You’re likely here because you’re trying to figure out how to deploy, monitor, and maintain your new LLM-powered application, agent, or product. You probably searched for “AI operations” hoping to find resources on running AI systems in production. Instead, you’re inundated with monitoring and observability vendors promising to “revolutionize your IT operations with AI!”

Welcome to the great naming crime of our time. “AIOps” is the great marketing misdirection that continues to haunt us all. On behalf of my industry marketing colleagues, I apologize. It certainly wasn’t my fault, but I feel your pain and (since I work as an industry vendor) feel some degree of responsibility by association.

Consider this blog my humble redemption attempt for that terribly illogical term hijacking. We’ll try to set the record straight: define AIOps vs. MLOps vs. LLMOps, and explain how to navigate your way out of confusion, and guide you into the right approach for your use case.

The Origin Story Nobody Asked For

Before we had Generative AI, we had “AIOps.” It was a simpler time, circa 2016-2017, when Gartner analysts were busy coining terms and monitoring vendors tried desperately to differentiate themselves in an increasingly commoditized market. In what can only be described as a profound moment of marketing hubris, someone decided that slapping “AI” in front of “Operations” would be the perfect way to rebrand their rule-based alerting systems and basic anomaly detection capabilities.

“AIOps” was born, not as a way to describe the practice of productionizing AI systems, but rather from vendors who needed a handy buzzword for debugging operations that mostly just used… quite frankly… fancy thresholding and correlation engines. As an industry, we used the term “AI” so loosely back then.

Fast forward to now. LLM-based applications and AI agents are coming out of development and landing in production. In modern times, we need to figure out current best practices for AI operations. Logically, no one could blame you if you reached for the term “AIOps.” But who has time for accurate labeling when there’s hype to be had?

What is AIOps vs. MLOps vs. LLMOps?

AIOps (Artificial Intelligence for IT Operations) applies machine learning and statistical techniques to IT and DevOps workflows. Its core purpose is to make sense of the massive volume of system telemetry (logs, metrics, and traces) generated by modern distributed systems. Instead of humans manually triaging alerts, AIOps systems cluster anomalies, surface root causes, and suggest remediations. Popular vendors include companies like Dynatrace, Datadog, Moogsoft, BigPanda, etc.

MLOps (Machine Learning Operations) focuses on the lifecycle management of machine learning models. It provides practices and tools to move from experimentation to production at scale. This includes versioning datasets, automating model training pipelines, deploying models, and monitoring model performance drift. The goal is to make ML reproducible, reliable, explainable, and continuously improvable. Platforms like MLflow, Kubeflow, and SageMaker operate here.

LLMOps (Large Language Model Operations) extends MLOps practices to the unique challenges of large language models. Fine-tuning, prompt engineering, embedding management, latency optimization, and cost monitoring become first-class concerns. Because LLMs rely heavily on retrieval, context management, and evaluation frameworks, LLMOps introduces additional observability layers not typically needed for smaller ML models. Tools like LangChain, Weights & Biases, or Arize operate in this space.

Some companies, like InsightFinder (yours truly), operate in all three of these spaces. While that could add a little confusion (AIOps would say this is the root cause of my guilt coming through), it also shows how these techniques can be complementary. To provide context for these definitions, let’s explore some use cases.

Use cases for AIOps, MLOps, and LLMOps

A common use case for AIOps is reducing mean-time-to-resolution (MTTR). Specifically, an AIOps solution can automatically correlate log spikes with network latency anomalies. Instead of waiting for multiple teams to manually correlate data, an AIOps system can flag the root cause in near real time and recommend a correct configuration fix. In this specific example, InsightFinder’s IT Observability platform fits the use case.

MLOps tools are often used to manage multiple recommendation models. For example, one can identify when shifts in product inventory patterns lead to a decline in recommendation accuracy. MLOps tools can continuously monitor and flag model drift. Combined with practices like automated retraining pipelines and data versioning, models can be aligned in real time to shift with changes in real-world behavior.

In LLMOps, a common use case is customer support automation using GPT-based models to answer incoming support tickets. By adopting LLMOps practices, you can iterate past costly deployments that are prone to hallucinations that damage user trust. LLMOps tools can help you evaluate prompt templates, monitor cost-per-response, and set guardrails for hallucination detection to reduce costs and improve customer experience.

InsightFinder’s AI Observability platform serves both MLOps and LLMOps use cases.

