MLOps Companies and Platforms for Model Deployment 2026

Berkeley, California

AimStack is a software development company based in Berkeley, California, founded in 2018. The company focuses on creating open-source AI metadata infrastructure to make AI development tools more accessible. AimStack consists of a small team of engineers and designers dedicated to addressing the complexities of AI metadata. The company's flagship product, Aim, is an open-source, self-hosted AI metadata tracking tool. It efficiently logs AI metadata, including experiments, prompts, and training runs, and features a user-friendly interface for exploring and comparing training runs. Aim also provides an SDK for programmatic metadata queries, making it suitable for experiment tracking and prompt engineering. Additionally, AimStack is developing AimHub, a collaboration platform designed to facilitate team-based management of machine learning experiments. Aim has already attracted thousands of AI researchers who utilize its capabilities to streamline their workflows. AimStack's mission is to enhance the observability of AI systems through its innovative open-source solutions.

42020

DagsHub

San Francisco, California

DagsHub is an MLOps collaboration platform that provides an end-to-end solution for managing multimodal AI data, tracking experiments, and versioning models. Founded in 2019 and based in Tel Aviv, Israel, DagsHub serves data scientists, ML engineers, and AI teams, from individual developers to large enterprises. The platform is built on popular open-source tools like Git, DVC, and MLflow, facilitating streamlined workflows in data science and machine learning. DagsHub offers a unified platform with capabilities such as dataset curation and annotation, experiment tracking, model management, and versioning. It supports secure and scalable MLOps workflows, including integration with Red Hat OpenShift. The platform is designed to transform unstructured data into high-quality datasets, enhancing AI model performance. DagsHub caters to a diverse audience, including individual developers, mid-market companies, and enterprise teams, focusing on scalable data management and collaborative efforts in machine learning.

112020

Guild AI

San Francisco, California

Guild.ai is a software development company based in San Francisco, founded in 2025. It specializes in an open, collaborative platform that acts as a neutral control plane for building, deploying, and scaling AI agents. The company promotes an open-source-like approach, allowing engineers to share intelligence, tools, and code, fostering collaboration and innovation in AI development. The mission of Guild.ai is to make AI building open and transparent, empowering developers to innovate quickly while ensuring reliability and community involvement. The platform supports production-grade operations with built-in safety features, including governance and auditability. Key offerings include tools for creating and sharing specialized AI agents, such as a Jira Agent for task delegation, a Slack Bot for workspace interaction, and a GitHub Commit Security Guard for analyzing commit history. Guild.ai has raised funding to expand its platform, with Google Ventures as a key partner.

422025

KServe

KServe is an open-source platform designed for deploying and scaling generative and predictive AI models on Kubernetes. As a Cloud Native Computing Foundation (CNCF) incubating project, it simplifies the process of productionizing AI models, providing a unified solution that operates across various cloud environments and on-premises infrastructure. The platform offers key services such as model deployment and serving, infrastructure abstraction, and multi-model orchestration. KServe allows users to deploy large language models and traditional machine learning models using OpenAI-compatible APIs. It manages complex tasks like autoscaling and health checking, enabling users to configure deployments easily with YAML files. KServe is capable of handling traffic spikes and efficiently managing hundreds of models, making it suitable for enterprise-scale applications. Its cloud-agnostic nature ensures consistent performance across AWS, Azure, GCP, and hybrid environments.