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A fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.

Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

It lets you run machine learning models with a few lines of code, without needing to understand how machine learning works.

Amazon Elastic Inference allows you to attach low-cost GPU-powered acceleration to Amazon EC2 and Amazon SageMaker instances to reduce the cost of running deep learning inference by up to 75%. Amazon Elastic Inference supports TensorFlow, Apache MXNet, and ONNX models, with more frameworks coming soon.

Makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively.

Machine learning service that makes it easy for developers to add individualized recommendations to customers using their applications.

It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.

It is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. It was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.

Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

It is an open-source product analytics suite for LLM-based applications. Iterate faster on your application with a granular view of exact execution traces, quality, cost, and latency.

It is a platform for building production-grade LLM applications. It lets you debug, test, evaluate, and monitor chains and intelligent agents built on any LLM framework and seamlessly integrates with LangChain, the go-to open source framework for building with LLMs.

It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.

It is an open-source observability platform for GPT-3 users. Save on your OpenAI bills and identify application issues by monitoring usage, latency, and costs.

It is a cloud computing platform, primarily designed for AI and machine learning applications. The key offerings include GPU Instances, Serverless GPUs, and AI Endpoints. It is committed to making cloud computing accessible and affordable.

It is a platform that simplifies the process of building production-ready AI applications. It provides a fully managed compute platform for scaling machine learning workloads, offers a unified development environment, and ensures seamless transition between development and production.

Configure and deploy your own private, hosted API endpoints to process text, images, and other data using state-of-the-art machine learning in a few clicks. Chain together one or more models to efficiently process and extract insights from your data.

It provides all the infrastructure you need to deploy and serve ML models performantly, scalably, and cost-efficiently. Get started in minutes and avoid getting tangled in complex deployment processes.

It is the enterprise-grade stack for building AI products. It helps evaluate your LLM app, so you can quickly and confidently ship to production. It also provides a Typescript/Python library to log evaluation experiments and production data.

CVETodo is a New Zealand-based security vulnerability tracking service, founded in Tauranga with the mission to make security management simpler and more efficient for IT professionals worldwide. We provide real-time CVE monitoring, trending insights, and comprehensive vulnerability intelligence to help organizations stay ahead of security threats.

It is a cloud-native AI gateway written in Go. Currently, it serves as a proxy to OpenAI. We let you create API keys that have rate limits, cost limits, and TTLs. The API keys can be used in both development and production to achieve fine-grained access control that is not provided by OpenAI at the moment. The proxy is compatible with OpenAI API and its SDKs.

It is an open-source AI model router engineered for efficiency & optimized for performance. Smoothly manage multiple LLMs and image models, speed up responses, and ensure non-stop reliability.

It dynamically routes requests to the best LLM in real-time. Higher performance and lower cost than any individual provider. See the results for yourself.

It is the first platform built for prompt engineers. Visually manage prompts, log LLM requests, search usage history, collaborate as a team, and more.

It is an open-source Python package for specifying structure and type, validating and correcting the outputs of large language models (LLMs).

It is a lightweight, cloud-native, and open-source LLM gateway, delivering high-performance LLMOps in one single binary. It provides a simplified way to build application resilience, reduce latency, and manage API keys.

It is an open-source toolkit for monitoring Large Language Models (LLMs). It extracts signals from prompts & responses, ensuring safety & security.

It is a specialized cloud provider, delivering a massive scale of GPUs on top of the industry’s fastest and most flexible infrastructure. It runs a fully-managed, bare metal serverless Kubernetes infrastructure to deliver the best performance in the industry while reducing your DevOps overhead.

It is a comprehensive tool designed to fortify the security of Large Language Models (LLMs). By offering sanitization, detection of harmful language, prevention of data leakage, and resistance against prompt injection attacks, it ensures that your interactions with LLMs remain safe and secure.

Create a scalable cloud architecture in minutes and with just a few prompts. It is an autonomous AI tool that helps developers build, deploy, and troubleshoot applications, streamlining DevOps tasks and making cloud operations more efficient.

It enables developers to rapidly build and improve custom fine-tuned models. Using this platform, you can turn raw data into a secure, production-ready, self-improving LLM in minutes.

It is a no-code compute platform for language models. It is aimed at AI developers and product builders. You can also vibe-check and compare quality, performance, and cost at once across a wide selection of open-source and proprietary LLMs.

It is an open-source, self-hostable vector database for semantic similarity search that specializes in low query latency. It bridges the gap between information retrieval and memory retention in Large Language Models.