How to Cut Your LLM Costs by 40%: A Practical Guide to Token Optimization

Key Takeaways

- Most teams can cut 30-50% of LLM costs without compromising quality

- Quick wins: model right-sizing (10-30% savings), prompt trimming (5-20%), caching (20-50%)

- Output tokens cost 3-5x more than input tokens—optimize output length first

- Use OpenAI Batch API for 50% discount on non-urgent workloads

- Fine-tuning smaller models can reduce costs by 50-80% for high-volume tasks

- Self-hosting becomes cost-effective at $30K+/month spend

Three months ago, your LLM bill was $500. You were fine with that—the value was obvious. Then traffic grew. You added features. You refined prompts. Now you're at $15,000 per month, and your finance team is asking hard questions about unit economics.

Sound familiar?

LLM costs have a way of spiraling faster than expected. Unlike traditional infrastructure, where scaling is relatively predictable, LLM costs depend on dozens of variables: prompt length, model choice, output verbosity, retry logic, temperature settings, and user behavior.

The good news: Most teams can cut 30-50% of their LLM costs with straightforward optimizations that don't compromise quality. This guide walks through proven techniques for token optimization, organized by implementation effort and potential savings.

By the end, you'll have a concrete action plan to reduce your bill starting this week.

The LLM Cost Problem: Why Costs Spiral So Quickly

Why do LLM costs spiral faster than traditional infrastructure?

Hidden Cost Multipliers

Most teams focus on per-request costs but miss the multipliers:

Verbose Prompts: A 2,000-token prompt vs a 500-token prompt with equivalent instructions costs 4x more—on every request.

Wrong Model Selection: Using GPT-4 for a task that GPT-4o-mini handles equally well costs 30x more per request.

Retry Logic: Aggressive retry policies can mean you're paying for the same failed request 3-5 times before succeeding or giving up.

Output Length: Output tokens typically cost 3-5x more than input tokens. A chatty model generating 1,000-word responses when 200 words suffice is hemorrhaging budget.

Lack of Caching: Repeatedly processing identical or similar queries without caching means you're paying for the same computation multiple times.

Real-World Cost Explosions

Here are actual cost explosions we've seen:

📊 Cost Explosion Pattern

Small optimization oversights → Large recurring costs → Budget overruns

Example 1: The Chatbot That Never Stopped Talking

A customer support chatbot was generating 500-800 token responses when 100-150 tokens would suffice. With 10,000 conversations per day at an average of 650 output tokens:

• Cost per response (GPT-4-turbo): 650 tokens × $0.03/1K = $0.0195
• Daily cost: $195
• Monthly cost: $5,850

After implementing max_tokens limits and prompt optimization:

• New average: 150 output tokens
• Cost per response: $0.0045
• Monthly cost: $1,350
• Savings: 77% ($4,500/month)

Example 2: The Prompt That Included Documentation

A code generation tool included full API documentation in every prompt "just in case." The documentation was 8,000 tokens, but only 200-400 tokens were relevant per request.

At 5,000 requests per day with GPT-4:

• Input tokens per request: 8,500 (8K docs + 500 actual prompt)
• Cost: 8,500 × $0.01/1K = $0.085 per request
• Monthly cost: $12,750

After implementing semantic search to include only relevant docs:

• New average input: 900 tokens
• Cost per request: $0.009
• Monthly cost: $1,350
• Savings: 89% ($11,400/month)

Example 3: The Retry Loop of Doom

An agent workflow had a parsing error in 15% of requests. The retry logic would attempt up to 5 times with exponential backoff—but never fixed the underlying prompt issue.

With 50,000 requests/day:

• Successful requests: 42,500 (one attempt each)
• Failed requests: 7,500 (average 3 attempts before failing)
• Total API calls: 42,500 + (7,500 × 3) = 65,000
• Paying for 30% more requests than necessary

After fixing the prompt to reduce parsing errors to 2%:

• Failed requests: 1,000
• Total API calls: 49,000
• Savings: 25% reduction in API calls

Understanding Your Cost Drivers

Before optimizing, you need to understand where money goes.

Token Math Basics

All major providers charge per token. Rough approximation: 1 token ≈ 0.75 words.

Pricing Examples (as of January 2026):

💰 Critical Insight

Output tokens cost 3-5x more than input tokens. Reducing output length by 50% has more impact than reducing input length by 50%.

