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MLflow GenAI Evaluation vs ZenML

MLflow GenAI Evaluation

mlflow.org

ZenML

zenml.io

Side by side

FigureMLflow GenAI EvaluationZenML
Price fromFree$999/mo
Free planYesYes
Free trial——
Open source——
WebYesYes
Windows—Yes
macOS—Yes
Linux—Yes
APIYesYes
Self-hostedYesYes
Plans published13
Spec score · ML Experiment Tracking Software7.5 (#17)7.8 (#11)
Spec score · MLOps Platforms7.5 (#13)7.8 (#7)
IntegrationsMLflow Tracing lists integrations with 40+ LLM and agent libraries and frameworks, including OpenTelemetry, LangChain, OpenAI, and Anthropic.The integrations directory lists 66 tools, including LangGraph, OpenAI Agents SDK, PyTorch, MLflow, AWS, and Google Cloud Vertex AI Pipelines.
PurposeMLflow helps teams measure, improve, and monitor the quality of LLM applications and AI agents from development through production.ZenML orchestrates AI and machine-learning pipelines and agents written in Python.

Plans

MLflow GenAI Evaluation

  • MLflow GenAI EvaluationFree

MLflow GenAI Evaluation pricing →

ZenML

  • Open SourceFree
  • Scale$999 a month
  • EnterprisePriced on request

ZenML pricing →

In their own words

MLflow GenAI Evaluation

MLflow GenAI Evaluation is a free, open-source API and UI for measuring, improving, and monitoring LLM applications and AI agents from development through production. Evaluation Datasets centralize test cases, expected answers, and evaluation data. Teams can collect feedback from end users and domain experts, attach it to traces, and retain metadata. Scoring includes LLM-as-a-Judge, custom LLM judges, and code-based scorers; automatic evaluation supports LLM judges but not code-based scorers. Results appear in the MLflow UI with per-record details and comparisons between agent versions. MLflow Tracing records production metrics such as latency and token usage. Integrations are listed for more than 40 LLM and agent libraries and frameworks, including OpenTelemetry, LangChain, OpenAI, and Anthropic. Judge adapters include Anthropic, Bedrock, Google, and xAI. API, self-hosted, and web options are available, with Python SDK support, prompt history and testing, regression runs, safety evaluations, tool-call checks, deployment controls, and optional basic HTTP authentication.

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ZenML

ZenML orchestrates AI and machine-learning pipelines and agents written in Python. Pipelines can run on Kubernetes, Vertex AI, SageMaker, AzureML, or a laptop without rewriting code. ZenML versions step outputs such as models and evaluation datasets, supports re-execution from any step, and reuses cached results when code and inputs have not changed. Deployment choices include local, SaaS, and self-hosted setups, with Docker and Kubernetes Helm methods. ZenML says it stores metadata while data, artifacts, and compute remain in the customer's infrastructure or VPC. Its integrations directory lists 66 tools, including LangGraph, OpenAI Agents SDK, PyTorch, MLflow, AWS, and Google Cloud Vertex AI Pipelines. The self-hosted Open Source plan is free and includes unlimited executions and projects. Scale costs $999 a month and includes 2,000 executions, three projects, and five snapshots. Enterprise has custom pricing and adds SSO, custom-role RBAC, audit logs, and air-gapped deployment. ZenML says it is SOC 2 Type II and ISO 27001 certified.

Visit zenml.io