A framework providing the right abstractions to ease research, development, and deployment of your ML pipelines.
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An Easy-to-Use and High-Performance AI deployment framework.
Neural networks framework in pure C: training and inference, no dependencies.
Simplifies and accelerates MLOps by bridging the gap between ML models and edge hardware.
Open-source GenAI and LLM observability platform native to OpenTelemetry with traces and metrics. opensource.
Open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease.
Turns your ML code into microservices with web API, interactive GUI, and more.
Platform and SDK for AI Engineers providing tools for LLM evaluation, observability, and a version-controlled enhanced prompt playground.
Vector database plugin for Postgres, written in Rust, specifically designed for LLM.
Open-source vector similarity search for Postgres.
MLOps and LLMOps, from the trenches.
Democratize and productionize Gen AI across your entire org with Portkey.
Prompt Engineering platform. Collaborate, test, evaluate, and monitor your LLM applications.
Quix is the agentic AI platform for hardware engineering — turn test-rig and sensor data into real-time insight.
OpenAI-compatible LLM inference server for Apple Silicon using MLX. 2-4x faster than Ollama with tool calling and prompt caching.
Examples showing how to use the OpenAI vision API to run inference on images, video files and webcam streams.
Provides containers to encapsulate and deploy EdgeML pipelines and applications.
MLOps framework to package, deploy, monitor and manage thousands of production machine learning models.
Python-free Rust inference server with OpenAI API compatibility and hot model swapping.
An MLOps/LLMOps platform for model building, evaluation, and fine-tuning.
Lets you create apps for your ML projects with deceptively simple Python scripts.
TensorZero builds open-source tools for production-grade LLM applications: LLM gateway, observability, optimization, evaluations, and experimentation.
Inference for text-embedding models.
A flexible and easy to use tool for serving PyTorch models.