Example: A 1,000-token prompt + 800-token response on GPT-4-turbo costs:

- Input: $0.01, Output: $0.024 = $0.034 total

- Reducing response to 400 tokens saves $0.012 (35% reduction)

- Reducing prompt to 500 tokens saves $0.005 (15% reduction)

The 80/20 Rule

Typically, 20% of your endpoints drive 80% of your costs. Your first step: identify those endpoints.

How to Analyze:

import pandas as pd

# Load your LLM request logs
df = pd.read_json('llm_logs.jsonl', lines=True)

# Calculate cost per request
df['cost'] = (df['input_tokens'] * 0.01 / 1000) + (df['output_tokens'] * 0.03 / 1000)

# Aggregate by endpoint
endpoint_costs = df.groupby('endpoint').agg({
    'cost': 'sum',
    'request_id': 'count'
}).rename(columns={'request_id': 'requests'})

endpoint_costs['cost_per_request'] = endpoint_costs['cost'] / endpoint_costs['requests']
endpoint_costs = endpoint_costs.sort_values('cost', ascending=False)

print(endpoint_costs.head(10))

This tells you:

• Which endpoints are most expensive in total
• Which have the highest cost per request
• Where optimization will have the biggest impact

Quick Wins (Implement This Week)

These changes require minimal code and deliver immediate savings.

Quick Win #1: Right-Size Your Models (10-30% Savings)

The Problem:

Using GPT-4 for everything is like using a Ferrari for grocery shopping. It works, but it's expensive and unnecessary.

The Solution:

Create a model routing strategy based on task complexity:

• Complex reasoning, code generation, nuanced writing: GPT-4 or Claude 3.5 Sonnet
• Simple classification, straightforward Q&A, summarization: GPT-4o-mini or Claude 3 Haiku
• Structured data extraction, formatting: GPT-4o-mini or even GPT-3.5-turbo

Savings Potential: 10-30% with no quality loss on simple tasks

Implementation Example:

def select_model(task_type, complexity_score=None):
    """Route to appropriate model based on task requirements"""

    if task_type in ['code_generation', 'complex_reasoning', 'creative_writing']:
        return 'gpt-4-turbo'

    if task_type in ['classification', 'simple_qa', 'summarization']:
        return 'gpt-4o-mini'

    # For tasks with variable complexity, use a heuristic
    if complexity_score is not None:
        if complexity_score > 7:
            return 'gpt-4-turbo'
        elif complexity_score > 4:
            return 'gpt-4o'
        else:
            return 'gpt-4o-mini'

    # Default to mid-tier
    return 'gpt-4o'

# Example usage
model = select_model('classification')
response = openai.ChatCompletion.create(
    model=model,
    messages=[{"role": "user", "content": prompt}]
)

Quick Test:

Pick your 3 highest-volume endpoints. For one week, run 10% of traffic through GPT-4o-mini instead of GPT-4. Compare quality metrics. If no degradation, switch fully.

Quick Win #2: Trim Your Prompts (5-20% Savings)

The Problem:

Prompts accumulate cruft. You add instructions, examples, and clarifications over time. What starts as 200 tokens becomes 1,500 tokens—but 70% is redundant.

The Solution:

Audit your prompts for:

• Redundant instructions: "Please", "I want you to", "Make sure to" add no value
• Verbose examples: Few-shot examples can be shortened
• Unnecessary context: Include only what the model needs

Savings Potential: 5-20% on input tokens

Before (1,247 tokens):

You are a helpful customer service assistant for Acme Corp. I want you to
please help users with their questions about our products. When answering,
make sure to be polite and professional. Always try to provide accurate
information based on the context below. If you don't know the answer, please
say so rather than making something up.

Here's some context about our company:
[8 paragraphs of company history and values - 600 tokens]

Here are our products:
[Full product catalog - 400 tokens]

User question: {question}

Please provide a helpful and accurate answer.

After (287 tokens):

You're a customer service agent for Acme Corp. Answer user questions accurately
using the context below. If unsure, say you don't know.

