AI adaptation is really fast and we are also moving fast. With this the operations around deploying and managing models are evolving every hour. In that, MLOps became the standard for ML workflows but, with the rise of large language models (LLMs), a new category emerged that is LLMOps.

If you are building or managing AI systems in 2025 then you need to understand both.

Our guide breaks down the differences between LLMOps vs MLOps, like where they overlap, where they differ, what tools does each power, and how to decide what you need, especially if you are considering MLOps as a career.

What is MLOps?

MLOps stands for Machine Learning Operations. It is the process of automating the ML lifecycle that is from model training and deployment to monitoring and retraining.

Core components of MLOps include:

  • Version control for data and code
  • Automated training pipelines
  • Model deployment to production
  • Continuous monitoring and feedback loops
  • Model governance and compliance

MLOps helps teams build scalable, reliable ML systems that don’t just work in notebooks but thrive in real-world environments.

What is LLMOps?

LLMOps is the specialized subset of MLOps for managing large language models like GPT, Claude, and LLaMA.

These models are:

  • Massive (billions of parameters)
  • Dynamic (often updated with new prompts or fine-tuning)
  • Resource-intensive (costly to train and serve)

LLMOps focuses on:

  • Prompt engineering workflows
  • Retrieval-Augmented Generation (RAG) systems
  • Fine-tuning and parameter-efficient training (PEFT)
  • Guardrails and moderation layers
  • Latency and cost optimization during inference

Key Differences Between LLMOps and MLOps

Feature MLOps LLMOps
Model size Varies (small to mid) Massive (billions of parameters)
Data requirements Structured & labeled Unstructured (text-heavy)
Training frequency Frequent retraining Infrequent full training; PEFT instead
Deployment complexity Medium High (due to scale & latency)
Monitoring focus Accuracy, drift, model decay Prompt effectiveness, toxicity, bias
Tooling MLFlow, Kubeflow, Seldon LangChain, LlamaIndex, Weights & Biases

Where Do LLMOps and MLOps Overlap?

They share core principles:

  • Automating the model lifecycle
  • Monitoring and improving model performance
  • Versioning and reproducibility
  • Governance and security practices

But their execution differs especially in inference, observability, and feedback systems.

Why LLMOps Matters in 2025

Large language models are now powering chatbots, agents, copilots, and enterprise search systems. Without LLMOps, these systems become:

  • Costly to maintain
  • Risky (hallucinations, bias)
  • Hard to scale

LLMOps introduces:

  • Tooling to test prompts before going live
  • APIs to control model behavior
  • Observability dashboards for user interactions

Example Workflows: MLOps vs LLMOps

MLOps Workflow:

  1. Data collection & labeling
  2. Model development
  3. CI/CD pipeline for ML
  4. Deployment via REST or batch
  5. Monitoring accuracy, latency, drift

LLMOps Workflow:

  1. Data ingestion (docs, text, PDFs)
  2. Indexing (RAG or vector DBs)
  3. Prompt design and testing
  4. Model orchestration (with LangChain, etc.)
  5. Real-time feedback, guardrails, retraining

Popular Tools in LLMOps vs MLOps

MLOps Tools:

  • MLflow
  • Kubeflow
  • Vertex AI
  • Seldon
  • Weights & Biases

LLMOps Tools:

  • LangChain
  • LlamaIndex
  • PromptLayer
  • Arize AI
  • OpenAI Eval

Career Opportunities: MLOps vs LLMOps Roles

Role Focus Area
MLOps Engineer Model CI/CD, infrastructure
LLMOps Engineer Prompt optimization, fine-tuning, latency
AI Platform Engineer Tooling for both MLOps & LLMOps
DataOps Engineer Pipeline and data management
AI QA Specialist Prompt testing, bias/toxicity detection

How to Choose Between MLOps and LLMOps

Choose MLOps if:

  • You are deploying traditional models (classification, regression, clustering)
  • Your team values structured training data
  • You need scalable infrastructure for retraining

Choose LLMOps if:

  • You are working on chatbots, search assistants, or agentic AI
  • Your models rely heavily on prompts and documents
  • Your challenges include hallucination, latency, or sensitive topics

Real-World MLOps and LLMOPs Case Studies

  • MLOps Example: A fintech firm uses MLOps to retrain credit scoring models weekly with fresh data.
  • LLMOps Example: An edtech company builds an LLM-powered tutor, using LLMOps to filter harmful content and improve answer relevance.

Conclusion: Where the Future Is Headed

The world isn’t choosing between LLMOps and MLOps, it is blending them.

Modern AI stacks combine both foundational models deployed using LLMOps, supported by structured model workflows managed via MLOps.

If you want to future proof your career then learn MLOps now, and build your pathway into LLMOps.

AgileFever’s MLOps Bootcamp

If you are up for it, then start with our MLOps BootCamp here.

Get hands-on with:

  • Building ML pipelines from scratch
  • Deploying models with CI/CD
  • Managing experiments and reproducibility
  • Working on real-world ML & GenAI projects

Our Bootcamp gives you job-ready MLOps + a head start into LLMOps.

FAQs

What is the difference between LLMOps and MLOps?

MLOps handles traditional ML workflows like training and deployment. LLMOps deals with LLM-specific challenges like prompt testing, RAG, and fine-tuning.

Is LLMOps a part of MLOps?

Yes. LLMOps is considered an extension of MLOps tailored for large language models.

Do I need to know MLOps before learning LLMOps?

Yes. MLOps provides the foundation in ML lifecycle management, which is essential before diving into LLMOps.

Which has better job opportunities, MLOps or LLMOps?

Both are in demand, but LLMOps is trending in 2025 due to rapid LLM adoption in enterprises.

What are the top tools used in LLMOps?

LangChain, LlamaIndex, PromptLayer, Arize AI, and OpenAI Eval are leading the LLMOps ecosystem.

Is there a certification for LLMOps?

There’s no global certification yet, but hands-on bootcamps like AgileFever’s MLOps Bootcamp offer real-world LLMOps (Upcoming modules soon) training 

Can MLOps and LLMOps be used together?

Yes. Many companies integrate both to support full-stack AI operations—traditional models and language-based systems.

Are LLMOps jobs high-paying?

Yes. Due to the niche skill set and limited talent pool, LLMOps engineers are currently commanding premium salaries.

How do I start a career in LLMOps?

Begin with MLOps fundamentals. Learn tools like LangChain, vector databases, and prompt tuning. Bootcamps are a fast-track option.