Proof of Usefulness Report

cachebench: prompt-cache observability for LLM APIs

Analysis completed on 5/15/2026

+38

Proof of Usefulness Score

You're In Business

Cachebench addresses a highly relevant and timely problem in LLM pipeline observability, offering a unified way to track prompt cache effectiveness across major providers. However, as a very early-stage open-source library launched just days prior to evaluation, it lacks quantifiable audience reach, user adoption, or revenue metrics, placing its score appropriately in the minimal traction tier.

Ready to Compete for $150k+ in Prizes?

Move this data into a HackerNoon blog draft to become eligible for your share of $150k+ in cash and software prizes

View All Reports

Score Breakdown

Real World Utility+17.5

Audience Reach Impact+1.0

Technical Innovation+9.0

Evidence Of Traction+0.625

Market Timing Relevance+8.5

Functional Completeness+3.75

Subtotal+40.375

Usefulness Multiplierx0.95

Final Score+38

Project Details

Project URL

github.com/MukundaKatta/cachebench

Description

A Python library that turns prompt-cache hits/misses into a numeric report you can act on. Per-call hit ratios, dollars saved, regression alerts when cache effectiveness drops, and miss-aware retry helpers. Works across Anthropic, OpenAI, and Bedrock so teams running multi-provider LLM pipelines can finally see what their cache layer is actually saving them.

Audience Reach

Early stage. cachebench v0.1.0 went live on PyPI on 2026-05-10. Adoption signal will be PyPI installs and GitHub stars over the next 30 days. Author has shipped 30+ open-source libraries (npm/PyPI/crates.io) under the same handle, so distribution flywheel is established.

Target Users

Teams running production LLM pipelines across multiple providers (Anthropic, OpenAI, Bedrock) who need to know what their prompt cache is actually saving them. Drop in next to your existing API client, get per-call hit ratios, dollars saved, and regression alerts when cache effectiveness drops.

Technologies

Bright Data, Python, PyPI, Anthropic SDK, OpenAI SDK, AWS Bedrock, GitHub Actions

Traction Evidence

GitHub: https://github.com/MukundaKatta/cachebench (MIT). PyPI: https://pypi.org/project/cachebench/ (v0.1.0 published 2026-05-10). Companion entries in the same agent-stack ecosystem: https://github.com/MukundaKatta/agentmemory (npm + PyPI), https://github.com/MukundaKatta/driftvane (PyPI), https://github.com/MukundaKatta/bedrock-kit (PyPI). 30+ shipped open-source libraries across npm, PyPI, and crates.io under @mukundakatta.

Algorithm Insights

Market Position

Growing utility with room for optimization

User Engagement

Documented reach suggests active user community

Technical Stack

Modern tech stack aligned with sponsor technologies

Recommendations to Increase Usefulness Score

Document User Growth

Provide specific metrics on user acquisition and retention rates

Showcase Revenue Model

Detail sustainable monetization strategy and current revenue streams

Expand Evidence Base

Include testimonials, case studies, and third-party validation

Technical Roadmap

Share development milestones and feature completion timeline