Shared vs. Siloed Tenants: Which Scales Better?
Shared vs. Siloed Tenants: Which Scales Better?
When managing logs for multiple clients, you must decide between shared or siloed tenant models. Each has its pros and cons, particularly when it comes to scaling, performance, and cost:
- Shared tenants: All clients share infrastructure, reducing costs and simplifying management. However, risks like the "noisy neighbour" effect (one tenant's activity impacting others) and potential data breaches require robust safeguards.
- Siloed tenants: Each client gets dedicated resources, ensuring strong isolation and consistent performance. This is ideal for compliance-heavy industries like healthcare or banking but leads to higher costs and operational complexity as the number of tenants grows.
Quick Comparison
| Feature | Shared Tenants | Siloed Tenants |
|---|---|---|
| Scalability | High; easy to onboard tenants | Low; setup slows as tenants grow |
| Performance | Variable; noisy neighbours risk | Consistent; no interference |
| Cost | Lower; shared resources | Higher; dedicated resources |
| Management | Centralized and simpler | Decentralized and complex |
| Isolation | Logical (IDs/schemas) | Physical/virtual (separate infra) |
For most, shared models are cost-effective and scalable, while siloed models suit industries needing strict data separation. A hybrid approach - shared by default, siloed for high-security clients - offers balance.
Shared vs Siloed Tenant Models Comparison Chart
AWS re:Invent 2024 - SaaS storage strategies: Scaling, securing & tuning multi-tenant data (SAS306)
How Shared Tenants Scale
Shared tenant models grow by expanding resources within a single infrastructure pool. Whether you're managing 50 or 500 organisations, scaling simply involves increasing the capacity of the existing cluster. This horizontal approach adjusts resources dynamically based on the overall workload across tenants [5].
This method significantly boosts operational efficiency. For example, onboarding a new client becomes a matter of minutes rather than hours since there's no need to set up separate infrastructure. As highlighted by AWS in their whitepaper:
"With the pool model, you don't need to provision separate storage infrastructure for each tenant that signs up for your SaaS service. You can simply provision your new tenant and use that tenant's ID as the index to access the tenant's data" [2].
This streamlined process ensures new clients are operational almost immediately, saving valuable time.
Another advantage is centralized maintenance. Security patches, feature updates, and configuration changes can be applied across all tenants simultaneously, keeping the system efficient and secure [5]. This avoids what Microsoft describes as an unsustainable practice:
"Deploying individual Azure resources for each customer is likely unsustainable, unless you provision and use a dedicated subscription for each tenant" [3].
Shared models also excel in resource optimization. For instance, when one client’s syslog traffic decreases overnight while another’s spikes during business hours, the same infrastructure adapts to handle both workloads seamlessly. This flexibility eliminates idle resources and keeps costs efficient as demand fluctuates [5].
Up next, we’ll explore how siloed tenant models measure up in terms of scalability, performance, and cost.
How Siloed Tenants Scale
Siloed models grow by allocating dedicated resources for each client. This means each tenant gets their own isolated compute, storage, and networking setup. The result? No competition for resources, ensuring consistent performance regardless of what other tenants are doing [1][8].
Because there are no "noisy neighbors", a sudden surge in one tenant's syslog traffic won’t disrupt another tenant’s ability to ingest or query data. This is especially important when using live syslog visualization to track events as they happen. AWS highlights this in their whitepaper:
"Silo addresses this concern by offering a dedicated environment with no potential of noisy neighbour scenarios" [1].
This level of reliability is crucial for high-volume workloads where even slight delays can interfere with real-time monitoring.
Siloed models also cater to industries with strict regulatory requirements. Sectors like banking and healthcare often demand physical or logical data separation, making siloed architectures ideal for meeting GDPR and NIS2 compliance [1][8]. Furthermore, if there’s an infrastructure failure or a security breach, the issue is confined to one tenant, preventing it from affecting others [1].
