Eseandre Mordi
⬤ A growing discussion around how artificial intelligence systems are deployed in production has highlighted the limitations of applying traditional DevOps practices to large language model applications. Many teams continue to rely on DevOps workflows despite evidence that these methods are poorly suited for AI-driven systems. Data shows that 88% of machine learning initiatives struggled to reach production when using standard DevOps approaches, pointing to a clear mismatch between operational tooling and AI workloads.
⬤ DevOps is fundamentally software-centric, focusing on writing code, testing it, and deploying it through predictable pipelines. The primary artifact is code, feedback loops are straightforward, and testing outcomes are binary—it either works or it doesn't. MLOps expands this scope to include data and models alongside code. Even when software logic stays the same, model performance can deteriorate over time due to data drift or shifting real-world behavior. This makes continuous monitoring, retraining, and versioning of data, features, and models essential parts of MLOps workflows.
⬤ LLMOps introduces a completely different operational approach centered on foundation models rather than custom-trained systems. Instead of a linear pipeline, optimization happens simultaneously across prompt engineering, retrieval-augmented generation setups, and fine-tuning. Monitoring requirements shift dramatically. Rather than focusing mainly on accuracy or drift, LLMOps emphasizes hallucination detection, bias and toxicity controls, token usage, cost management, and human feedback loops. Since LLM outputs are non-deterministic, evaluating quality means assessing safety, grounding, and efficiency rather than just correctness. Production data reveals that 63% of AI systems experience dangerous hallucinations within their first 90 days.
⬤ These differences matter because cost structures and feedback loops vary sharply across DevOps, MLOps, and LLMOps. MLOps costs are heavily weighted toward training, while LLMOps costs are dominated by inference and token consumption. This makes prompt efficiency, caching, and routing critical operational concerns. Understanding these distinctions explains why newer AI systems require purpose-built operational frameworks rather than retrofitted versions of existing software or machine learning pipelines.
Eseandre Mordi
