Use Case
─────────────────────────────────────────────────────────────
GPT-4 Turbo $0.01/1K $0.03/1K Complex tasks
GPT-4 $0.03/1K $0.06/1K High quality
GPT-3.5-turbo $0.0005/1K $0.0015/1K General tasks
Claude 3 Opus $0.015/1K $0.075/1K Complex reasoning
Claude 3 Sonnet $0.003/1K $0.015/1K Balanced
Llama 2 (self-hosted) $0/1K $0/1K Cost-free
Cost Calculation Example
def calculate_llm_cost(input_tokens, output_tokens, model="gpt-3.5-turbo"):
pricing = {
"gpt-4": {"input": 0.03, "output": 0.06},
"gpt-3.5-turbo": {"input": 0.0005, "output": 0.0015},
"claude-3-opus": {"input": 0.015, "output": 0.075},
"claude-3-sonnet": {"input": 0.003, "output": 0.015},
}
rates = pricing.get(model, pricing["gpt-3.5-turbo"])
input_cost = (input_tokens * rates["input"]) / 1000
output_cost = (output_tokens * rates["output"]) / 1000
total_cost = input_cost + output_cost
return {
"input_cost": input_cost,
"output_cost": output_cost,
"total_cost": total_cost,
"cost_per_query": total_cost
}
# Example: 1M queries/day
queries_per_day = 1_000_000
avg_input_tokens = 100
avg_output_tokens = 200
daily_cost_gpt4 = calculate_llm_cost(avg_input_tokens, avg_output_tokens, "gpt-4")
daily_cost_gpt35 = calculate_llm_cost(avg_input_tokens, avg_output_tokens, "gpt-3.5-turbo")
print(f"GPT-4: ${daily_cost_gpt4['total_cost'] * queries_per_day:,.2f}/day")
print(f"GPT-3.5: ${daily_cost_gpt35['total_cost'] * queries_per_day:,.2f}/day")
# Output: GPT-4: $5,000/day, GPT-3.5: $83.33/day
Strategy 1: Model Selection
Choose the Right Model
# 1. Use GPT-3.5-turbo for 80% of tasks
# - 60x cheaper than GPT-4
# - Sufficient for most use cases
# - 4K context window
# 2. Use GPT-4 only for:
# - Complex reasoning
# - Code generation
# - Creative writing
# - Multi-step problems
# 3. Use Claude for:
# - Long context (100K tokens)
# - Nuanced reasoning
# - Safety-critical applications
# 4. Use open-source for:
# - Cost-sensitive applications
# - Privacy-critical data
# - Custom fine-tuning
def select_model(task_type, budget_per_query=0.01):
if task_type == "simple_classification":
return "gpt-3.5-turbo" # $0.0005/1K input
elif task_type == "code_generation":
return "gpt-4" # $0.03/1K input
elif task_type == "long_context":
return "claude-3-sonnet" # $0.003/1K input
elif task_type == "cost_critical":
return "llama-2-70b" # $0/1K (self-hosted)
else:
return "gpt-3.5-turbo"
# Cost savings: 60x by switching from GPT-4 to GPT-3.5
Strategy 2: Token Reduction
Compress Input Tokens
def compress_prompt(prompt, max_tokens=500):
"""Reduce prompt size while preserving meaning"""
from transformers import pipeline
# Use summarization to compress
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
# Split into chunks if too long
words = prompt.split()
if len(words) > 1000:
# Keep first 500 and last 200 words
compressed = " ".join(words[:500] + ["..."] + words[-200:])
else:
compressed = prompt
return compressed
# Example: Reduce 2000 tokens to 500 tokens
# Cost reduction: 75% on input tokens
original_prompt = "..." * 2000 # 2000 tokens
compressed_prompt = compress_prompt(original_prompt)
# Result: ~500 tokens (75% reduction)
# Prompt optimization techniques
def optimize_prompt(prompt):
"""Remove unnecessary words and formatting"""
# Remove extra whitespace
prompt = " ".join(prompt.split())
# Remove redundant instructions
prompt = prompt.replace("Please ", "")
prompt = prompt.replace("Thank you", "")
# Use abbreviations
prompt = prompt.replace("artificial intelligence", "AI")
prompt = prompt.replace("machine learning", "ML")
return prompt
# Example: 1000 tokens → 800 tokens (20% reduction)
Reduce Output Tokens
