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Standardized Serverless ML Inference Platform on Kubernetes.

Developer-friendly, serverless vector database for AI applications. Easily add long-term memory to your LLM apps!

LLM Ops platform with Analytics, Monitoring, Evaluations and an LLM Optimization Studio powered by DSPy.

Neural network inference from the command line, implemented in CHICKEN Scheme.

A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.

Collection of infrastructure and tools for research in neural network interpretability.

Version and deploy your ML models following GitOps principles.

Machine learning model serving framework with dynamic batching and pipelined stages, provides an easy-to-use Python interface.

Ncnn is a high-performance neural network inference framework optimized for the mobile platform.

Easy-to-use library to boost AI inference.

A framework providing the right abstractions to ease research, development, and deployment of your ML pipelines.

An Easy-to-Use and High-Performance AI deployment framework.

Neural networks framework in pure C: training and inference, no dependencies.

Turns your ML code into microservices with web API, interactive GUI, and more.

Parris, the automated infrastructure setup tool for machine learning algorithms.

Vector database plugin for Postgres, written in Rust, specifically designed for LLM.

Testing infrastructure for LLM and agentic applications with collaborative evaluation.

Provides containers to encapsulate and deploy EdgeML pipelines and applications.

MLOps framework to package, deploy, monitor and manage thousands of production machine learning models.

An MLOps/LLMOps platform for model building, evaluation, and fine-tuning.

Lets you create apps for your ML projects with deceptively simple Python scripts.

Inference for text-embedding models.

A flexible and easy to use tool for serving PyTorch models.

Provides an optimized cloud and edge inferencing solution.