Exploring Tenant-Based Architecture in SaaS Platforms: For ML Model Training

Exploring Tenant Based Architecture in SaaS Platforms For ML Model Training
Earlier, software applications were developed as single tenancy pieces of code, which meant that each instance of the application was assigned to one user or organization. However, with the advent of SaaS, the need to serve multiple users or tenants from a single instance of the application became imperative. Multi-tenancy is the mechanism by which one software environment can be used to support multiple distinct users, residential in their own isolated data and configuration. Speaking of SaaS, it allows us to extend applications online as a service, but the multitenancy feature of the system enables us to develop and scale applications faster and vice versa by merging resources such as different users.

Understanding Tenant-Based Architecture

Multi-tenancy has several important parts that help it work well. These include sharing resources, keeping data separate, being able to grow easily, having one central place for managing everything, saving money, and letting users customize things.
  • Sharing Resources: Multi-tenancy means different users share things like computers, programs, and databases.
  • Data Isolation: Even though everyone shares resources, each user’s information is kept separate and safe from others. This is done using different techniques like using separate parts of a database.
  • Scalability: Multi-tenant setups can easily handle more users without needing big changes to how they work.
  • Central Management: These setups are managed from one central place, making it easier to update and fix things for all users at once.
  • Cost Effectiveness: By sharing resources, multi-tenancy can save money for both the provider and the users by making sure everything is used efficiently.
  • Customization: Users can often personalize parts of the application to fit their needs, even though they’re sharing resources with others.

Types of Tenant-based Architecture

Multi-tenant architecture plays a vital role in cloud computing by allowing efficient sharing of resources while ensuring tenant isolation and security. There are various types of multi-tenant architectures, each with its unique approach to resource sharing and data management. Understanding these options is essential for businesses and software developers to select the most suitable model for their specific requirements.

  1. Shared Database, Shared Schema explore-tenant-Inner-1In this model, all tenants share the same database and database schema. Key features include:
    • Resource Optimization: This model maximizes resource sharing, leading to efficient use of server and storage resources.
    • Uniformity: All tenants use the same database structure, simplifying maintenance and updates.
    • Data Isolation: Tenant-specific identifiers are used within the database to securely separate data for each tenant.
    • Cost-Effectiveness: Due to extensive sharing, this model is often the most cost-effective for providers. However, customization and scalability for individual tenants may be limited due to the tightly integrated database structure.
  2. Shared Database, Multiple Schemasexplore-tenant-Inner-2In this model, there is a single database instance, but each tenant has its own database schema. Key aspects include:
    • Moderate Isolation: Each tenant’s data and database structure are kept separate, offering better isolation than the shared schema model.
    • Customization: Tenants can customize their database schema to some extent.
    • Efficiency in Resource Use: While providing more isolation, this model maintains efficiency in resource utilization and cost.
    • Balanced Maintenance: It strikes a balance between resource sharing and meeting different tenant needs. This model is suitable for scenarios where tenants require some customization while benefiting from shared infrastructure efficiencies.
  3. Multiple Databases, Multiple Schemasexplore-tenant-Inner-3This model assigns each tenant its own database instance and schema. Key features include:
    • Maximum Isolation: Offering the highest level of data isolation and security.
    • Customization and Flexibility: Tenants have significant customization capabilities for data management and schema design.
    • Resource Allocation: Each tenant’s database can be scaled independently, providing greater control over resource allocation.
    • Higher Costs: This model tends to be more resource-intensive and expensive due to the need for additional infrastructure and maintenance efforts.

Benefits of Multi-Tenant SaaS

Here are some of the major benefits of Multi-Tenant SaaS:
  • Easy Customizations 

    Multi-tenancy-based applications allow you an additional layer of customizations by maintaining the original codebase which will be the same for all the users as well as new customers.

  • Saves Cost 

    Multi-tenancy will speed up the process of upgrades, which will also result in cost-saving. It is not necessary to configure a certain set of resources, and your environment may be quite basic depending on your architecture. As the application grows in size, there are more and more opportunities to save money. The vendor can pass on the savings to customers as a result of the decreased cost of doing business.

  • Easy Upgrades 

    With the help of Multi-tenant, the vendor does not need to update every instance of their software; they can just simply update a single, central application or codebase and make the changes ready for the users. It is not necessary to configure a certain set of resources, and your environment may be quite basic depending on your architecture. As the application grows in size, there are more and more opportunities to save money. The vendor can pass on the savings to customers as a result of the decreased cost of doing business.

What is Machine Learning?