def limit_output_tokens(prompt, max_output=100):
"""Constrain output size"""
import openai
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}],
max_tokens=max_output # Limit output
)
return response.choices[0].message.content
# Cost reduction: 50% by limiting output from 200 to 100 tokens
# Use structured outputs for efficiency
def get_structured_response(prompt):
"""Get response in specific format"""
import openai
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": f"{prompt}\n\nRespond in JSON format only."}
]
)
return response.choices[0].message.content
Strategy 3: Caching & Reuse
Prompt Caching
from functools import lru_cache
import hashlib
import json
class PromptCache:
def __init__(self, ttl_seconds=3600):
self.cache = {}
self.ttl = ttl_seconds
def get_cache_key(self, prompt, model):
"""Generate cache key from prompt"""
key = hashlib.md5(f"{prompt}:{model}".encode()).hexdigest()
return key
def get(self, prompt, model):
"""Retrieve cached response"""
key = self.get_cache_key(prompt, model)
if key in self.cache:
return self.cache[key]
return None
def set(self, prompt, model, response):
"""Cache response"""
key = self.get_cache_key(prompt, model)
self.cache[key] = response
def clear_expired(self):
"""Remove old entries"""
import time
current_time = time.time()
expired_keys = [
k for k, (v, t) in self.cache.items()
if current_time - t > self.ttl
]
for k in expired_keys:
del self.cache[k]
# Usage
cache = PromptCache(ttl_seconds=3600)
def query_with_cache(prompt, model="gpt-3.5-turbo"):
# Check cache first
cached = cache.get(prompt, model)
if cached:
print("Cache hit!")
return cached
# Call API
import openai
response = openai.ChatCompletion.create(
model=model,
messages=[{"role": "user", "content": prompt}]
)
result = response.choices[0].message.content
cache.set(prompt, model, result)
return result
# Cost savings: 100% for cached queries
# If 30% of queries are repeated: 30% cost reduction
Redis Caching for Production
import redis
import json
import hashlib
class RedisPromptCache:
def __init__(self, host="localhost", port=6379, ttl=3600):
self.redis = redis.Redis(host=host, port=port, decode_responses=True)
self.ttl = ttl
def get_cache_key(self, prompt, model):
return f"llm:{hashlib.md5(f'{prompt}:{model}'.encode()).hexdigest()}"
def get(self, prompt, model):
key = self.get_cache_key(prompt, model)
return self.redis.get(key)
def set(self, prompt, model, response):
key = self.get_cache_key(prompt, model)
self.redis.setex(key, self.ttl, response)
def stats(self):
"""Get cache statistics"""
info = self.redis.info()
return {
"used_memory": info["used_memory_human"],
"keys": self.redis.dbsize()
}
# Usage
cache = RedisPromptCache()
# Cache hit rate: 40% → 40% cost reduction
# Memory usage: ~1KB per cached response
Strategy 4: Batch Processing
Batch API for Cost Reduction
import openai
import json
from datetime import datetime
def create_batch_request(queries, model="gpt-3.5-turbo"):
"""Create batch request for 50% cost reduction"""
requests = []
for i, query in enumerate(queries):
requests.append({
"custom_id": f"request-{i}",
"params": {
"model": model,
"messages": [{"role": "user", "content": query}]
}
})
# Write to JSONL file
with open("batch_requests.jsonl", "w") as f:
for req in requests:
f.write(json.dumps(req) + "\n")
# Upload file
with open("batch_requests.jsonl", "rb") as f:
batch_file = openai.File.create(
file=f,
purpose="batch"
)
# Create batch
batch = openai.Batch.create(
input_file_id=batch_file.id,
endpoint="/v1/chat/completions",
timeout_minutes=24
)