Products:
[Relevant products only - 150 tokens]

User: {question}

Action Plan:

1. Export your most-used prompts
2. Remove filler words ("please", "make sure", "I want you to")
3. Condense examples to minimum necessary
4. Include context dynamically (only what's relevant per request)
5. Test quality with 100 sample requests

Quick Win #3: Implement Caching (20-50% Savings)

The Problem:

Users often ask the same or very similar questions. You're paying to process "What are your hours?" 50 times a day.

The Solution:

Implement two types of caching:

Exact-Match Caching:

For deterministic queries, cache by prompt hash.

import hashlib
import json

cache = {}

def get_completion_with_cache(prompt, model='gpt-4o-mini', temperature=0):
    # Create cache key from prompt + model + temperature
    cache_key = hashlib.sha256(
        f"{prompt}:{model}:{temperature}".encode()
    ).hexdigest()

    # Check cache
    if cache_key in cache:
        print("Cache hit!")
        return cache[cache_key]

    # Cache miss - call API
    response = openai.ChatCompletion.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        temperature=temperature
    )

    result = response.choices[0].message.content

    # Store in cache
    cache[cache_key] = result

    return result

Semantic Caching:

For similar (not identical) queries, use embedding similarity.

import openai
import numpy as np

semantic_cache = []

def get_embedding(text):
    response = openai.Embedding.create(
        input=text,
        model="text-embedding-3-small"
    )
    return np.array(response.data[0].embedding)

def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

def get_completion_with_semantic_cache(prompt, model='gpt-4o-mini', similarity_threshold=0.95):
    prompt_embedding = get_embedding(prompt)

    # Check for similar cached queries
    for cached_prompt, cached_embedding, cached_response in semantic_cache:
        similarity = cosine_similarity(prompt_embedding, cached_embedding)
        if similarity >= similarity_threshold:
            print(f"Semantic cache hit! Similarity: {similarity:.2f}")
            return cached_response

    # Cache miss - call API
    response = openai.ChatCompletion.create(
        model=model,
        messages=[{"role": "user", "content": prompt}]
    )

    result = response.choices[0].message.content

    # Store in cache
    semantic_cache.append((prompt, prompt_embedding, result))

    return result

Savings Potential: 20-50% depending on query repetition

Note: Semantic caching works best for FAQ-style applications. For unique, context-dependent queries, exact-match caching with lower temperature is more reliable.

Medium-Term Optimizations (This Month)

These require more implementation effort but deliver substantial savings.

Optimization 1: Prompt Compression

The Problem:

Sometimes you need long context (documentation, chat history, retrieved documents), but every token costs money.

The Solution:

Use prompt compression techniques to reduce token count while preserving semantic meaning.

Technique A: LLMLingua-Style Compression

Remove low-information tokens while keeping key concepts:

def compress_prompt(text, compression_ratio=0.5):
    """
    Simple compression: remove common words, keep nouns/verbs.
    Production use: Use LLMLingua library for better results.
    """
    import nltk
    from nltk.corpus import stopwords
    from nltk.tokenize import word_tokenize

    tokens = word_tokenize(text)
    stop_words = set(stopwords.words('english'))

    # Keep important words
    important_tokens = [
        token for token in tokens
        if token.lower() not in stop_words or token in ['not', 'no']
    ]

    # If still too long, keep first/last portions
    target_length = int(len(tokens) * compression_ratio)
    if len(important_tokens) > target_length:
        # Keep first 60% and last 40% (adjustable)
        first_part = important_tokens[:int(target_length * 0.6)]
        last_part = important_tokens[-int(target_length * 0.4):]
        important_tokens = first_part + ['...'] + last_part

    return ' '.join(important_tokens)

# Example usage
original = "The quick brown fox jumps over the lazy dog in the park on a sunny day"
compressed = compress_prompt(original, compression_ratio=0.5)
print(f"Original: {original}")
print(f"Compressed: {compressed}")
# Output: "quick brown fox jumps lazy dog park sunny day"

Technique B: Summarization for Long Context

For very long context (chat history, documents), use a cheap model to summarize before passing to the expensive model:

def summarize_context(long_context, max_tokens=500):
    """Use cheap model to condense context"""
    summary_response = openai.ChatCompletion.create(
        model='gpt-4o-mini',  # Cheap model for preprocessing
        messages=[{
            'role': 'user',
            'content': f'Summarize this in {max_tokens} tokens:\n\n{long_context}'
        }],
        max_tokens=max_tokens
    )
    return summary_response.choices[0].message.content