AIOps vs MLOps vs LLMOps: Overlaps and Shared DNA

Despite the differences, these approaches all share common operational DNA. All three emphasize automation, monitoring, and continuous feedback. All three seek to reduce human toil in high-volume, high-variance environments. AIOps, however, only targets surfacing issues that are visible from examining systems telemetry data. MLOps and LLMOps both target the performance of the underlying models in use.

Both MLOps and LLMOps can have a system-level focus (closer to AIOps) since both ML models and LLMs can be affected by underlying infrastructure components (when self-hosted). They both also often interact with live infrastructure (APIs, databases, real-time retrieval), meaning that both MLOps and LLMOps can be hybrid disciplines that are concerned with both model lifecycle and the resilience of the surrounding ecosystem.

ML models and LLMs have varying traits when it comes to deterministic or probabilistic operations. Because they behave differently, LLMOps is an extension of MLOps specifically for Large Language Models, addressing their unique needs like prompt engineering, handling large model sizes, and monitoring for issues like toxicity and hallucinations. While MLOps provides the foundation, LLMOps adds specialized practices for generative AI, focusing on context chaining, prompt design, and monitoring for outcomes like bias, relevance, and toxicity rather than just accuracy.

When to Use AIOps, MLOps, or LLMOps

Here’s a handy cheat-sheet:

• AIOps: Best for platform teams and SREs drowning in observability data. If alert fatigue or slow incident response is your bottleneck, AIOps is the right first step.
• MLOps: Essential when your team has multiple ML models in production. If retraining, versioning, or monitoring drift consumes bandwidth, MLOps practices bring discipline and scale.
  
• LLMOps: Relevant when deploying generative AI or LLM-driven applications, agents, or products to production. If latency, cost control, or hallucination monitoring are pressing issues, LLMOps frameworks help bridge the gap between prototyping and reliable deployment.

Pitfalls to Avoid in AIOps, MLOps, and LLMOps

Today, when you search for “AI Operations” you’re probably looking for something that isn’t the AIOps of the 2016 era. The great naming crime aside, here are a few typical missteps to watch out for.

AIOps pitfalls: Teams sometimes adopt vendor solutions that flood dashboards with alerts instead of reducing noise. AI-driven anomaly detection can either be too noisy or (even worse) not noisy enough. Often, over or under reporting issues leads to distrust that stifles adoption and these issues are typically caused by improperly labeled data sets or incomplete training models. Approaches like unsupervised behavior learning remove the need to manually label data in favor of learning that is autonomous, improving AI output automatically.

MLOps Pitfalls: Treating MLOps as just “DevOps for ML” misses the nuances of data versioning, experiment reproducibility, and bias monitoring. A common mistake is building pipelines for training and deployment but ignoring data lineage. When regulators or business teams ask why a model made a decision, tracing back inputs becomes critical for explainability.

LLMOps Pitfalls: This is a fast moving space and tooling is often still immature and fragmented. Relying on early-stage frameworks without clear evaluation metrics risks operational chaos. For example, using LLMs in production without hallucination or inappropriate tone detection introduces reputational risks. Running LLMs in production without cost monitoring can often lead to budget overruns. Teams should resist the urge to treat LLM prototypes as production-ready without proper guardrails in place.

Practitioner Recommendations: Choosing the Right Ops Strategy

If you’re a platform engineer or SRE, start by mapping your organization’s pain points to the right operational discipline. AIOps helps with system observability at scale. If your organization is mature in ML adoption, MLOps is critical for model lifecycle management. And if LLMs are already part of your roadmap, be prepared to extend MLOps into the specialized world of LLMOps for generative AI applications.

Perhaps the best advice is not to treat these as competing silos but as complementary lenses. AIOps can feed into MLOps by reducing infrastructure noise around ML pipelines. LLMOps can learn from both, borrowing monitoring practices from AIOps and lifecycle rigor from MLOps. The end goal is the same: to reduce toil, increase trust, and safely scale intelligence across systems.

The distinctions between AIOps, MLOps, and LLMOps matter less than aligning the right approach to the maturity of your systems and teams. Each discipline addresses a different dimension of complexity. Understanding the overlaps and the boundaries helps you avoid buzzword fatigue and invest in practices that actually deliver resilience.

See How InsightFinder Can Help

If you’re already wrestling with where to begin, InsightFinder can help unify these perspectives with AIOps in our IT observability platform, and MLOps + LLMOps in our AI observability platform.

Contact us to learn how we can use the right approach to tackle the biggest challenges for your team.