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
- Readable in 8 minutes
- Published 2026-02-03
- 7 context themes covered
Sending a whole repository with every question can waste input tokens. This guide shows how to compare raw-context and focused-context costs with your own provider price, query volume, and measured token usage.
Key Takeaways
- Measure before optimizing — record the input tokens used by a real project question
- Use scenario math — substitute your provider price and daily volume
- Evidence first, lower cost — focused context makes answers easier to verify
- No LLM changes required — keep using Claude, GPT, or Gemini
An illustrative raw-context cost
The following hypothetical example uses a 500K-token repository, 100 questions per day, and a $3 per million input-token price. It is arithmetic, not a customer result or a current provider quote.
Illustrative raw-context scenario
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.
Most questions need a subset of the repository
For a scoped question, many repository files will be unrelated. When you ask "How does authentication work?", you probably do not need:
- Your styling configuration
- Test fixtures for unrelated features
- Documentation for the billing system
- Package lock files
- Most of your components
Measure which sources answer the question, then compare their token count with the raw input. The 5K-token value below is an example budget, not a guaranteed requirement.
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:
- Lower input usage when retrieval returns a smaller useful context
- Hybrid search (keywords + semantic)
- Structure-aware chunking
- Session memory
- No infrastructure to maintain
- Works with existing LLM
- Monthly subscription ($49+)
- Requires indexing your docs
The math: one illustrative 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:
Track three outcomes on your own workload: input tokens, whether the answer cites the source you expected, and whether a reviewer accepts the answer. Focused context can improve those signals, but this article does not claim a universal rate.
Get Started in 60 Seconds
Snipara's free plan includes 1,000 queries/month — enough to see the cost difference on a real project.
# Claude Code - add Snipara MCPclaude mcp add --scope project --transport http snipara \ "https://api.snipara.com/mcp/YOUR_PROJECT" \ --header "X-API-Key: YOUR_KEY"# Query with automatic context optimizationsnipara_context_query("How does auth work?", max_tokens=5000)