DevOps vs MLOps vs LLMOps: Differences, Similarities, and Use Cases

DevOps empowers automation, MLOps empowers intelligence, and LLMOps empowers the rise of large-scale AI: All are powering modern software engineering.

And today, LLMOps takes it even further – allowing organizations to deploy, monitor, and continuously refine large language models that are capable of thinking and responding like humans.

As per Fortune Business Insights' 2026 study, the Asia Pacific region is expected to witness the highest growth rate due to increasing DevOps adoption among SMEs and rapid cloud adoption in the world.

Together, DevOps, MLOps, and LLMOps create an integrated operational ecosystem that connects software engineering, AI development, and intelligent automation in a way that has never been done before. Let’s explore how.

What is DevOps?

DevOps is a culture, methodology, and toolset bringing together software development and IT operations. Its objective is to enable quick processes, minimal faults, and enhanced collaboration of development teams. The developers and support staff are not separated. They simply automate the building, testing, and release process. Did you know that the average salary of a DevOps Engineer in the US is $122,950 /year?

Key characteristics of DevOps:

• Seamlessly integrate development and operations teams.
• Fast and reliable code delivery via CI/CD pipeline automation.
• Monitoring, feedback, and improvements.
• Leverage tools like Jenkins, Docker, Kubernetes, Git, and Terraform.
• Deeply focuses on various software development metrics such as frequency of deployment, failure rate, and time of recovery.

Now you might be thinking, Why do we need MLOps?

While traditional DevOps pipelines are great for software, they aren’t ideal for ML projects. An ML system includes data collection, model training, deployment, and continuous monitoring. As data drifts, models degrade and require governance and retraining – something you can’t achieve with classical DevOps. This is where MLOps steps in because it has all the features of DevOps and has the ability to manage machine-learning workflows.

7 Phases of DevOps Lifecycle

DevOps is a loop of continuous feedback that encompasses seven stages. They are as follows:

• Continuous Development: Iteratively plan, code, and build the product’s features.
• Continuous Integration: Code, implement, and test the product frequently to find bugs.
• Continuous Testing: Validate each integration automatically through rigorous testing.
• Continuous Monitoring: Track the performance and detect issues early on.
• Continuous Feedback: User and operational insights for improvement.
• Continuous Deployment: Automated delivery to production with reliability.
• Continuous Operations: Maintain uptime, security, and performance at production.

These phases make DevOps the lifeline of software engineering in modern times.

What is MLOps?

MLOps, or Machine Learning Operations, expands DevOps to machine-learning systems. It helps manage data pipelines, experiment tracking, model training, versioning, deployment, and monitoring. MLOps is all of DevOps, but specifically applied to machine learning. The MLOps Engineer's average salary is $87,220 /year in the US. (ZipRecruiter)

Key characteristics of MLOps:

• Strengthen collaboration between data scientists, ML engineers, and operations teams.
• Automate data collection, training, and deployment flows.
• Support continuous training and versioning.
• Detect and remedy model drift automatically.
• Enable Machine Learning model scaling and compliance.

According to the Global Market Insights, the market size of MLOps is projected to grow to USD 39 billion by 2034.

5 Phases of MLOps Lifecycle

The MLOps lifecycle involves the following stages:

• Data Collection and Preparation: Acquire, clean, and shape data for training
• Model Development and Experimentation: Choose and run algorithms, tune hyperparameters, and benchmark
• Model Training and Validation: Run various experiments to achieve the best performance metrics
• Model Deployment and Monitoring: Through the CI/CD pipelines, push the model into production.
• Model Governance and Retraining: Track, reproduce, and retrain models as new data emerges (data evolution).

All of these phases combine to close the gap between experimentation and actual deployment. This is the foundation of any contemporary machine-learning project.

Are MLOps and DevOps the Same?

Assume that they are all part of the same DNA, such as automation, continuous improvement, and collaboration.

However, they also have some similarities. Let’s explore the similarities between MLOps and DevOps:

• Continuous integration and deployment.
• Collaboration between technical teams
• Monitoring, alerting, and feedback components
• CI/CD pipelines and containerization tools to be used.
• Improving delivery rate and reliability.

While each facilitates more rapid and safer innovation, one does the same for engineering operations, while the other does the same for Machine Learning projects.

Differences Between DevOps and MLOps

These are the major differences between DevOps and MLOps in 2026.

Applications of DevOps and MLOps in 2026

DevOps Applications

• Fast software updates: DevOps allows software engineering to release new features and applications quickly through automated pipelines.
• Cloud systems management: DevOps has been embraced by many companies to manage apps across cloud platforms in a manner that is both safe and effective.
• Smart monitoring: With the help of AI-powered DevOps tools, system problems are identified early. As a result, problems are fixed before the users notice them.
• Fintech systems: DevOps plays a role in updating banking and payment apps to be secure, quick, and reliable.
• Built-in security: DevOps engineers use DevSecOps tools to check if regulations and security requirements are met before every software release.

MLOps Applications

• E-commerce recommendations: Because Machine Learning projects deployed with MLOps can be used in real-time to power product suggestions for E-commerce platforms.
• Healthcare predictions: MLOps allow ML engineers to create accurate AI models with which doctors can detect diseases earlier than before.
• Fraud detection: Finance teams can use MLOps tools to train models to learn to identify fake transactions that go faster than humans can across the flag.
• AI chatbots and NLP apps: Using MLOps, for instance, can help us train and place Chatbots by training natural language processing applications to help people be smarter within minutes.
• Edge AI systems: Executives of AI running on small devices like phones or sensors can update AI models with MLOps and interpret data faster, resulting in data-driven decisions.

What Is LLMOps?

LLMOps or Large Language Model Operations frames the operational side of LLMS applications like ChatGPT, Gemini, or Claude. Among others, it deploys, fine-tunes, scales, and monitors foundation models with hallucinations, bias, prompt lengths, and inference costs. Here are LLMops characteristics:

• It governs the LLM-based system life cycle.
• It tracks prompt-response pairs and context accuracy.
• It enables RAG and other hybrid models.
• It focuses on generation quality, latency, and cost monitoring.
• It uses vector databases, prompt management frameworks, and orchestration tools.

How LLMOps Differs from DevOps and MLOps?

Distinctive aspects of LLMOps:

• Works on foundation models.
• Necessary acceleration and context-management frameworks.
• Output monitoring: hallucinations, bias, and toxicity.
• Increased proficiency in cost optimization for high-compute inference.
• Retrieval pipelines and prompt version control.
• Significant governance-aware generative outputs and user feedback.

The Bottom Line

Beyond 2026, the triad of DevOps, MLOps, and LLMOps is what makes intelligent software systems possible. Acquiring these skills in DevOps, MLOps, and LLMOps will help you become successful in the software development career.

If you are confused about which path to follow to gain skills in them, you can check out the Top AI ML Certifications, which are training professionals not only the AI and ML skills but also in how to work with the modern tools, cultural philosophies, and practices such as DevOps.