However, scaling siloed tenants isn’t without its challenges. Unlike shared models, which are easier to scale, siloed setups become increasingly complex as the number of tenants grows. Managing 10 or 20 isolated accounts might be doable, but scaling to hundreds or thousands? That’s a different story. As Sandro Cirulli from The Scale Factory puts it:
"Having 10-20 siloed accounts is still manageable from the operational point of view but how about managing hundreds or even thousands isolated accounts? Not so easy…" [6].
Another drawback is cost inefficiency. Dedicated resources often remain underutilized during off-peak times, and unlike shared models, unused capacity cannot be reallocated to other tenants [1][8]. To address these issues, you can leverage tools like Terraform and AWS Control Tower to automate resource provisioning. Monitoring tools like AWS Trusted Advisor can also help identify and eliminate idle resources, improving overall efficiency [6][8].
This sets the stage for a closer look at how shared and siloed models compare in terms of scalability, performance, and cost.
Shared vs. Siloed Tenants: Scalability, Performance, and Cost
When comparing scalability, performance, and cost efficiency, shared and siloed models each bring their own set of strengths and challenges. Understanding these differences is crucial for selecting the right architecture for your syslog management needs.
Scalability
Shared models shine when it comes to scalability. They can onboard new clients instantly using a tenant ID index and centralized monitoring systems. In contrast, siloed models require provisioning an entirely separate stack for each new tenant, which slows down the process significantly. While managing 10–20 siloed accounts is manageable, scaling to hundreds or thousands of accounts can quickly become a logistical nightmare. As AWS highlights:
"If you have a thousand tenants, that would likely begin to impact operational efficiency and agility" [1].
Shared models also offer the convenience of unified management - everything is centralized under a single interface for updates and monitoring. On the other hand, siloed models demand more effort, as data must be aggregated from decentralized sources. These scalability differences naturally influence performance, which we’ll explore next.
Performance
Siloed models excel in delivering consistent performance. By dedicating resources to each tenant, they eliminate the risk of noisy neighbour effects [1]. In shared models, however, resource governance becomes critical. Without proper controls, one tenant’s heavy workload could impact the performance of others. Additionally, infrastructure failures in a shared environment can disrupt all tenants, whereas issues in siloed setups are isolated to individual accounts.
That said, shared models can maintain stable performance with the right proactive measures, such as robust monitoring and throttling mechanisms. These tools help ensure that workloads are balanced and that no single tenant overwhelms the system.
Cost Efficiency
Cost is another area where the two models differ significantly. Shared models are generally more cost-effective, as they scale resources based on aggregate demand. Siloed models, however, incur higher costs due to their isolated resource allocation. As Microsoft explains:
"Operating a solution that has shared components is less expensive than using individual resources for each tenant" [3].
In siloed setups, dedicated resources often go unused during off-peak periods, leading to inefficiencies. Miłosz Wojarski, .NET Tech Lead, notes:
"With more independence, there come higher costs, but depending on your business model and use case, those costs could be necessary to take to provide a stable, safe and reliable system" [9].
Here’s a quick comparison of how the two models stack up:
| Feature | Shared (Pool) Model | Siloed (Silo) Model |
|---|---|---|
| Scalability | High; scales with total demand | Lower; constrained by resource limits |
| Performance | Variable; risk of noisy neighbour effects | Consistent; no cross-tenant interference |
| Cost Efficiency | High; benefits from shared resources | Low; higher costs due to idle capacity |
| Management | Centralized and simplified | Decentralized; more labor-intensive |
| Isolation | Logical (tenant IDs/schemas) | Physical/virtual (separate DBs/accounts) |
Some organizations find a middle ground by adopting a hybrid approach. For example, they might use a shared model as the default but reserve siloed setups for high-value clients who require stricter isolation. This approach balances cost savings with the flexibility to meet varying client needs.
Challenges and How to Address Them
Both shared and siloed tenant models bring their own set of challenges, beyond just scalability and performance concerns.
