MLOps refers to the process of automating machine learning systems to enhance various operations. Here, we’ll discuss the various factors to consider when choosing between cloud vs on-prem MLOps to automate the AI and ML systems in your business.
Artificial intelligence and machine learning capabilities are no longer optional in today’s world. Every business, be it a startup or a multinational enterprise, has to optimize and streamline its processes to reduce expenses and increase returns. Machine learning models help achieve this by automating recurring activities, workflows, and systems in the organization.
MLOps are used to simplify machine learning systems in an enterprise. Instead of manually managing and tracking various ML processes, you can automate everything using MLOps. According to Global Market Insights, the global MLOps market size was $1.7 billion in 2024 and is expected to grow at a CAGR (compound annual growth rate) of 37.4% to reach $39 billion by 2034.
However, an important question arises here. Should you invest in cloud-based MLOps or build on-prem MLOps? What do the latest benchmarks say about cloud vs on-prem MLOps?
Let’s find out in this blog.
What is MLOps?
MLOps is an acronym for Machine Learning Operations, referring to a set of practices that combine artificial intelligence, machine learning, and operational processes.
Simply put, it streamlines and automates how you develop, deploy, and maintain the machine learning systems, models, etc. Every stage of the entire process (from development to maintenance) can be monitored by MLOps to ensure seamless operations and results.
Most enterprises hire MLOps developers to handle the process as it is complex and involves a large number of steps. It requires specific skills, domain expertise, and knowledge in various technologies like AI, ML, cloud computing, data engineering, etc.
That’s because data preparation is the first step to begin MLOps. Every other stage hinges on how well you collect, store, and clean the data to use it for training the ML algorithm and deriving insights or generating the desired outcomes.
Given the extensive nature of MLOps, businesses prefer to opt for cloud-based solutions as it is more convenient and cost-effective. At the same time, some organizations have built on-prem MLOps systems and find them more effective.
Both methods have their pros and cons. That said, the ML lifecycle management in cloud vs local (on-prem) has basic differences that can affect your revenue and outcomes. Talk to an MLOps service provider to identify the best option for your business requirements.
On-Premises MLOps Advantages and Disadvantages
On-premises MLOps is where the entire infrastructure required to manage the machine learning models is built within the enterprise. This requires various hardware and software systems that are compatible with the MLOps platforms. Legacy systems have to be modernized, security layers should be added, and the entire setup has to be optimized to consume fewer resources.
So, how can you benefit from on-prem MLOps?
The major difference between cloud vs on-prem MLOps is data sovereignty. Your data is stored in physical databases in your organization. This allows you to comply with the local/ national data regulations and ensure it is entirely secure from unauthorized access.
In industries that collect and store sensitive data, such as finance, healthcare, insurance, eCommerce, etc., on-prem MLOps allows you to enhance the security layers and minimize the risk of data breaches. Additionally, you can partner with a data warehousing company to create a robust centralized repository to store all business data in a single location and access it from anywhere in the enterprise.
Another advantage of on-prem MLOps is the ease of optimization by tailoring the hardware and software configuration to suit your requirements. For example, real-time apps with fast processing speeds can use networks specially designed for the purpose.
This prevents overloading the same network and reduces latency. Furthermore, on-prem MLOps can be more efficient than shared cloud-based MLOps as they are exactly customized to align with your needs.This invariably helps meet the enterprise MLOps platform benchmarks and increase the standards.
However, on-prem MLOps adoption comes with its share of challenges. Firstly, building the infrastructure for it is highly expensive, as you have to invest in the latest hardware that is compatible with the software technologies. Then MLOps scalability benchmarks become a concern. After all, scaling a physical IT infrastructure is a time-consuming and cost-intensive process.
You cannot simply pay for extra space the way it is possible with cloud-based solutions. More challenges arise when you have to collaborate with others. Physical systems are usually isolated and not built for collaboration. Finally, all resources for the on-prem MLOps will be yours. You have to allocate the required resources to keep the entire system running smoothly.
Cloud MLOps Advantages and Disadvantages
As per statistics, the global cloud computing market is expected to surpass $1 trillion by 2028. This indicates how quickly businesses around the globe are migrating to cloud solutions instead of relying on on-prem systems. That’s because the performance of cloud ML platforms is greater, especially when you choose a private or hybrid cloud and don’t share the resources with other businesses.
Vendors optimize the MLOps platforms to reduce resource consumption and cut down unwanted costs. Though it requires an initial investment like on-prem MLOps, the amount you spend is much less. Also, the ROI is greater and faster since cloud-based vendors offer flexible subscription models. The MLOps deployment cost analysis clearly shows that cloud solutions are cost-effective as well as scalable, agile, and future-ready.
They make it easier for business teams to collaborate with remote workers and with teams from other B2B companies. Additionally, you don’t have to build everything from scratch. You can use existing SaaS (Software as a Service) and PaaS (Platform as a Service) solutions and integrate them with the MLOps tool to automate the process. This increases the time to market and gives you a competitive edge.
That said, data security could become a challenge unless you have a comprehensive data governance and compliance strategy for data backup and security, along with measures to adhere to the data privacy regulations to avoid lawsuits. For this, you can hire data engineering consulting services from experienced companies and implement the strategic blueprint for building and maintaining a cloud-based MLOps.
Data encryption, remote backup, secure networks for data transfer, etc., are some ways to enhance the security layers when using cloud solutions. So, while security incloud vs on-prem AI and MLOps can be a concern if you use shared and public cloud, the advantages outweigh the challenges.
