“You can't make an omelette without breaking eggs,” MSH’s CEO Oz Rashid says about AI and its disruptors. That has certainly been the case when understanding MLOps vs. LLMOPs vs. AIOps. 

It  is so confusing when you are trying to figure out a platform to buy. Getting that picture right comes before any AI and ML engineer staffing decision, because each one requires a different type of role to run it.

Choosing one over the other is not a sign of  maturity or a product definition. In fact, large financial enterprises can have all three. JPMorgan Chase includes an LLM Suite, an ML Center of Excellence (MLCOE), and AIOps.

The trick to deciding what kind of AI ops support you need is to connect what problem/s your business is trying to solve and the AI technology you need to solve them. Then you need to have the right roles in the loop to build, ship, and scale your offering in production. 

Staffing for an LLMOps team when your use case and tooling require MLOps is an expensive mistake, Let's get to it, and go into more detail with an easy guide and a Three-Ops Decision Map.

Key Takeaways

• MLOps is a factory environment that builds and runs machine learning models in production.
• LLMOps is the concierge service for an application built on a foundation model like ChatGPT or Claude.
• AIOps is the IT administration center controllling workflows and functionalities using AI to be more efficient.
• All three differ in the roles needed to run the operations. An MLOps team has different staffing needs than the LLMOps or AIOps centers. 

MLOps vs. LLMOps vs. AIOps at a Glance

New concepts sort out fastest against the basic questions, what, why, who and how. MLOps vs. LLMOps vs. AIOps is no different. Our table below captures this information.

What: The discipline or framework

Why: Primary problem

Who: The people who use this methodology

How: Examples of the tools used

MLOps vs. LLMOps vs. AIOps Comparisons

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What Is MLOps?

MLOps is a methodology that takes an ML model from a business goal to a reliable, cost-controlled, monitored solution in production. It does so by automating ML model functions for continuous delivery and limits technical debt. Those processes cover six functions.

• Experimentation 
• Building
• Deploying
• Scaling
• Observation
• Retraining

Enterprises need MLOps roles and responsibilities to keep the production models running using MLOps tooling.

What Is LLMOps?

It is easy to get confused about what is LLMOps vs. MLOps. LLMOps is a subset of MLOps that handles the applications built on top of foundational ones, like ChatGPT or Claude. These customized models tie more directly into the business context, better connecting to the business strategy and use cases. LLMOps focuses on seven activities.

• Cost-savings
• Prompting
• Training (RAG and fine-tuning)
• Model evaluation
• Performance (inference cost and latency)
• Hallucinations and drift 
• Security

To run LLMOps, you need applied AI and ML scientists and AI and LLM engineers. These roles are highly demanded and are hard to find. 

What Is AIOps?

You need AIOps to support your IT team in its operations. Many IT services, from software development, security, and computer setup, touches an AI model. AIOps supports all that. The term, AIOps, was coined by Gartner in 2016. That workflow covers five functions, in IT.

• Anomaly detection
• Event correlation
• Incident response 
• Performance monitoring
• Security

This one functions separately. It  may exist simultaneously with MLOps or have existed earlier.

The role running AIOps vs. MLOps is the AIOps engineer, sits closer to platform engineering and site reliability than to retraining a ML model. Some IT departments piggyback AIOps on an existing role, such as an IT security specialist.

Which One Do You Need, The Three-Ops Decision Map

The choice between MLOps vs. LLMOps vs. AIOps is not a technology question, but a busine. The Three-Ops Decision Map below routes a problem to a discipline and names the first hire behind it.

The Three-Ops Decision Map

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Dot  IT operations run into bottlenecks, such as alert fatigue, incident volume or noisy telemetry?

• If it is IT, then choose AIOps.
• Or else go to the next bullet.

Are you supporting or creating an ML model in production for an internal customer?

• If so, then choose MLOps.
• Or else go to the next bullet.

Are you implementing generative AI or agentic AI?