# Then use summary with expensive model
long_chat_history = "..." # 5000 tokens
summary = summarize_context(long_chat_history, max_tokens=300)  # Costs ~$0.001

final_response = openai.ChatCompletion.create(
    model='gpt-4-turbo',
    messages=[
        {'role': 'system', 'content': f'Context summary: {summary}'},
        {'role': 'user', 'content': user_query}
    ]
)
# Saves 4700 input tokens on the expensive model

Savings Potential: 10-30%

Trade-offs: Slight latency increase (additional API call), possible minor quality degradation

Optimization 2: Streaming + Early Termination

The Problem:

You ask for a full response but only need the first few sentences. The model generates 800 tokens; you use 200.

The Solution:

Use streaming and stop generation when you have enough:

def get_completion_with_early_stopping(prompt, stop_condition, model='gpt-4-turbo'):
    """Stream response and stop early if condition is met"""

    accumulated_text = ""

    response = openai.ChatCompletion.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        stream=True
    )

    for chunk in response:
        delta = chunk.choices[0].delta.get('content', '')
        accumulated_text += delta

        # Check stop condition
        if stop_condition(accumulated_text):
            # Stop streaming
            break

    return accumulated_text

# Example: Stop after first complete sentence
def first_sentence_complete(text):
    sentences = text.split('. ')
    return len(sentences) >= 2

result = get_completion_with_early_stopping(
    "Explain quantum computing",
    first_sentence_complete
)

Savings Potential: 5-15% for use cases where partial responses suffice

Best For: Summaries, previews, classification (where you parse the first token)

Optimization 3: Batch Requests

The Problem:

You're processing requests in real-time even when latency doesn't matter (analytics, nightly reports, bulk processing).

The Solution:

Use OpenAI's Batch API for 50% cost reduction on non-urgent workloads.

import openai
import time

# Create batch file
batch_requests = []
for item in data_to_process:
    batch_requests.append({
        "custom_id": item['id'],
        "method": "POST",
        "url": "/v1/chat/completions",
        "body": {
            "model": "gpt-4-turbo",
            "messages": [{"role": "user", "content": item['prompt']}]
        }
    })

# Upload batch
with open('batch_requests.jsonl', 'w') as f:
    for req in batch_requests:
        f.write(json.dumps(req) + '\n')

batch_file = openai.File.create(
    file=open('batch_requests.jsonl', 'rb'),
    purpose='batch'
)

# Create batch job
batch_job = openai.Batch.create(
    input_file_id=batch_file.id,
    endpoint="/v1/chat/completions",
    completion_window="24h"  # Results within 24 hours
)

# Check status periodically
while batch_job.status not in ['completed', 'failed']:
    time.sleep(60)
    batch_job = openai.Batch.retrieve(batch_job.id)

# Retrieve results
if batch_job.status == 'completed':
    result_file = openai.File.download(batch_job.output_file_id)
    results = [json.loads(line) for line in result_file.splitlines()]

Savings Potential: 50% on eligible requests

Best For:

• Nightly analytics processing
• Bulk data labeling
• Report generation
• Any workload with >10 minute latency tolerance

Not For:

• User-facing features
• Real-time APIs
• Anything requiring immediate response

Strategic Optimizations (This Quarter)

These are larger investments with bigger payoffs.

Strategy 1: Fine-Tune Smaller Models

The Problem:

You're using GPT-4 because GPT-4o-mini doesn't quite meet your quality bar. But GPT-4 costs 30x more.

The Solution:

Fine-tune GPT-4o-mini on your specific task. A fine-tuned smaller model often outperforms a generic larger model.

When This Makes Sense:

• High request volume (>100K/month)
• Well-defined task (classification, extraction, specific writing style)
• You have or can create training data (500+ examples)

Cost Analysis:

• Fine-tuning cost: $2-5 per 1M tokens of training data (one-time)
• Inference cost: GPT-4o-mini is $0.15/M input vs GPT-4-turbo $10/M input

Break-even calculation:

If you're currently spending $3,000/month on GPT-4 for a specific task:

• Switch to fine-tuned GPT-4o-mini: $90/month (30x cheaper)
• Fine-tuning cost: $100 (one-time)
• Break-even: Immediate
• Monthly savings: $2,910

Steps:

1. Collect 500-1000 examples of your task (prompt + ideal completion)
2. Format as training data
3. Fine-tune GPT-4o-mini
4. Evaluate quality vs base GPT-4
5. Deploy if quality is acceptable

Savings Potential: 50-80% for high-volume, specialized tasks

Strategy 2: Self-Hosted Models

The Problem:

At massive scale, paying per token becomes unsustainable. If you're spending $50K+/month on LLM APIs, self-hosting might be cheaper.