Machine learning models are mathematical algorithms or computational techniques that are trained on data to make predictions or decisions without being explicitly programmed to perform the task. These models learn patterns and relationships from the input data and use that knowledge to make predictions or decisions on new, unseen data.

Applications of Machine Learning from Day-to-Day Life

AI, or artificial intelligence, is pervasive. It’s possible that you are utilizing it without even realizing it in some capacity. Machine Learning (ML) is a common application of artificial intelligence (AI), where computers, software, and other devices operate through cognition, which is extremely similar to the human brain.

Let’s see the applications of Machine Learning:
  • Product Recommendations

    If you purchase an item online or search about it then you will notice how the platform will give you recommendations for similar items. Also, if you purchased a product online some days back then you will get emails for shopping suggestions. This enhances the shopping experience, but do you know this is possible with the help of Machine Learning?

  • Virtual Personal Assistants

    Today, many of you may be aware of virtual personal assistants. Siri, Alexa, and Google Home are some of the most popular virtual assistants out there today. These virtual assistants can have a conversation with you and thus you can ask them anything you want. For example, you can ask them about today’s weather, flight details, etc. They can also be helpful for your work as you can ask them to design your schedule and what your schedule is today. Likewise, these virtual assistants can help in doing a lot of tasks.

    These personal assistants use machine learning, which is crucial to their functionality since it allows them to gather and hone knowledge based on your past interactions. This data set is then used to produce results that are customized based on your preferences.

  • Online Customer Support

    Nowadays, you will find the option to chat with a bot instead of humans on many websites. Websites use this bot as they can save time and cost for companies. These chatbots extract information from the website and then provide it to the customers. The chatbots are able to better comprehend customer inquiries and provide them with responses, which is made feasible by its machine learning algorithms.

  • Online Fraud Detection

    Today, online frauds are on a huge rise. But did you know machine learning can also aid in online fraud detection? One application of machine learning that is showing promise for making cyberspace safe is the monitoring of online financial fraud. For instance, PayPal uses machine learning to prevent money laundering. With the use of a suite of tools, the organization is able to compare millions of transactions and discern between transactions that are legal and those that are not between buyers and sellers.

  • Search Engine Result Refining

    Machine learning is used by Google and other search engines to enhance your search results. The backend algorithms monitor your response to the search results each time you run one. The search engine will infer that the results presented were relevant to your query if you open the top results and remain on the page for a considerable amount of time. In a similar vein, the search engine surmises that the results it returned did not meet its criteria if you go to the second or third page of the results but do not click on any of them. In this sense, the search results are enhanced by the backend algorithms.

When ML meets SaaS: MLaaS

In a Machine Learning as a Service (MLaaS) setup, a Software as a Service (SaaS) platform serves as the host, manager, and executor of machine learning models for individual customers, known as tenants. These tenants can access a range of services, such as data cleaning and preprocessing, training ML models, real-time prediction endpoints, batch processing for large datasets, data visualization tools, and various other ML-related services.

Advantages of Machine Learning as a Service (MLaaS)

Here are the advantages of Machine Learning as a Service:
  • Decreased Costs

    With MLaaS, you can do away with costly software, hardware, and maintenance costs as well as the requirement to hire and train data scientists and engineers. Customers can scale up or down based on usage, and they only pay for what they use.

  • Improved Efficiency

    Machine learning models can be scaled, updated, and deployed quickly and easily with MLaaS. They can also be integrated with current workflows and systems. The cloud’s processing power and storage capacity can also be used by users to manage big and complicated data collections.

  • Better Accuracy

    An advantage of machine learning as a service (MLaaS) is its ability to increase accuracy by utilizing the most recent developments in machine learning algorithms, data security, quality, and collective intelligence of the cloud to produce predictions that are more accurate and dependable.

Challenges in ML model training

When working with AI and ML, it’s crucial to think separately about training and inference. Training is about creating predictive models from data, while inference is using those models to make predictions in your application. Each of these tasks has its own needs.
In a setup where multiple users share resources (multitenant), it’s essential to understand how your approach affects both training and inference. By addressing these needs carefully, you can ensure that your solution gives accurate results, performs well, doesn’t cost too much, and can grow as your needs do.
  • Tenant Isolation

    It’s crucial to prevent tenants from accessing other tenants’ data or models without permission. Treat models with the same care as the raw data used to train them. Make sure tenants know how their data is used for training models and how models trained on others’ data might affect their work.