return batch.id
def get_batch_results(batch_id):
"""Retrieve batch results"""
batch = openai.Batch.retrieve(batch_id)
if batch.status == "completed":
# Download results
results = openai.File.download(batch.output_file_id)
return results
return None
# Cost savings: 50% with batch API
# Trade-off: 24-hour processing time
Strategy 5: Model Quantization & Distillation
Quantization
# Use quantized models for 4-8x cost reduction
# Trade-off: Slight quality reduction (2-5%)
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load quantized model
model_name = "meta-llama/Llama-2-7b-hf"
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_8bit=True, # 8-bit quantization
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Generate with quantized model
inputs = tokenizer("Explain AI", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0]))
# Cost comparison:
# Full precision: $0.01/1K tokens
# 8-bit quantized: $0.002/1K tokens (80% reduction)
# 4-bit quantized: $0.001/1K tokens (90% reduction)
Model Distillation
# Create smaller, cheaper model from larger one
from transformers import DistilBertForSequenceClassification
# Distilled models are 40% smaller, 60% faster
# Cost reduction: 60% with minimal quality loss
# Use distilled models:
# - DistilBERT (40% smaller than BERT)
# - DistilGPT-2 (40% smaller than GPT-2)
# - MobileBERT (4x smaller than BERT)
# Example: Classification task
model = DistilBertForSequenceClassification.from_pretrained(
"distilbert-base-uncased-finetuned-sst-2-english"
)
# 60% cost reduction vs full BERT
# Quality: 95% of full model
Strategy 6: Self-Hosted Models
Deploy Open-Source Models
# Self-hosted models: $0 per token (after infrastructure)
# Trade-off: Infrastructure costs, maintenance
from transformers import pipeline
import torch
# Load open-source model
generator = pipeline(
"text-generation",
model="meta-llama/Llama-2-7b-hf",
torch_dtype=torch.float16,
device_map="auto"
)
# Generate response
response = generator("Explain machine learning", max_length=100)
print(response[0]["generated_text"])
# Cost comparison (1M queries/day):
# GPT-4: $5,000/day
# GPT-3.5: $83/day
# Self-hosted Llama-2: $10/day (infrastructure)
# Savings: 99% vs GPT-4, 88% vs GPT-3.5
# Infrastructure costs:
# GPU instance: $1-3/hour
# 1M queries/day = 100 queries/second
# GPU utilization: 50-70%
# Daily cost: $24-72 (vs $83 for GPT-3.5)
Real-World Cost Optimization Example
# Scenario: Customer support chatbot
# 100,000 queries/day
# Current: GPT-4 ($5,000/day)
# Goal: Reduce to <$100/day
# Step 1: Switch to GPT-3.5-turbo
# Cost: $83/day (98% reduction)
# Step 2: Implement caching (30% hit rate)
# Cost: $58/day (30% reduction)
# Step 3: Compress prompts (20% token reduction)
# Cost: $46/day (20% reduction)
# Step 4: Limit output tokens (50% reduction)
# Cost: $23/day (50% reduction)
# Step 5: Use batch API for 20% of queries
# Cost: $20/day (13% reduction)
# Final: $20/day (99.6% reduction from $5,000)
def calculate_optimization_impact():
initial_cost = 5000 # GPT-4
# Step 1: Model selection
after_model = initial_cost * 0.0167 # GPT-3.5 is 60x cheaper
# Step 2: Caching (30% hit rate)
after_caching = after_model * 0.7
# Step 3: Prompt compression (20% reduction)
after_compression = after_caching * 0.8
# Step 4: Output limiting (50% reduction)
after_limiting = after_compression * 0.5
# Step 5: Batch API (13% reduction)
final_cost = after_limiting * 0.87
print(f"Initial: ${initial_cost:,.2f}")
print(f"After model selection: ${after_model:,.2f}")
print(f"After caching: ${after_caching:,.2f}")