Challenges in Shared Tenants
Shared tenant models often struggle with the noisy neighbour effect. This happens when one tenant uses up more resources than expected, potentially slowing down performance for others. As AWS puts it:
"The more resources are shared, the more chances there are for one tenant to impact the experience of another" [5].
Another issue is the blast radius - a failure in shared infrastructure can disrupt all tenants simultaneously [5]. Additionally, shared environments encounter resource quotas and limits, such as storage throughput or API rate caps, which become more noticeable as you scale [3].
To tackle these problems, tenant-aware sharding and robust monitoring can help. These strategies spread tenants across multiple resource pools to avoid hitting limits and ensure heavy workloads are detected and managed before they cause disruptions [3]. Adding RBAC (role-based access control) further enhances logical separation between tenants.
Challenges in Siloed Tenants
Siloed models avoid noisy neighbours but bring their own complications, particularly around management overhead. Sandro Cirulli from The Scale Factory highlights this challenge:
"Having 10–20 siloed accounts is still manageable from the operational point of view but how about managing hundreds or even thousands isolated accounts? Not so easy…" [6].
Each tenant in a siloed model requires its own infrastructure stack. This means updates, security patches, and provisioning need to be handled individually [6]. Another issue is resource inefficiency - dedicated resources often remain underutilised during off-peak times, which can undermine the cost-effectiveness of a SaaS model [1].
Automation tools like Terraform and AWS Control Tower can simplify provisioning and reduce manual work [6]. Tools such as AWS Trusted Adviser can help identify unused resources, allowing for right-sizing to cut down on unnecessary costs [6]. Addressing these inefficiencies is essential for maintaining scalability and managing expenses.
LogCentral as a Scalable Solution
Automation and integrated platforms provide a way to navigate these challenges effectively.
LogCentral offers a balanced approach by combining the cost advantages of shared infrastructure with the security of logical isolation. Its native multi-tenancy uses RBAC to ensure tenants remain logically isolated. The platform mitigates the noisy neighbour effect with intelligent alerts and 24/7 monitoring, designed to catch and address anomalies before they escalate.
For organisations focused on compliance, LogCentral is GDPR-ready and hosted in Europe, simplifying adherence to data residency rules. It also supports crypto-shredding, enabling the deletion of tenant-specific encryption keys to make data permanently inaccessible upon request [4]. Managed service providers (MSPs) benefit from a centralised interface, which reduces the complexity of managing siloed setups while still meeting the isolation and security needs of enterprise clients.
Which Model Scales Better for Syslog Management?
The choice of model hinges on your specific needs. Shared tenant models are designed to maximise hardware efficiency by hosting multiple tenants on a single infrastructure stack. This approach keeps costs low, speeds up onboarding, and simplifies updates and security patches across the platform [7][2]. However, if your environment demands strict data segregation, the siloed model might be a better fit.
Siloed tenant models are tailored for industries like banking, healthcare, or government, where compliance requirements demand physical data separation. These sectors often impose strict data residency controls and dedicated resource usage [10][6]. While this model ensures robust isolation, scaling it to manage hundreds or thousands of accounts can lead to significant operational challenges, potentially affecting efficiency.
Hybrid models provide a balanced solution, combining shared infrastructure for standard users with dedicated resources for clients that have heightened compliance needs [2][4]. This allows managed service providers (MSPs) to optimise resource use for most tenants while offering isolated environments when enhanced security is necessary. Amazon Web Services (AWS) highlights this approach:
"The silo model lets you offer your tenants an option to create a more concrete boundary between tenant data... whilst the efficiency and agility of the pool model might unlock your path to an environment that embraces rapid and continual releases" [2].
A practical example of this balance is LogCentral, which demonstrates how shared models can scale effectively while maintaining high security standards. Its multi-tenancy system uses role-based access control (RBAC) to ensure logical data separation, eliminating the need to manage numerous siloed accounts. Fully GDPR-compliant and hosted in Europe, LogCentral simplifies data residency compliance for French businesses. For MSPs, the platform's centralised interface reduces complexity, while intelligent alerts and 24/7 monitoring help prevent issues like noisy neighbour effects.
