genai

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

This Langflow vs LangGraph article explains all the differences between these AI agentic systems.

Lessons from the Maven Evals course are combined with 50+ real-world case studies from ZenML's LLMOps Database to show how companies like Discord, GitHub, and Coursera implement the Three Gulfs model and Analyze-Measure-Improve lifecycle to transform failing LLM systems into production-ready applications.

In this LangGraph vs Autogen article, we explain the difference between these platforms and when to use which one for the best results.

287 latest curated summaries of LLMOps use cases in industry, from tech to healthcare to finance and more. This blog also highlights some of the trends observed across the case studies.

Discover the top 8 LangGraph alternatives for scalable agent orchestration.

Discover the new ZenML MCP Server that brings conversational AI to ML pipelines. Learn how this implementation of the Model Context Protocol allows natural language interaction with your infrastructure, enabling query capabilities, pipeline analytics, and run management through simple conversation. Explore current features, engineering decisions, and future roadmap for this timely addition to the rapidly evolving MCP ecosystem.

A comprehensive overview of lessons learned from the world's largest database of LLMOps case studies (457 entries as of January 2025), examining how companies implement and deploy LLMs in production. Through nine thematic blog posts covering everything from RAG implementations to security concerns, this article synthesizes key patterns and anti-patterns in production GenAI deployments, offering practical insights for technical teams building LLM-powered applications.

This comprehensive guide explores strategies for optimizing Large Language Model (LLM) deployments in production environments, focusing on maximizing performance while minimizing costs. Drawing from real-world examples and the LLMOps database, it examines three key areas: model selection and optimization techniques like knowledge distillation and quantization, inference optimization through caching and hardware acceleration, and cost optimization strategies including prompt engineering and self-hosting decisions. The article provides practical insights for technical professionals looking to balance the power of LLMs with operational efficiency.