Complete Guide: Optimize Your LLM Costs with Praevia.ai

Intensive use of language models (LLMs) can quickly become expensive. Discover how Praevia.ai allows you to reduce your expenses by 50 to 90% without compromising quality.

Why Optimize Your LLM Costs?

Companies using LLMs face three major challenges:

• High Costs: Tokens accumulate quickly, especially with large contexts
• Significant Latency: More context = more processing time
• Technical Complexity: Manually managing optimization takes time

LLM Cost Analysis

The Numbers That Speak

| Metric | Without Praevia | With Praevia | Savings | |--------|-----------------|--------------|---------| | Tokens per request | 10,000 | 2,000 | 80% | | Cost per request | $0.50 | $0.10 | 80% | | Average latency | 2.5s | 0.8s | 68% |

How Does Praevia.ai Work?

Our optimization engine uses three advanced techniques:

1. Intelligent Context Selection

# Example using the Praevia API
import praevia

# Your large context
context = load_large_context()  # 50,000 tokens

# Intelligent compression
optimized = praevia.optimize(
    context=context,
    query="What is the Q3 marketing strategy?",
    target_tokens=2000
)

# Result: reduced but relevant context
print(f"Original tokens: {len(context)}")
print(f"Optimized tokens: {len(optimized)}")
# Output: 50,000 → 2,000 tokens (96% reduction)

2. Semantic Compression

Our algorithm analyzes the content and keeps only essential information:

"Rather than sending 100 pages of documentation, Praevia identifies and extracts the 2 paragraphs truly relevant to your question."

Praevia Architecture

3. Smart Cache

Similar queries reuse already optimized contexts, further accelerating performance.

Concrete Use Cases

Automated Customer Support

Problem: Knowledge base of 500,000 tokens
Solution: Reduction to 3,000 tokens on average
Result: Savings of $15,000/month

Legal Document Analysis

Problem: Processing 200-page contracts
Solution: Extraction of relevant clauses only
Result: Processing time divided by 10

Code Assistant

Problem: Analyzing entire codebase on each query
Solution: Contextual selection of relevant files
Result: Costs divided by 5

Analytics Dashboard

Universal Compatibility

Praevia.ai works with all LLMs:

• OpenAI (GPT-4, GPT-3.5)
• Anthropic (Claude)
• Mistral AI
• Cohere
• Open-source models (Llama, etc.)

Setup in 3 Steps

Step 1: Installation

npm install @praevia/sdk
# or
pip install praevia-sdk

Step 2: Configuration

import { Praevia } from '@praevia/sdk';

const praevia = new Praevia({
  apiKey: process.env.PRAEVIA_API_KEY,
  compression: 'auto', // or 'aggressive', 'balanced'
});

Step 3: Usage

const result = await praevia.optimize({
  context: largeContext,
  query: userQuery,
  targetSize: 2000, // target tokens
});

// Use optimized context with your LLM
const response = await openai.chat.completions.create({
  messages: [
    { role: 'system', content: result.optimizedContext },
    { role: 'user', content: userQuery }
  ]
});

Metrics and Monitoring

Praevia provides a complete dashboard to track your savings:

• Tokens saved in real-time
• Before/after optimization costs
• Average query latency
• Compression rate by document type

Monitoring Dashboard

Transparent Pricing

We only charge for what you use:

1. Starter: Up to 10M tokens/month - Free
2. Pro: Unlimited usage - $0.0001/1000 optimized tokens
3. Enterprise: On-premise + dedicated support - Custom

Tip: Even with our billing, you save 70-80% compared to sending full context to your LLM.

Conclusion

Context optimization is no longer an option, it's a necessity for any AI application in production.

Praevia.ai allows you to:

• Reduce your costs by 50-90%
• Improve response speed
• Maintain result quality
• Scalability without cost explosion

Ready to Get Started?

Create a free account or Request a demo

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Frequently Asked Questions

Q: Is response quality impacted?
A: No, our semantic engine preserves relevant information. Our tests show 98% identical satisfaction.

Q: Is it compatible with my infrastructure?
A: Yes, our API integrates with any LLM and can be deployed on-premise.

Q: How long does integration take?
A: On average 2 hours for basic integration, 1 day for complete production deployment.