For most syslog management needs, a well-executed shared model - or a hybrid approach - offers the best combination of scalability, cost-efficiency, and security. Siloed models still hold value for specialised compliance scenarios, but automation tools like Terraform are essential to mitigate operational bottlenecks [6].
Conclusion
Shared and siloed tenant models each come with their own pros and cons. Siloed architectures offer strong data isolation and consistent performance, making them ideal for industries like banking, healthcare, and government, where strict compliance rules demand physical or logical separation of data [6][2]. However, managing hundreds of siloed instances without automation can quickly become overwhelming, and the associated costs remain steep.
On the other hand, shared models shine in terms of resource efficiency and faster onboarding, significantly reducing per-tenant costs [11]. But with shared resources, there’s always a level of risk to consider.
To navigate these trade-offs, many organisations are adopting a hybrid strategy. This approach involves pooling resources for standard users while reserving dedicated infrastructure for clients with stringent compliance needs. It’s a way to achieve cost efficiency without sacrificing security.
A great example of this is LogCentral, which has implemented a scalable shared model that maintains GDPR compliance. Hosted in Europe, it uses RBAC (Role-Based Access Control) to ensure logical data separation while simplifying operations. For French businesses and MSPs, this means easier compliance management and smoother monitoring processes.
FAQs
What are the benefits of using a hybrid tenant management model?
A hybrid tenant management model combines the advantages of shared and siloed architectures, delivering a flexible and cost-conscious solution for businesses with varying requirements. By allowing tenants with similar needs to share infrastructure, this model optimises resource allocation and trims down operational costs. Simultaneously, it provides isolated environments for tenants needing higher levels of security or customisation, ensuring data security and adherence to regulations like GDPR.
This dual approach is especially beneficial for IT teams and Managed Service Providers (MSPs), as it enables tailored configurations without sacrificing performance. For instance, sensitive data can be stored in secure, isolated setups, while day-to-day operations can leverage shared resources for efficiency. This balance makes the hybrid model an appealing option for organisations looking to grow without compromising on operational or regulatory needs.
Platforms such as LogCentral are specifically built to support these models. They offer features like native multi-tenancy, real-time log visualisation, and smart alerts, making it easier for businesses to manage operations smoothly and boost performance across the board.
What is the 'noisy neighbour' effect in shared tenant models, and how does it impact performance?
The 'noisy neighbour' effect happens in shared tenant models when one tenant overuses shared resources like CPU, memory, or bandwidth. This can result in slower response times or even service disruptions for others sharing the same infrastructure. Imagine a tenant performing heavy data imports or running resource-intensive operations - this could cause noticeable delays for everyone else.
To address this, techniques such as resource isolation, setting quotas, or even providing dedicated resources per tenant are commonly used. These approaches help maintain fair resource distribution and keep systems stable, but they can come with higher costs. Organisations need to find the right balance between shared and isolated models to maximise both performance and cost-efficiency. Tools like LogCentral, which incorporate native multi-tenancy and smart resource management, offer practical solutions to tackle these challenges.
Why would a company opt for a siloed tenant model even if it costs more?
Companies might opt for a siloed tenant model, even with its higher costs, because it delivers key advantages like stronger data security, strict adherence to regulatory requirements, and the ability to create tailored environments for individual tenants. This setup ensures complete isolation, making it especially appealing for organisations managing sensitive information or working in sectors governed by strict data protection laws, such as GDPR in Europe.
Although shared tenant models are generally more budget-friendly, the siloed model provides unmatched control and adaptability. For IT teams and managed service providers (MSPs), tools like LogCentral - offering native multi-tenancy, robust security features, and compliance with European regulations - can effectively address performance, scalability, and compliance challenges.