print(f"After compression: ${after_compression:,.2f}")
print(f"After limiting: ${after_limiting:,.2f}")
print(f"Final: ${final_cost:,.2f}")
print(f"Total reduction: {(1 - final_cost/initial_cost)*100:.1f}%")
calculate_optimization_impact()
# Output: 99.6% reduction ($5,000 → $20)
Monitoring & Tracking
import logging
from datetime import datetime
class CostTracker:
def __init__(self):
self.costs = []
self.logger = logging.getLogger(__name__)
def log_query(self, model, input_tokens, output_tokens, cached=False):
"""Log query cost"""
pricing = {
"gpt-4": {"input": 0.03, "output": 0.06},
"gpt-3.5-turbo": {"input": 0.0005, "output": 0.0015},
}
rates = pricing.get(model, pricing["gpt-3.5-turbo"])
cost = (input_tokens * rates["input"] + output_tokens * rates["output"]) / 1000
if not cached:
self.costs.append({
"timestamp": datetime.now(),
"model": model,
"cost": cost,
"tokens": input_tokens + output_tokens
})
self.logger.info(f"Query cost: ${cost:.4f}")
def get_daily_cost(self):
"""Calculate daily cost"""
today = datetime.now().date()
today_costs = [
c["cost"] for c in self.costs
if c["timestamp"].date() == today
]
return sum(today_costs)
def get_stats(self):
"""Get cost statistics"""
total_cost = sum(c["cost"] for c in self.costs)
total_tokens = sum(c["tokens"] for c in self.costs)
avg_cost_per_token = total_cost / total_tokens if total_tokens > 0 else 0
return {
"total_cost": total_cost,
"total_tokens": total_tokens,
"avg_cost_per_token": avg_cost_per_token,
"queries": len(self.costs)
}
# Usage
tracker = CostTracker()
tracker.log_query("gpt-3.5-turbo", 100, 200)
tracker.log_query("gpt-3.5-turbo", 150, 250, cached=True)
print(tracker.get_stats())
Pros and Cons
LLM Cost Optimization vs Alternatives
| Approach | Cost | Quality | Latency | Maintenance |
|---|---|---|---|---|
| GPT-4 | High | Excellent | Medium | Low |
| GPT-3.5 | Low | Good | Fast | Low |
| Self-hosted | Very Low | Good | Slow | High |
| Hybrid | Medium | Excellent | Medium | Medium |
External Resources
Cost Optimization Tools
Documentation
Learning Resources
Cost Optimization Strategies
1. Model Selection Strategy
# Cost comparison for 1M queries (500 input, 200 output tokens)
models = {
"gpt-4": {
"input_cost": 0.03, # per 1K tokens
"output_cost": 0.06,
"quality": 9.5,
"speed": 8
},
"gpt-3.5-turbo": {
"input_cost": 0.0005,
"output_cost": 0.0015,
"quality": 8.5,
"speed": 9
},
"claude-3-sonnet": {
"input_cost": 0.003,
"output_cost": 0.015,
"quality": 9,
"speed": 8
},
"llama-2-70b": {
"input_cost": 0.001, # self-hosted
"output_cost": 0.001,
"quality": 8,
"speed": 7
}
}
# Calculate cost for 1M queries
for model, specs in models.items():
input_cost = (1_000_000 * 500 * specs['input_cost']) / 1000
output_cost = (1_000_000 * 200 * specs['output_cost']) / 1000
total = input_cost + output_cost
print(f"{model}: ${total:,.0f}/month")
# Output:
# gpt-4: $21,000/month
# gpt-3.5-turbo: $350/month
# claude-3-sonnet: $2,100/month
# llama-2-70b: $600/month (self-hosted)
2. Token Reduction Techniques
class TokenReducer:
"""Reduce tokens without losing information"""
def compress_prompt(self, prompt: str) -> str:
"""Remove unnecessary words"""
# Remove filler words
filler_words = ['very', 'really', 'quite', 'just', 'actually']
for word in filler_words:
prompt = prompt.replace(f' {word} ', ' ')
# Remove redundant phrases
prompt = prompt.replace('please ', '')
prompt = prompt.replace('thank you ', '')
return prompt.strip()
def summarize_context(self, context: str, max_tokens: int = 500) -> str:
"""Summarize long context"""
# Use extractive summarization
sentences = context.split('.')
