How to Cut Your LLM API Costs by 90%
LLM API costs spiraling out of control? Learn how context optimization reduces token usage from 500K to 5K per query — cutting your Claude and GPT bills from $4,500 to $45/month while improving answer quality.
Alex Lopez
Founder, Snipara
LLM API costs can spiral out of control. At $3-15 per million tokens, a team of developers querying Claude or GPT with full codebases can spend $500-5,000/month on API calls alone. Here's how to cut those costs by 90% without sacrificing answer quality.
Key Takeaways
- 90% of context is irrelevant — only ~5K tokens matter for any given query
- $4,500 → $450/month — real savings at 100 queries/day
- Better answers, lower cost — focused context reduces hallucinations
- No LLM changes required — keep using Claude, GPT, or Gemini
The Real Cost of Raw Context
Let's calculate what most teams are actually spending:
Typical Workflow (Expensive)
And that's assuming you can even fit 500K tokens in the context window. Most developers manually select files, missing critical context and getting inconsistent answers.
The 90% Rule: Most Context Is Noise
Here's the insight that changes everything: for any given query, 90% of your codebase is irrelevant. When you ask "How does authentication work?", you don't need:
- Your styling configuration
- Test fixtures for unrelated features
- Documentation for the billing system
- Package lock files
- Most of your components
You need maybe 5-10 files. That's 5-10K tokens, not 500K.
Three Strategies to Cut LLM Costs
Strategy 1: Manual File Selection (Free, Tedious)
You can manually select which files to include in your prompt. This works but has major drawbacks:
- No additional tools needed
- Complete control
- Time-consuming (5-10 min per query)
- Easy to miss relevant files
- Inconsistent results
- Doesn't scale to complex questions
Strategy 2: RAG Pipeline (Complex, Medium Savings)
Set up a retrieval-augmented generation pipeline with vector embeddings. Better than manual, but requires significant engineering:
- Automated retrieval
- Semantic understanding
- Complex to set up correctly
- Fixed-size chunking breaks code
- Misses exact matches (function names)
- No session memory
- Ongoing maintenance
Strategy 3: Context Optimization Service (Best ROI)
Use a purpose-built context optimization layer that handles retrieval, ranking, and token budgeting:
- 90%+ cost reduction
- Hybrid search (keywords + semantic)
- Structure-aware chunking
- Session memory
- No infrastructure to maintain
- Works with existing LLM
- Monthly subscription ($19-49)
- Requires indexing your docs
The Math: Real Cost Comparison
| Approach | Tokens/Query | Cost/Query | Monthly (100/day) |
|---|---|---|---|
| Raw codebase | 500K | $1.50 | $4,500 |
| Manual selection | 50K | $0.15 | $450 |
| Basic RAG | 20K | $0.06 | $180 |
| Context optimization | 5K | $0.015 | $45 |
Bonus: Better Answers at Lower Cost
The surprising benefit: focused context produces better answers. When you reduce noise, the LLM can focus on what matters:
Get Started in 60 Seconds
Snipara's free plan includes 100 queries/month — enough to see the cost difference on a real project.
# Claude Code - add Snipara MCPclaude mcp add snipara \ --header "X-API-Key: YOUR_KEY" \ https://api.snipara.com/mcp/YOUR_PROJECT# Query with automatic context optimizationrlm_context_query("How does auth work?", max_tokens=5000)