Cloud vs On-Prem MLOps Comparison Table
| Cloud MLOps | On-Prem MLOps | |
| Control | Basic control over tools and solutions | More control over the tools and process |
| Scalability | More options to scale, and easier to upgrade and downgrade | Limited options; hard to scale frequently; more suitable for predictable and steady requirements |
| Complexity | Less complex as everything is hosted on cloud servers and uses SaaS solutions | More complex since everything has to be on-premises |
| Performance | Depends on the type of cloud chosen – greater performance for private/ hybrid cloud | High performance, as the entire system is in-house and runs on the business’s resources |
| Security | Varies based on the security tools; private and hybrid clouds have greater security | High security with everything belonging to the business and hosted on-prem |
| Cost | Cost-effective; can pick and choose vendors for more savings | Expensive and requires regular investment for maintenance; upgrades are also costly |
Hybrid MLOps Strategies 2025
Most of the time, it is not straightforward to choose between cloud vs on-prem MLOps, as each model has its pros and cons. In such instances, you can go for hybrid MLOps strategies that enhance the advantages of both models and reduce the risks/ challenges.
This is achieved by setting up a portion of the infrastructure on-premises and hosting the rest of it on cloud platforms. For example, you can build the data pipelines on-premises to keep them secure and integrate them with a cloud-based data warehouse or a data lake and other AI/ ML tools that are a part of the cloud ecosystem.
Many enterprises these days are hiring MLOps consulting services to get tailored solutions for hybrid MLOps to reduce costs and increase efficiency without compromising quality, security, and scalability. Moreover, you can benefit from priority maintenance and support services offered by MLOps and data engineering companies.
Latest MLOps Tools for 2025
MLOps ecosystem offers open-source and closed-source solutions, making it easy for businesses to choose the type of platform they need. Open-source MLOps platforms are more in demand for the flexibility they offer. They can be customized to align with your demands and deliver the results you expect.
The following are a few popular MLOps platforms in the market:
- DataRobot MLOps
- Metaflow
- Databricks
- Kubeflow
- TrueFoundry
- Modelbit
- Domino Enterprise MLOps Platform
- Vertex AI (Google Cloud), etc.
Conclusion
MLOps brings DevOps principles to machine learning by automating the entire ML lifecycle. It accelerates ML model deployment, increases accuracy, enhances business value, and future-proofs the processes.
Achieve your objectives and increase ROI by partnering with a reputable data engineering company with experience in your industry. Hire end-to-end solutions and choose between cloud vs on-prem MLOps solutions based on your specific requirements.
Schedule a meeting with our experts to learn more.
More in MLOps Services Providers
MLOps services include a range of processes like creating ML pipelines, deploying ML models, and integrating them with third-party systems, setting up a continuous delivery mechanism, and continuous monitoring to create a streamlined workflow.
It also includes governance and compliance, as well as data engineering services, to create a robust, flexible, and scalable solution for the organization. All this revamps the business processes to reduce costs and increase efficiency.
Check out the links below for more information about MLOps.
- Best MLOps Companies in the USA – Top 10 for ML Engineering
- Why Your ML Model Deployment is Failing (and How to Fix It?)
FAQs
How do I choose between cloud and on-prem for MLOps?
You can choose between cloud vs on-prem MLOps by considering the following factors:
- Business objectives
- Future requirements
- Existing IT infrastructure
- Budget and ROI expectations
- Talent gap
Cloud and on-prem MLOps can also be combined to create a hybrid model. At DataToBiz, we offer tailored end-to-end solutions to implement the right MLOps processes for your business.
Will moving to cloud MLOps cut my operational costs?
Yes, moving to cloud MLOps can reduce operational costs if the entire system is optimized. It is all about balancing quality, flexibility, and scalability with cost-effectiveness. DataToBiz has certified professionals who know how to reduce operational costs and overheads for businesses through a customized MLOps process.
Can I run a hybrid MLOps setup for better flexibility?
Yes, you can absolutely run hybrid MLOps for better flexibility. In fact, many MLOps consulting services recommend some form of hybrid solution to give you the best of both worlds. Even at DataToBiz, we provide cloud, on-prem, and hybrid MLOps services to align them with your business systems and requirements. We future-proof your processes and make them more flexible.
How do I measure performance differences between cloud and on-prem MLOps?
You can measure the performance of cloud ML platforms against on-prem solutions by selecting KPIs (key performance indicators) for tracking the process. These include deployment frequency, costs, lead times, failure rates, adoption rate, user satisfaction, etc. Other aspects, like flexibility and scalability, should also be considered.
What security trade-offs should I consider in cloud vs on-prem MLOps?
Security in cloud vs on-prem AI and MLOps platforms is a major factor. On one hand, on-prem offers more control and greater security than a public cloud solution, as everything is physically hosted in the enterprise. However, cloud MLOps can also offer similar security, especially if you select a vendor known for following strict industry standards. Moreover, most cloud solutions can be integrated with third-party security systems.
How will my team’s workload change if we switch to cloud MLOps?
Switching to cloud MLOps will bring quite a few changes to your team’s working process. Recurring tasks will be automated, but they are likely to have new responsibilities as they have to work with different tools and interfaces. This requires a learning curve. However, once they get used to the new processes, the workload will reduce significantly.
Fact checked by –
Akansha Rani ~ Content Management Executive