The Solution:

Run open-source models on your infrastructure.

Break-Even Analysis:

Let's say you're spending $60,000/month on GPT-4 API:

Option A: Keep Using API

• Cost: $60,000/month = $720,000/year

Option B: Self-Host Llama 3 70B

• Infrastructure: 4x A100 GPUs ($10,000/month on AWS)
• Engineering: 0.5 FTE for maintenance ($75,000/year = $6,250/month)
• Total: $16,250/month = $195,000/year
• Savings: $525,000/year

When This Makes Sense:

• Spending >$30K/month on LLM APIs
• High request volume with consistent load
• Tasks where open-source models are competitive (coding, summarization, classification)
• Have ML engineering resources

When To Avoid:

• Low or variable request volume
• Tasks requiring cutting-edge capabilities (GPT-4-level reasoning)
• No ML engineering team to manage infrastructure

Hybrid Approach:

Many teams use a mix:

• Self-hosted models for high-volume, low-complexity tasks (90% of requests)
• Cloud APIs for complex, low-volume tasks (10% of requests)

This gives you most of the cost savings with a fallback for hard problems.

Setting Up Cost Monitoring

You can't optimize what you don't measure. Here's what to track:

Essential Metrics

Cost Per Request:

def calculate_cost_per_request(model, input_tokens, output_tokens):
    pricing = {
        'gpt-4-turbo': {'input': 0.01 / 1000, 'output': 0.03 / 1000},
        'gpt-4o-mini': {'input': 0.00015 / 1000, 'output': 0.0006 / 1000},
    }
    p = pricing.get(model, {'input': 0, 'output': 0})
    return (input_tokens * p['input']) + (output_tokens * p['output'])

Track by endpoint, user, feature.

Cost Per User:

Total spend / active users. Track trends over time. Outlier users may indicate abuse or bugs.

Cost Per Feature:

Tag each request with feature name. Understand which features drive costs.

Alerting Thresholds

Set up alerts at multiple levels:

Daily Budget:

DAILY_BUDGET = 500  # USD
current_spend = get_todays_spend()

if current_spend > DAILY_BUDGET * 0.8:
    send_alert('warning', f'80% of daily budget used: ${current_spend:.2f}')

if current_spend > DAILY_BUDGET:
    send_alert('critical', f'Daily budget exceeded: ${current_spend:.2f}')
    # Optional: implement circuit breaker

Cost Anomalies:

# Compare today to 7-day average
avg_daily_spend = get_average_spend(days=7)
todays_spend = get_todays_spend()

if todays_spend > avg_daily_spend * 1.5:
    send_alert('warning', f'Spend is 50% above average: ${todays_spend:.2f} vs ${avg_daily_spend:.2f}')

Per-User Limits:

def check_user_spend(user_id, limit=100):
    user_spend = get_user_spend_today(user_id)
    if user_spend > limit:
        # Throttle or block user
        return False
    return True

Circuit Breakers

Implement automatic cost controls:

class CostCircuitBreaker:
    def __init__(self, hourly_limit=100):
        self.hourly_limit = hourly_limit
        self.spend_this_hour = 0
        self.last_reset = time.time()

    def check_and_update(self, cost):
        # Reset if new hour
        if time.time() - self.last_reset > 3600:
            self.spend_this_hour = 0
            self.last_reset = time.time()

        # Check if we'd exceed limit
        if self.spend_this_hour + cost > self.hourly_limit:
            raise Exception(f'Cost limit exceeded: ${self.spend_this_hour:.2f}/${self.hourly_limit}')

        self.spend_this_hour += cost

# Usage
breaker = CostCircuitBreaker(hourly_limit=100)

def safe_llm_call(prompt):
    estimated_cost = estimate_cost(prompt)  # Estimate before calling
    breaker.check_and_update(estimated_cost)
    return call_llm(prompt)

Real-World Case Study: 40% Cost Reduction in 3 Weeks

Let's look at a real optimization journey.

