LLMOps

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.

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.

Explore 419 new real-world LLMOps case studies from the ZenML database, now totaling 1,182 production implementations—from multi-agent systems to RAG.

In this article, you learn about the best Promptfoo alternatives that help you ship better AI agents.

Discover the 9 best prompt monitoring tools for ML and AI engineering teams.

Discover the 10 best LLM monitoring tools you can use this year.

In this article, you will learn about the best DeepEval alternatives that you can use for LLM evaluation.

In this Langfuse vs Phoenix guide, we conclude which open-source framework fits your LLMs stack by comparing features, integration, and pricing.

In this article, you learn about the best Langfuse alternatives for tracing, eval, prompt management, and metrics for LLM apps.