Community Savings
0
tokens saved
↑ +48K/s
94.2% average savings rate · $0 cost saved · 12ms latency
Token Savings
0%
1.6× better than compression
Output Quality
0%
Recall@5 on benchmark suite
Optimize Latency
0ms
Rust engine, budget=8K tokens
Languages
0
Skeleton extraction support
Test Suite
0 + 0
Rust + Python tests passing
Token Savings — Same Workload, Head-to-Head
⚡ Entroly
Compression
Top-K / Raw
Tokens Saved (Last 48h)
Kolmogorov Entropy
Submodular IOS
Multi-Resolution TPSE
PRISM RL Feedback
Rust Data Plane
EGSC Cache
Frequently Asked Questions
What do the live token savings metrics show?
They show projected community savings based on measured benchmarks. The figures are extrapolated from verified token reduction rates across real-world codebases. On average, users reduce their token payload by 94.2%.
How does Entroly reduce Claude and ChatGPT API bills?
Entroly runs a local Rust-based knapsack optimizer to select the most relevant files/functions for your query, then applies multi-resolution context engineering, and stabilizes the prefix so provider cache discounts (e.g. Claude Prompt Caching) remain active.
Does Entroly add significant latency overhead?
No. The local Rust engine processes and optimizes context in under 12 milliseconds, which is negligible compared to the seconds saved in generation time and network latency by sending smaller prompt payloads.
Is my local data private?
Yes, absolutely. Entroly operates entirely as a local proxy on your machine. Your code never leaves your local environment. No outbound analytics by default.
Reduce prompt tokens locally.
Measure quality honestly.
One command. Zero config. Information-theoretic selection with benchmarked retention, proxy headers, and local no-LLM measurement.