Home Artificial Intelligence What’s Multitenancy in Vector Databases?

What’s Multitenancy in Vector Databases?

What’s Multitenancy in Vector Databases?

While you upload and manage your data on GitHub that nobody else can see unless you make it public, you share physical infrastructure with other users. That is because GitHub uses multitenancy as a cheap and easier-to-manage alternative to assigning a separate database to every user.

Nevertheless, sharing the identical infrastructure becomes a security risk when all users can view one another’s data. Multitenancy addresses this issue by logically partitioning user data while allowing them to run on the identical resources.

This text explores multitenancy in vector databases, its advantages, limitations, and real-world use cases.

How Does Multitenancy Work in Vector Databases?

Multitenancy is an approach where multiple tenants, i.e., users, share the identical database but store their data in an isolated environment.

An isolated environment is created using unique credentials for every tenant to secure their data. Because of this, each tenant can store, manage, and alter their data of their isolated environment. Nevertheless, the corporate has the access to administer and control tenant resources and limitations.

Sample illustration of a two-tenant collection with isolated access to the identical database. Image Source: Qdrant

Vector databases use indexing as a search technique that organizes vectors based on similarity. The indexing strategy impacts the tenant data partitioning. Currently, two indexing strategies are utilized in multitenant vector databases.

Let’s discuss each indexing strategies in multitenant vector databases:

  1. Shared Indexing: All tenants share the identical index with unique credentials partitioning the information. This method is memory efficient. Nevertheless, it requires robust security and access control mechanisms to guard tenant data.
  2. Per-tenant Indexing: Every tenant has a separate index in per-tenant indexing. This enables complete access control and improved search performance. Nevertheless, this method is resource-intensive.

Some vector databases like Qdrant and Milvus offer multitenant architecture to permit added customization and scalability for users with each indexing strategies.

Advantages of Multitenancy in Vector Databases

Multitenancy in vector databases offers quite a few advantages for firms that require isolated database instances for several users. Among the advantages include:

1. Cost reduction

Using fewer resources for more users ends in reduced infrastructure costs.

2. Scalability

Multitenancy allows need-based resource sharing. This implies tenants with more storage requirements get more resources and vice versa.

3. Customization

A separate environment allows tenants to configure it based on their needs, including database schema, plugins, metrics, and dashboards. Configurations are private to tenants, and tenants can change them as their requirements change.

4. Manageability

A single database for all tenants allows centralized resource management, configuration, and monitoring as a substitute of monitoring all tenants individually. While an organization can manage all tenants in a single place, tenants have the control to administer their data inside their isolated environments.

Limitations of Multitenancy in Vector Databases

Like several other architectural approach, multitenancy has some limitations. Considering these limitations is significant for careful decision-making. Essentially the most common limitations include:

1. Additional Complexities

Managing multiple tenants on a single resource requires added configuration. This includes tenant onboarding, access control, user authentication, and authorization. Lack of know-how and support could lead on to unwanted outcomes like accidental data sharing or resource overhead.

To handle this, careful planning and database support ensures a secure user environment.

2. Security Concerns

Malicious access, accidental misconfigurations, or vulnerabilities in underlying infrastructure can result in shared data amongst tenants. As guardrails, implementing careful design, conducting regular audits, and incorporating multi-layer security measures can strengthen overall security.

3. Performance Bottlenecks

Higher usage of resources by a tenant can decelerate the performance of others. Shared indexing specifically affects search performance resulting from runtime permission checks to match the access list. Resource management and control, regular updates, and tenant education are necessary to mitigate performance issues.

4. System Outage

Scheduled maintenance, hardware failure, and software bugs affect all tenants after they share an identical infrastructure. This results in data, fame, and financial losses. Regular risk assessment, infrastructure quality assurance, and timely backup can minimize the negative impact of system outages.

Use cases of Multitenancy

Multitanency is helpful in various applications, from e-commerce advice systems to training large machine learning (ML) models in firms. Just a few of probably the most common use cases include:

1. Advice Systems

Imagine an e-commerce platform where users can join and save their shopping preferences. A multitenant setup will allow personalized product recommendations to every user.

On the e-commerce platform, all tenants can set their criteria, so the advice system sends personalized product recommendations to finish users.

2. Enterprise Applications

Large software applications serving multiple employees and customers use the identical database for all users. All users can upload and manage their data while protecting it from others. For example, Dropbox and HubSpot allow all users to share the identical resources but keep their data protected against one another.

3. Anomaly and Fraud Detection

Multitenancy allows the event of sturdy fraud detection systems while keeping individual data secure. Firms train fraud detection models on their anonymized data and send only the trained model over the centralized database. This enables them to maintain their data secure while contributing to developing fraud detection systems.

For instance, bank card fraud detection systems use ML for enhanced privacy and efficiency.

When to Use and When To not Use Multitenancy

Multiple aspects contribute to the choice to modify to multitenancy, including tenant performance, isolation requirements, and security concerns. Let’s discuss when and when not to make use of multitenancy intimately below.

When to Use Multitenancy

The next indicators make multitenancy fit:

  1. Multiple tenants need separate environments.
  2. Tenants can accept performance tradeoffs.
  3. Cost reduction is your priority.
  4. Centralized tenant management improves your operations.

When To not Use Multitenancy

Limitations of multitenancy keep it from making fit for all situations. A multitenant vector database isn’t fit for you for those who’ve the next requirements:

  1. Tenants own highly sensitive data with strict security requirements.
  2. A limited variety of tenants with slow growth.
  3. Tenants require dedicated environments and might’t tolerate performance degradation.
  4. Limited multitenant expertise and capability to handle increasing complexity.

Multitenancy introduces additional scalability and manageability to the vector databases. If configured accurately, multitenancy saves significant costs and resources for a company.

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