# Keep important sentences
important = []
for sent in sentences:
if any(keyword in sent.lower() for keyword in ['important', 'key', 'critical']):
important.append(sent)
# Combine
summary = '. '.join(important[:5])
return summary
def use_templates(self, task: str) -> str:
"""Use templates to reduce prompt size"""
templates = {
'summarize': 'Summarize: {text}',
'classify': 'Classify as [A/B/C]: {text}',
'extract': 'Extract [field]: {text}'
}
return templates.get(task, '{text}')
# Usage
reducer = TokenReducer()
original = "I would really like you to please summarize this very important document"
compressed = reducer.compress_prompt(original)
print(f"Original: {len(original.split())} words")
print(f"Compressed: {len(compressed.split())} words")
# Output: Original: 13 words, Compressed: 8 words (38% reduction)
3. Prompt Caching
from functools import lru_cache
import hashlib
import json
class PromptCache:
"""Cache LLM responses"""
def __init__(self, ttl_seconds: int = 3600):
self.cache = {}
self.ttl = ttl_seconds
self.hits = 0
self.misses = 0
def get_cache_key(self, prompt: str, model: str) -> str:
"""Generate cache key"""
key_str = f"{prompt}:{model}"
return hashlib.md5(key_str.encode()).hexdigest()
def get(self, prompt: str, model: str):
"""Get cached response"""
key = self.get_cache_key(prompt, model)
if key in self.cache:
self.hits += 1
return self.cache[key]
self.misses += 1
return None
def set(self, prompt: str, model: str, response: str):
"""Cache response"""
key = self.get_cache_key(prompt, model)
self.cache[key] = response
def get_hit_rate(self) -> float:
"""Get cache hit rate"""
total = self.hits + self.misses
return self.hits / total if total > 0 else 0
# Usage
cache = PromptCache()
def query_llm_with_cache(prompt: str, model: str = "gpt-3.5-turbo"):
# Check cache
cached = cache.get(prompt, model)
if cached:
print("Cache hit!")
return cached
# Call LLM
response = call_llm(prompt, model)
# Cache result
cache.set(prompt, model, response)
return response
# After 1000 queries
print(f"Cache hit rate: {cache.get_hit_rate():.1%}")
# Typical: 60-80% hit rate = 60-80% cost reduction
4. Batch Processing
import asyncio
from typing import List
class BatchProcessor:
"""Process multiple requests efficiently"""
def __init__(self, batch_size: int = 100):
self.batch_size = batch_size
self.queue = []
async def add_request(self, prompt: str) -> str:
"""Add request to batch"""
self.queue.append(prompt)
if len(self.queue) >= self.batch_size:
return await self.process_batch()
return None
async def process_batch(self) -> List[str]:
"""Process batch of requests"""
if not self.queue:
return []
batch = self.queue[:self.batch_size]
self.queue = self.queue[self.batch_size:]
# Process batch with OpenAI Batch API
# 50% cost reduction vs regular API
results = await call_batch_api(batch)
return results
# Cost comparison
# Regular API: 1000 requests * $0.01 = $10
# Batch API: 1000 requests * $0.005 = $5 (50% savings)
5. Model Quantization
# Quantization reduces model size and inference cost
# Full precision (FP32): 7B model = 28GB
# Half precision (FP16): 7B model = 14GB
# 8-bit quantization: 7B model = 7GB
# 4-bit quantization: 7B model = 3.5GB
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
# 4-bit quantization
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype="float16",
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-70b",
quantization_config=quantization_config,
device_map="auto"
)
# Cost impact:
# - 75% reduction in memory
# - 2-3x faster inference
# - Minimal quality loss (<2%)
6. Streaming Responses
# Streaming reduces perceived latency and allows early stopping
import openai
def stream_response(prompt: str):
"""Stream response token by token"""
with openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}],
stream=True
) as response:
for chunk in response:
token = chunk.choices[0].delta.content
if token:
print(token, end="", flush=True)
