ZenML

ZenML is a unified layer for machine learning and AI work, now spanning two products: ZenML for ML pipelines and Kitaru for replaying and improving AI agents, with ZenML Pro providing a managed control plane over both. The pairing reflects a real problem in teams currently running both traditional models and agent-based systems, which are usually tooled entirely separately despite sharing infrastructure, data and the same engineers.

Kitaru's replayability is the interesting capability. Being able to replay an agent run and change something is the equivalent of a reproducible pipeline in traditional machine learning, and it addresses the same underlying problem: without it, debugging depends on whether the failure happens again, which for non-deterministic systems is not a reliable assumption.

The pricing structure is unusually simple. One flat plan at $39 covers a Kitaru workspace with agent evaluations and a ZenML workspace with ML pipelines, with community support and a 14-day free trial. A Scale SaaS tier at $999 targets teams running machine learning in production, adding a model registry among other capabilities.

A single flat plan rather than a feature matrix is a deliberate simplification, and it suits the buyer, since a small ML team evaluating tooling wants to know whether it works rather than which tier contains the feature it needs.

The LLMOps Database is a genuinely valuable published resource independent of the product: 2,007 searchable case studies of how organisations have actually deployed language model systems. Material of that kind is scarce, since most public writing on the subject is either vendor marketing or research papers rather than accounts of production deployments.

The open-source project has over 5,500 GitHub stars, with documentation published separately for both Kitaru and ZenML.