• Make sure you have MLOps in place.
• If you have MLOps in place, go to the next bullet

Does your customized application connect to a foundational model like ChatGPT or Claude

• If so, then choose LLMOps.
• Otherwise go on to the next business problem.

This decision map is iterative. Most enterprises need more than one framework, depending on how and when their strategies evolve.A useful rule of thumb is to rerun the Three-Ops decision map after any significant business change and decide then whether to expand MLOps, LLMOps or AIOps capability.

The Common Thread, You Still Need People To Run It

A platform is only one component that identifies MLOps vs. LLMOps vs. AIOps. As Oz Rashid iterates, “The human stays in the loop.”

No system knows which ML model in the registry to update because your customer service team needs it. No application notices the surprise costs of token usage across the IT ecosystem. An AI solution, used by IT, isn’t going to comply with regulations when reporting a security breach to the CTO.  

You need people to do all that, to have the judgment and accountability to get the work done. A failure announces a missing role/s. A model drifts, an integration breaks, an agent returns confident nonsense and nobody owns the fix.

The business complains about using AI. That project then joins the more than 40% or more of agentic AI projects Gartner expects to be canceled by the end of 2027. The trick is to first hire the right roles, before the business logs multiple tickets. Figure out your AI and ML engineer staffing needs for your business use cases, and apply MLOps vs. LLMOps vs. AIOps concepts in action.

FAQ

What is the difference between MLOps, LLMOps, and AIOps?

The three differ in what they manage, who runs them and what problem they solve.

• MLOps is a factory environment that builds and runs machine learning models in production.
• LLMOps is the concierge service for an application built on a foundation model like ChatGPT or Claude.
• AIOps is the IT administration center controllling workflows and functionalities using AI to be more efficient.

Is AIOps the same as MLOps?

No. AIOps vs. MLOps solve different problems and require different roles. IT workflows and functionalities define what is AIOps. Producing reliable, monitored, and cost-effective ML models identifies what is MLOps. Also, AIOps and MLOps require different roles and responsibilities.

Is LLMOps just MLOps for LLMs?

No. Although LLMOps overlaps with MLOps, it does much more. LLMOps supports models developed in-house that are connected to foundational models, like ChatGPT.  These integrations require additional services to handle the LLMs and reproduce its behavior.

• Cost-savings
• Prompting
• Training (RAG and fine-tuning)
• Model evaluation
• Performance (inference cost and latency)
• Hallucinations and drift 
• Security

Which do you need, MLOps, LLMOps, or AIOps?

It all depends on your business strategy and which problems are a priority.  Some enterprises, like large banks, have all three teams. Organizations which are less dependent on Gen AI technologies, may have one. In some cases, as in AIOps, an employee may take on responsibilities for those operations, in addition to their existing duties.

What is the difference between MLOps and DevOps?

MLOps vs. DevOps differs in what they deliver to the wnterpirses. MLOps automates delivery of reproducible, innovative, and governed models in production. It not ensures CI/CD and continuous training (CT) to meet business demands..

DevOps, a part of IT, ensures developers can release their work into and maintain the code in the production environment. Software development functions in a less-complex ecosystem, without the hidden debt in machine learning, that ML models, in production, have. So the MLOps and DevOps skillsets are very different and impact who you hire.

Which came first?

AIOps came first, coined by Gartner in 2016 to describe algorithmic IT operations. MLOps followed as machine learning moved from research into production systems and needed automation. LLMOps is the newest requirements. By 2024, many companies were regularly using generative AI to get business value.

Get In Touch

MLOps vs. LLMOps vs. AIOps is not a contest between three rivals. Organizations need the frameworks at different times for different reasons.

Each provides a unique framework that provides business value, such as more efficient IT processes, traceable ML models, or smoother interactions with the LLM vendors. 

Now that we have the concepts down, the hardest part is to apply your MLOps vs. LLMOps vs. AIOps knowledge.Its a matter of getting the right  professionals to run the tools and communicate with users. Being proactive in hiring your AI and ML engineering staff limits unexpected surprises that business people hate.