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What is Multi-Tenant architecture?
Fluid AI offers an architecture to the organisations where a single software Instance can serves multiple department or use-cases (Tenants) within organsiation. Underlying resources are shared, but data and operations are isolated.
Imagine an apartments in building: each apartment is a tenant with its own space and privacy, but they all share the same building structure and utilities (Instance).
Fluid AI enables you to deliver personalized experiences to prospective tenants.
For more security & compartmentalizing data access within your organisation among the users with Multitenancy
The primary concern for GPT users revolves around data security.
At Fluid AI, dedicated instance is provided to the customers & offer them to create various multiple Tenants for different departments or use cases under their one dedicated instance, this ensures only required data access to the users. This helps organisation to create Role-based Knowledge access framewok, so organisation now dont have to worry about data security or data exchange between different departments, and streamline information exchange across various teams, such as Sales, Marketing, Operations, IT, and Customer Support, fostering unique tenants for each.
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Knowledge Management for your Multi-tenants
Each Tenants have seperate Knowledge- base, where data added to a particular tenant would be restricted & accessible to that tenant only. Tenants interact and retrive data from their respective Knowledgebase.
Users can further organize their Knowledge Bases by creating folders for distinct document types or business areas such as product lines and services. Authoritative Users have the flexibility to simply navigate among Tenants & create different Knowledge-base for their different departments.
For Example:
- A Tenant tailored for the sales department would consist data related to marketing, sales, product/services information, customer data, CRM insights, etc.
- A Tenant designed for the operations team would contain standard operating procedures (SOPs), manuals, emergency situation guidelines, and operational directives, etc
- A Tenant for the HR department would store resumes, employee SOPs, policy documents, directives, contractual agreements, etc.
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User Management for Role based access to the Multi-Tenant platform
Only Authorised user will be accessible to switch/browse between Tenants & build the Knowledge-base, add/delete data and create folders.
Users access permissions are set by authoritative users to ensure data security. Different users can have access to one or multiple tenats, provided to them by the authorized user
Only Authorised users can create/delete users, invite users to join the platform, assign role based access & edit their access to different tenants & folders in the tenants. This robust user management system ensures controlled access to the platform and its resources.
Users with administrative privileges are granted access to exhaustive reports on usage metrics, such as tokens consumed, feedbacks inights, evaluation results, & more. This Automatic Performance Evaluation Screen helps to improve EX/CX from data analysis, enabling organizations to evaluate performance and make informed decisions, adjusting or scaling plans accordingly.
Chat with your data, using 150+ natural language queries
Users can interact with the system with 150+ languages supported, asking their queries, in natural human language and receive resolution to their query back in human-like response , with the power of advanced Generative AI & NLP technology. The chatbot is designed to facilitate a dynamic exchange of conversation, Users can experience seamless, free-flow Conversational and Contextually aware interactions asking qts back & forth, without being limited to preset questions.
The chatbot's also ensures efficient and targeted search capabilities, enabling users to find the information they need easy & quick
Tailored Search Capability:
- Context-Aware Database Search- It is adept at performing searches across databases, folders, and files within a specific tenant, ensuring relevant outcomes.
- Focused Inquiries- Users have the option to direct questions to a specific folder, prompting the chatbot to restrict its search to that selected folder.
- Document-Specific Queries- The chatbot can also concentrate on a single document, providing answers exclusively from that source to meet the user's inquiry and receive relevant information directly.
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Advantage of Multi-Tenant Architecture
- Isolating Data: features for isolating data and outputs between different tenants. This is crucial to maintain data privacy and security.
- Control and access: Multi-tenancy introduces additional security concerns. The system must be designed to prevent tenants from accessing each other's data or interfering with each other's workloads.
- Containerization: This method isolates each tenant's data and workload within a separate container, ensuring complete separation.
- Scalability: Popular options for multi-tenant deployments due to their inherent scalability and security features. The architecture should adapt to accommodate growth in tenants and data:
- Cloud-based infrastructure: Leverage the scalability of cloud platforms like AWS or Azure. simplify multi-tenant deployments, secure, cost-effective, minimal upfront investment
- On-Premise Infrastructure: Maximum control over infrastructure and data. Ideal for strict control over data and infrastructure. Suitable for organizations with existing on-premise infrastructure. Requires significant investment or data privacy concerns that necessitate keeping data in-house.
