Analysis of 1,200+ production LLM deployments reveals that context engineering, architectural guardrails, and traditional software engineering skills—not frontier models or prompt tricks—separate teams shipping reliable AI systems from those stuck in demo purgatory.
Analysis of 1,200 production LLM deployments reveals six key patterns separating successful teams from those stuck in demo mode: context engineering over prompt engineering, infrastructure-based guardrails, rigorous evaluation practices, and the recognition that software engineering fundamentals—not frontier models—remain the primary predictor of success.
In this Neptune AI vs MLflow vs ZenML article, we explain the difference between the three platforms by comparing their features, integrations, and pricing.
ZenML's new pipeline deployments feature lets you use the same pipeline syntax to run both batch ML training jobs and deploy real-time AI agents or inference APIs, with seamless local-to-cloud deployment via a unified deployer stack component.
ZenML launches Pipeline Deployments, a new feature that transforms any ML pipeline or AI agent into a persistent, high-performance HTTP service with no cold starts and full observability.
ZenML's Pipeline Deployments transform pipelines into persistent HTTP services with warm state, instant rollbacks, and full observability—unifying real-time AI agents and classical ML models under one production-ready abstraction.
By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.