The Company Profile

Company:    Mid-sized B2B SaaS
Feature:    AI-powered writing assistant
Users:      50,000 active users
Volume:     200,000 requests/day
Timeline:   3-week optimization sprint

Before (Monthly Cost: $12,000)

• Model: GPT-4 for everything
• Average prompt: 1,200 tokens (included long system message + full user profile)
• Average completion: 600 tokens
• Cost per request: $0.024
• No caching
• No cost alerts

Cost Breakdown:

• Input tokens: 240M × $0.01/1M = $2,400
• Output tokens: 120M × $0.03/1M = $3,600
• Total per day: $6,000 / 5 = $1,200 × 30 = $36,000/month (later corrected)

Actual calculation:

• 200K requests/day × $0.024 = $4,800/day
• Monthly: $4,800 × 25 working days = $12,000/month

Changes Implemented (Over 3 Weeks)

Week 1: Model Right-Sizing

• Analyzed request types
• Found 40% were simple rewrites/formatting
• Switched those to GPT-4o-mini
• Savings: $1,920/month (16%)

Week 2: Prompt Optimization

• Removed verbose system message (300 tokens saved)
• Made user profile context conditional (only include relevant fields)
• Average prompt: 1,200 → 600 tokens
• Savings: $1,200/month (10%)

Week 3: Caching + Output Limits

• Implemented semantic caching for common requests (30% cache hit rate)
• Set max_tokens=400 (down from unlimited, which averaged 600)
• Savings from caching: $1,440/month (12%)
• Savings from output limits: $960/month (8%)

After (Monthly Cost: $7,200)

• Mixed models (60% GPT-4, 40% GPT-4o-mini)
• Average prompt: 600 tokens
• Average completion: 400 tokens
• 30% cache hit rate
• Cost per request: $0.012 (50% reduction)
• Total savings: 40% ($4,800/month)

Timeline

• Week 1: Model routing (4 hours engineering time)
• Week 2: Prompt optimization (8 hours engineering time)
• Week 3: Caching implementation (12 hours engineering time)
• Total effort: 3 days of engineering work
• ROI: $4,800/month savings for 3 days work = 99% ROI in first month

Cost Optimization Checklist

Use this checklist to prioritize your efforts:

Quick Wins (Do This Week)

• [ ] Audit top 10 most expensive endpoints
• [ ] Switch simple tasks to cheaper models
• [ ] Remove verbose prompt instructions
• [ ] Set max_tokens limits on all requests
• [ ] Implement basic exact-match caching

Medium-Term (Do This Month)

• [ ] Implement semantic caching for FAQ-style queries
• [ ] Compress long-context prompts
• [ ] Move batch workloads to Batch API
• [ ] Set up cost monitoring dashboard
• [ ] Configure budget alerts

Strategic (Do This Quarter)

• [ ] Evaluate fine-tuning for high-volume tasks
• [ ] Analyze self-hosting ROI if spending >$30K/month
• [ ] Implement LLM-as-router for automatic model selection
• [ ] Build cost attribution by feature/user
• [ ] Create cost forecasting models

Ongoing

• [ ] Review cost reports weekly
• [ ] Test new models/providers for cost-quality tradeoffs
• [ ] Monitor cache hit rates
• [ ] Track cost per user trends
• [ ] Continuously optimize prompts

Conclusion

Cutting LLM costs by 40% is achievable for most teams without sacrificing quality. The key is systematic optimization:

1. Measure: Know where money goes
2. Right-size: Use cheap models for simple tasks
3. Trim: Remove prompt bloat
4. Cache: Stop paying for duplicate work
5. Monitor: Set up alerts to prevent surprises

Start with the quick wins. They take a few hours and deliver immediate savings. Then move to medium-term optimizations. Strategic changes like fine-tuning and self-hosting only make sense at significant scale.

The teams that control LLM costs are the ones that monitor proactively, optimize continuously, and treat token usage as a first-class metric alongside latency and error rate.

Your Action Plan

LLM costs don't have to spiral. With the right instrumentation and optimization mindset, you can scale your AI features sustainably.

Related Articles

• The Complete Guide to LLM Observability - Set up proper monitoring infrastructure
• Top 8 LLM Observability Tools - Compare cost tracking features across platforms

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