ucbrise/clipper

Health v2 · maintenance only

Adoption not part of the score

1420 stars · 281 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Clipper is a low-latency prediction serving system that sits between user-facing applications and machine learning models, exposing a standard REST interface for predictions and feedback. It supports multiple ML frameworks with adaptive batching, caching, and straggler mitigation, but is no longer actively maintained and is available only as a research artifact.

Use cases

• serve machine learning model predictions over a REST API
• deploy models from different ML frameworks behind one interface
• reduce prediction latency with adaptive batching and caching
• collect prediction feedback from applications for model improvement
• combine predictions from multiple models with ensemble methods
• run a self-hosted model serving cluster with Docker

When to choose

• you need a research-grade prediction serving system to study or extend
• you want a framework-agnostic REST layer in front of multiple ML models
• you need millisecond-latency serving with batching and caching techniques

When to avoid

• you need a maintained, production-supported model server
• you want modern features like GPU inference servers or Kubernetes-native serving
• your stack requires recent Python versions or active community support

Facets

service · maturity abandoned

machine-learning http-server caching monitoring deployment machine-learning artificial-intelligence backend apis microservices python cross-platform self-hosted prediction-serving model-serving model-deployment low-latency rest-api research-artifact adaptive-batching ensemble-methods docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="ucbrise/clipper")