ZenML vs Metaflow — Comparison

ZenML

One layer for orchestration, versioning, and governance — from training pipelines to agent evals, local to Kubernetes.

Stop writing fragile scripts to connect your tools. ZenML provides a standardized protocol to bind your data retrieval (LlamaIndex), reasoning (LangChain), and training (PyTorch) steps into a single, cohesive system. Teams lose velocity rewriting notebook code for the cloud. ZenML allows the exact same @step to run locally for debugging, in batch for massive evaluations, and then deploy seamlessly to your production serving infrastructure. Your current orchestrator runs the job, but it doesn't track the data. ZenML adds a metadata layer to tools like Airflow or Kubeflow, giving you the artifact lineage and reproducibility that raw orchestrators lack. Built on Apache 2.0 for flexibility, hardened for the enterprise. Deploy ZenML inside your own VPC. Keep full sovereignty over your data, models, and API secrets while meeting SOC2 and ISO 27001 standards. Join thousands of teams using ZenML to eliminate chaos and accelerate AI delivery 60+ integrations across the AI ecosystem. From sklearn to LangGraph. We have put down our expertise around building production-ready, scalable AI platforms, building on insights from our top customers. Learn how teams are using ZenML to save time and simplify their MLOps. ZenML tracks production AI deployments across the industry Former Chief Scientist Salesforce and Founder of You.com ML Engineer / ML Ops / ML Solution architect at ADEO Services Professor of Linguistics and CS at Stanford Machine Learning Engineer at WiseTech Global Stay updated on the latest developments, announcements, and updates from the ZenML ecosystem. ZenML is a metadata layer on top of your existing infrastructure, meaning all data and compute stays on your side. ZenML is SOC2 and ISO 27001 compliant, validating our adherence to industry-leading standards for data security, availability, and confidentiality in our ongoing commitment to protecting your ML workflows and data. Subscribe to the ZenML newsletter and receive regular product updates, tutorials, examples, and more. Thanks for subscribing! Check your inbox to confirm. We care about your data in our privacy policy. Everything you need to know about the product. Predictable, transparent pricing that scales with value. Apply for a special price to access ZenML Pro features for early-stage companies building ML-powered products, universities, research institutions, and educational use cases. ZenML is a metadata layer on top of your existing infrastructure, meaning all data and compute stays on your side. ZenML is SOC2 and ISO 27001 Compliant ZenML is SOC2 and ISO 27001 compliant, validating our adherence to industry-leading standards for data security, availability, and confidentiality in our ongoing commitment to protecting your ML workflows and data. Everything you need to know about the product. Join the ZenML Community and start improving your MLOps stacks registered last 12 months ZenML offers the capability to build end-to-end ML workflows t

Metaflow

Build and manage real-life ML, AI, and data science projects with Metaflow.

Open-source Metaflow makes it quick and easy to build and manage real-life ML, AI, and data science projects. Explore with notebooks, develop with Metaflow, and test and debug locally. Results are stored and tracked automatically for easy analysis. Break out from the confines of a laptop or a single notebook. Scale out easily to the cloud, utilizing GPUs, multiple cores, and multiple instances in parallel. Metaflow organizes the work for easy collaboration on the way. Deploy experiments to production with a single click without changing anything in the code. Make flows react to updating data and other events automatically. Get started easily on a laptop. When you are ready to scale, deploy the Metaflow stack on your cloud account or on-premise Kubernetes cluster. Metaflow integrates seamlessly with your existing infrastructure, security, and data governance policies. To get a taste of Metaflow in the cloud, try Metaflow Sandbox in the browser. Deploy on EKS and S3, or AWS Batch & AWS Step Functions. Deploy on AKS and Azure Blob Storage. Deploy on GKE and Google Cloud Storage. For maximum flexibility, deploy on a custom Kubernetes cluster. Metaflow was originally developed at Netflix to address the needs of developers and data scientists who work on demanding real-life ML, AI, and data projects. Netflix open-sourced Metaflow in 2019. Today, Metaflow is used by hundreds of companies across industries, powering diverse projects from state-of-the-art GenAI and compute vision to business-oriented data science, statistics, and operations research. Create flows incrementally step-by-step with the new spin command Build agentic systems with the new recursive and conditional steps Compose flows with reusable custom decorators Use uv to manage dependencies, from dev to cloud Setup the full Metaflow stack on your laptop with one click Checkpoint long-running model training and other tasks with the new @checkpoint decorator Configure flows freely with the new Config object New APIs allow you to run and deploy Metaflow in notebooks and scripts Learn about various patterns of scalable compute with Metaflow. Train and fine-tune large language models and other generative AI models on AWS Trainium. Build observable ML/AI systems with cards that update in real-time. Install dependencies from PyPI as well as Conda in your Metaflow steps. Connect to external services securely using the new @secrets decorator. Metaflow 2.9 allows you to trigger workflows based on real-time events. Apache Arrow and Metaflow.S3 make it easy to process data fast. Learn how to use Metaflow for demanding GPU tasks. Develop with Metaflow, deploy on your existing Apache Airflow servers. Deploy and operate Metaflow on GCP and all other m

ZenML

Metaflow

ZenML

Metaflow

ZenML

Metaflow