How We Reduced AI Coding Costs by 63%
The Benchmark
We tested Ragtoolina against standard file-inclusion strategies across 50 real-world coding questions on open-source projects ranging from 10k to 500k lines of code.
For each question, we measured:
- Token count — Total input tokens sent to the model
- Answer quality — Rated by senior engineers on a 1-5 scale
- Latency — Time from question to complete response
Results
| Metric | Without Ragtoolina | With Ragtoolina | Change | |--------|-------------------|-----------------|--------| | Avg. tokens per query | 48,200 | 17,800 | -63% | | Avg. quality score | 4.1 / 5 | 4.2 / 5 | +2% | | Avg. response time | 12.3s | 5.8s | -53% |
The key finding: fewer tokens led to equal or better answers. By removing irrelevant context, the model could focus on the code that actually mattered.
Why Fewer Tokens = Better Answers
It seems counterintuitive, but large context windows filled with noise actively hurt model performance. Models struggle to find relevant information in a sea of irrelevant code. Ragtoolina's semantic search ensures the model sees only what it needs.
What This Means for Your Budget
If your team spends $500/month on AI coding tokens, Ragtoolina can bring that down to around $190/month — while improving answer quality. At $19/month for Pro, the ROI is immediate.
Check our ROI calculator on the homepage to estimate your savings.