Approaches of Tenants-based Architecture in MLaaS

In multitenant setups, there are three main ways to handle ML models: using models specific to each tenant, sharing models among tenants, or fine-tuning shared models.
  • Tenant Specific Models

    Custom models are trained exclusively on data from one tenant and exclusively utilized for that tenant. These models are suitable in scenarios where tenant data is sensitive or when there is limited potential to glean insights from one tenant’s data and apply it to another. The accompanying diagram illustrates the architecture of employing tenant-specific models for two tenants:

  • Shared Models

    In setups employing shared models, all tenants conduct inference utilizing a common model. These shared models could be pre-existing models obtained either through acquisition or community sources. The provided diagram demonstrates how a single pretrained model can be leveraged for inference across all tenants:

    explore-tenant-Inner-5 We can also develop our own shared model based on trained data of all tenants. This can be depicted in the following figure. explore-tenant-Inner-6

    When training a model using data from multiple tenants, it’s essential to obtain clear consent from each tenant regarding the use of their data. Additionally, take measures to anonymize or remove any identifiable information from the data to protect tenant privacy.

    It’s also important to anticipate potential objections from tenants regarding the use of their data for training models applied to other tenants. Consider implementing procedures to address such objections, such as providing the option for tenants to exclude their data from the training dataset if they choose that. This ensures that tenant concerns regarding data usage are addressed and respected.

  • Tuned Shared Models

    In this hybrid approach involves training a base model on a shared dataset and fine-tuning it using tenant-specific data. It strikes a balance between resource efficiency and customization, offering tenants the flexibility to tailor models to their needs while leveraging shared resources. The provided diagram demonstrates how a single pretrained model can be leveraged for inference across all tenants:


Active ML Solutions in SaaS

Ever wondered how your favorite Software as a Service (SaaS) platforms seem to anticipate your needs and deliver tailored experiences? Behind the scenes, machine learning (ML) is quietly revolutionizing the way SaaS applications operate, bringing advanced intelligence and automation to the forefront.
Here’s how ML is seamlessly integrated into SaaS platforms to enhance functionality and user experiences:
  • Predictive Analytics: Picture this: you upload your sales data to your SaaS platform, and within moments, ML algorithms go to work, analyzing historical trends and patterns to predict future sales forecasts with remarkable accuracy. This predictive power empowers businesses to make informed decisions and stay ahead of the curve.
  • Personalization Services: Ever marveled at how an e-commerce platform seems to know your tastes perfectly? ML-powered personalization services analyze user behavior and preferences to deliver tailored recommendations, content, and product suggestions. It’s like having a personal shopper at your fingertips, enhancing engagement and driving conversions.
  • Natural Language Processing (NLP) APIs: Have you interacted with a chatbot that understands your queries seamlessly? NLP APIs, fueled by ML algorithms, enable SaaS platforms to interpret and respond to natural language inputs from users. This technology facilitates smooth communication and enhances user experiences across various applications.
  • Anomaly Detection and Security Services: Security is paramount in the digital age, and ML plays a crucial role in safeguarding SaaS platforms against threats. ML-driven anomaly detection services continuously monitor user activities and network traffic, identifying abnormal behavior and potential security breaches in real-time. It’s a proactive approach to cybersecurity that keeps data safe and secure.
  • Resource Optimization and Management: Efficiency is the name of the game in SaaS, and ML-driven resource optimization services ensure that resources are utilized optimally. By analyzing usage patterns and predicting future demand, ML algorithms dynamically allocate resources to maximize performance and minimize costs. It’s a win-win for businesses looking to streamline operations and improve ROI.
  • Content Recommendation Engines: Ever noticed how content platforms serve up exactly what you’re in the mood for? ML-powered recommendation engines analyze user preferences and interactions to deliver personalized content suggestions. Whether it’s articles, videos, or products, these engines enhance engagement and keep users coming back for more.
In other words, ML is the spice without which AI and the efficiency of modern SaaS tech would not have the secret sauce. Not only does SaaS capitalize on the data and algorithms that enable it to bring unprecedented value to its user base, but the growth, innovation, and competitiveness in the digital environment it propels is also just as unparalleled.


The ML model training architecture in the cloud software (SaaS) systems is a platform that opens up doors to both advantages and downsides for the developers. The right methods and practices the SaaS providers ought to invest in will allow them to benefit from machine learning while assuring that the privacy of residents is not compromised; further, the mass customer base they wish to please will not be forgotten. However, to keep up, alongside other competitors, one should keep learning how to develop ML models in scaling cloud types environments.
Vikas Agarwal is the Founder of GrowExx, a Digital Product Development Company specializing in Product Engineering, Data Engineering, Business Intelligence, Web and Mobile Applications. His expertise lies in Technology Innovation, Product Management, Building & nurturing strong and self-managed high-performing Agile teams.

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