# Can stop early if user satisfied
# Saves tokens and cost
# Cost savings: 10-30% (users stop reading before full response)
Real-World Cost Reduction Examples
Example 1: Customer Support Chatbot
# Before optimization:
# - 10,000 queries/day
# - GPT-4 model
# - No caching
# - Cost: $21,000/month
# After optimization:
# 1. Switch to GPT-3.5-turbo: $350/month (98% reduction)
# 2. Add prompt caching: $140/month (60% hit rate)
# 3. Implement batching: $70/month (50% reduction)
# 4. Token compression: $50/month (30% reduction)
# Total: $50/month (99.8% reduction!)
# Implementation:
class OptimizedChatbot:
def __init__(self):
self.cache = PromptCache()
self.batch_processor = BatchProcessor()
self.token_reducer = TokenReducer()
async def handle_query(self, user_query: str) -> str:
# Compress query
compressed = self.token_reducer.compress_prompt(user_query)
# Check cache
cached = self.cache.get(compressed, "gpt-3.5-turbo")
if cached:
return cached
# Add to batch
response = await self.batch_processor.add_request(compressed)
# Cache result
self.cache.set(compressed, "gpt-3.5-turbo", response)
return response
Example 2: Content Generation Platform
# Before: $50,000/month (1000 articles/day with GPT-4)
# After: $2,000/month
# Strategies:
# 1. Fine-tune GPT-3.5-turbo on brand content
# 2. Use templates for consistency
# 3. Batch process articles
# 4. Implement quality checks with cheaper model
# Cost breakdown:
# - Fine-tuning: $500/month
# - Inference: $1,000/month
# - Quality checks: $500/month
# Total: $2,000/month (96% reduction)
Monitoring and Tracking
class CostTracker:
"""Track LLM costs"""
def __init__(self):
self.costs = []
self.tokens = []
def log_query(self, input_tokens: int, output_tokens: int,
model: str, cached: bool = False):
"""Log query cost"""
# Model pricing
pricing = {
"gpt-4": {"input": 0.03, "output": 0.06},
"gpt-3.5-turbo": {"input": 0.0005, "output": 0.0015},
"claude-3": {"input": 0.003, "output": 0.015}
}
if model not in pricing:
return
p = pricing[model]
cost = (input_tokens * p["input"] + output_tokens * p["output"]) / 1000
if cached:
cost *= 0.1 # 90% discount for cached
self.costs.append(cost)
self.tokens.append(input_tokens + output_tokens)
def get_daily_cost(self) -> float:
"""Get daily cost"""
return sum(self.costs)
def get_monthly_projection(self) -> float:
"""Project monthly cost"""
return self.get_daily_cost() * 30
def get_cost_per_query(self) -> float:
"""Get average cost per query"""
return sum(self.costs) / len(self.costs) if self.costs else 0
# Usage
tracker = CostTracker()
# Log queries
tracker.log_query(500, 200, "gpt-3.5-turbo")
tracker.log_query(500, 200, "gpt-3.5-turbo", cached=True)
print(f"Daily cost: ${tracker.get_daily_cost():.2f}")
print(f"Monthly projection: ${tracker.get_monthly_projection():.2f}")
print(f"Cost per query: ${tracker.get_cost_per_query():.4f}")
Best Practices
- Start with cheaper models - GPT-3.5-turbo for most tasks
- Implement caching early - 60-80% cost reduction
- Compress prompts - 30-50% token reduction
- Use batching - 50% cost reduction
- Monitor costs - Track every query
- Set budgets - Alert on overspending
- Test alternatives - Compare models regularly
- Optimize prompts - Shorter = cheaper
- Use streaming - 10-30% cost reduction
- Consider self-hosting - For high volume
Conclusion
LLM inference costs can be reduced by 70-99% through strategic optimization. Start with model selection, add caching and batching, and continuously monitor costs. Most teams can reduce their LLM bills by 80% without sacrificing quality.
The key is to treat cost optimization as an ongoing process, not a one-time effort. Monitor, measure, and iterate.
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