ZenML Blog

We're expanding ZenML beyond its original MLOps focus into the LLMOps space, recognizing the same fragmentation patterns that once plagued traditional machine learning operations. We're developing three core capabilities: native LLM components that provide unified APIs and management across providers like OpenAI and Anthropic, along with standardized prompt versioning and evaluation tools; applying established MLOps principles to agent development to bring systematic versioning, evaluation, and observability to what's currently a "build it and pray" approach; and enhancing orchestration to support both LLM framework integration and direct LLM calls within workflows. Central to our philosophy is the principle of starting simple before going autonomous, emphasizing controlled workflows over fully autonomous agents for enterprise production environments, and we're actively seeking community input through a survey to guide our development priorities, recognizing that today's infrastructure decisions will determine which organizations can successfully scale AI deployment versus remaining stuck in pilot phases.

Discover the top 7 LlamaIndex alternatives to build AI production agents with ease.

In this LangGraph vs CrewAI article, we explain the difference between the three platforms and educate you about using them efficiently inside ZenML.

In this LangGraph pricing guide, we discuss the costs, features, and value LangGraph provides to help you decide if it’s the right investment for your business.

Discover the top 8 LangGraph alternatives for scalable agent orchestration.

Learn how to build production-ready agentic AI workflows that combine powerful research capabilities with enterprise-grade observability, reproducibility, and cost control using ZenML's structured approach to controlled autonomy.

In this ClearML pricing breakdown, we discuss the costs, features, and value ClearML provides to help you decide if it’s the right investment for your business.

In this Prefect vs Airflow vs ZenML article, we explain the difference between the three platforms and educate you about using them in tandem.

Discover why production teams are treating agentic workflows as MLOps evolution, not revolution—plus how ZenML achieved 200x performance improvements for enterprise ML operations. Real insights from 130+ MLOps engineers on building reliable AI systems.