- Subscription-based: multi-tenant architecture benefit from a subscription-based model that provides a scalable approach to AI adoption. This model is conducive to fostering innovation and experimentation within organizations, as it allows them to tap into the power of generative AI without the need for extensive upfront investment or specialized infrastructure.
- Cost-Effectiveness: Sharing the infrastructure and resources of a single AI instance across multiple tenants can significantly reduce costs compared to individual deployments. This makes generative AI more accessible to organizations or those with limited budgets.
- Faster Time to Value: With pre-built infrastructure and potentially pre-trained models, multi-tenant solutions can get organizations up and running with generative AI much faster.
- Cost savings: Multitenancy allows companies to reduce costs by leveraging shared resources instead of having separate systems dedicated to one tenant at a time or investing in additional servers or hardware.
Not all Generative AI models are designed for Seamless Multi-Tenancy
Fluid AI makes multi-tenancy quite easy to implement for organisation, tailored to meet the specific needs and preferences based on their usecases
Fluid AI’s multi-tenant SaaS architecture establishes a secure and scalable environment where multiple Department (tenants) can share nad operate on the same underlying infrastructure while maintaining isolation of their data and operations. This is powered by the robust capabilities of generative AI, enabling multiple tenants to utilize a unified system instance effectively.
Ready to elevate your organization's efficiency with state-of-the-art multi-tenancy? Embrace the future with Fluid AI – where secure, isolated, and scalable solutions are offered with Flexibility to meet needs & preferences of your organisation. Connect with us today to unlock the full potential of Fluid AI's generative AI for your enterprise!
Unlock the future of your business with Fluid AI! Discover how our multitenant architecture can empower your enterprise with a high level of security, privacy, and scalability. Let's transform your company's approach to data and AI together. Get in touch with us now!
| Decision points | Open-Source LLM | Close-Source LLM |
|---|---|---|
| Accessibility | The code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation. | The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer. |
| Customization | LLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques. | Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer. |
| Community & Development | Benefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements. | Development is controlled by the owning company, with limited external contributions. |
| Support | Support may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance. | Typically comes with dedicated support from the developer, offering professional assistance and guidance. |
| Cost | Generally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance. | May involve licensing fees, pay-per-use models or require cloud-based access with associated costs. |
| Transparency & Bias | Greater transparency as the training data and methods are open to scrutiny, potentially reducing bias. | Limited transparency makes it harder to identify and address potential biases within the model. |
| IP | Code and potentially training data are publicly accessible, can be used as a foundation for building new models. | Code and training data are considered trade secrets, no external contributions |
| Security | Training data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the community | The codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment |
| Scalability | Users might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resources | Companies often have access to significant resources for training and scaling their models and can be offered as cloud-based services |
| Deployment & Integration Complexity | Offers greater flexibility for customization and integration into specific workflows but often requires more technical knowledge | Typically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor. |
FAQ’s related to Multi-Tenant Gen AI Architecture Solution
Why is Multi-Tenant Architecture important for Gen AI deployments?
Multi-tenant architecture is crucial for Gen AI deployments because it allows organizations to create dedicated instances for different departments or use cases, ensuring data compartmentalization and security. This ensures that only the right people have access to the right data.
How does Multi-Tenant Architecture benefit my organization?
This architecture provides scalability, cost-efficiency, and enhanced security. It allows for efficient resource utilization and ensures that sensitive data is accessible only to authorized users, thus making necessary data avaliable instantly at fingertip without extensive manual searches, saving time and maintaining data privacy.
How do I deploy a Multi-Tenant Gen AI solution?
- Set Up the Infrastructure: Begin by establishing the required cloud-based or on-premises infrastructure to support the multi-tenant environment.
- Configure Tenants: Determine the number of tenants needed and set up configurations for each one
- User and Admin Configuration: Assign users to each tenant and appropriate authorized administrators for granting necessary permissions and control.
- Provision Databases: For each tenant, create a bespoke database and populate it with the relevant data to function effectively, ensuring they have the necessary resources, so query resolution would be faster and accurate
Can each tenant customize their environment?
Yes, each tenant can customize their environment to meet specific requirements. This includes user management, access controls, and knowledge management.
How are updates and maintenance handled?
Updates and maintenance are seamlessly handled by our team for smooth user experience. This includes LLM model upgrade/ switch, additions of any new features/ capabilities and performance optimizations.
Fluid AI International Corporation (USA)