🤖Jevstiller Cuts Network Calls by 98%, Local Model Answers in 15ms
Local model answers 98% of requests, cutting network calls
TL;DR
Jevstiller uses a local model to answer 98% of requests in 15ms, reducing network calls to Jev by 98%. This speeds up batch jobs and cuts costs.
Jevstiller introduces a local model that answers 98% of requests in just 15ms, drastically reducing network calls to Jev. This speeds up batch jobs and cuts costs. Developers using Jev for batch processing will see significant performance improvements. The local model is trained on a few thousand rows, retrained every 2,000 new Jev answers, and shadow-tested on live traffic before promotion. The system ensures a 95% probability that the local model adheres to the 98% accuracy contract.
Key Points
Jevstiller answers 98% of requests locally, reducing network calls to Jev by 98%
Local model trains a multinomial logistic regression head in seconds on a few thousand rows
Model retrained every 2,000 new Jev answers, versioned, and shadow-tested on live traffic
System ensures 95% probability that local model adheres to 98% accuracy contract
Local model answers in 15ms on a CPU, compared to 300ms for Jev network calls
Why It Matters
If you're running batch jobs with Jev, Jevstiller cuts network calls by 98%, reducing batch job time from 300ms to 15ms. This speeds up processing and cuts costs. However, smaller databases may not see the same benefits, as the $0.20/GB-month premium only pencils out above ~100K read IOPS.
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