Peter Hoffmann

Tenant Isolation in Snowflake

Peter Hoffmann
Posted on October 16, 2023 in architecture snowflake sql

Multi-tenancy is an architectural approach in which a single software system or data platform serves multiple independent customers (tenants), while ensuring that each tenant’s data, workloads, and configurations remain logically or physically isolated according to defined boundaries. Snowflake offers different options to achive tenant isolation.

Multi-tenancy is an architectural approach in which a single software system or data platform serves multiple independent customers (tenants), while ensuring that each tenant’s data, workloads, and configurations remain logically or physically isolated according to defined boundaries. The goal of multi-tenancy is to maximize resource efficiency and operational scalability while preserving security, performance predictability, and administrative separation, with isolation implemented at one or more layers such as infrastructure, accounts, databases, schemas, or rows within shared tables.

There is no single best architecture and it is always a balance between competing goals, architectural choices, customer size, and the total number of customers being served. This post walks through the main tenant isolation strategies in Snowflake—from fully separate accounts down to logical isolation with a tenant_id column—covering pros, cons, best practices, and operational considerations.

This article focuses primarily on tenant isolation from a data storage and data access perspective, examining how tenants can be separated using accounts, databases, schemas, or shared tables. It is important to note, however, that Snowflake introduces a separate and equally important dimension through its compute layer, where virtual warehouses provide independent scaling, workload isolation, and performance control. While storage-level isolation defines how data is organized and secured, compute-level isolation plays a critical role in managing concurrency, cost, and noisy-neighbor effects.

What Do We Mean by Tenant Isolation?

Tenant isolation typically aims to achieve some combination of:

Snowflake’s architecture (separate storage and compute, strong RBAC, and secure sharing) allows you to choose isolation at multiple layers.

Option 1: Separate Snowflake Accounts per Tenant

Tenant isolation using separate Snowflake accounts is best suited for large enterprise customers and for scenarios with strict compliance or regulatory requirements where strong isolation is mandatory. It is also appropriate for tenants with highly variable or unpredictable workloads, as well as for “bring your own Snowflake” models where customers operate within their own Snowflake environments.

Description

Each tenant gets its own Snowflake account. Data, users, warehouses, and security policies are completely isolated.

Tenant A → Snowflake Account A  
Tenant B → Snowflake Account B

Best Practices

Snowflake Organizations should be used to centrally manage and govern multiple Snowflake accounts, providing a unified view for administration, security, and billing across tenants. This helps reduce operational overhead while maintaining strong account-level isolation.

Account and tenant provisioning should be fully automated using infrastructure-as-code tools such as Terraform or Snowflake’s APIs. Automation ensures consistency, reduces manual errors, and makes onboarding and offboarding tenants predictable and repeatable at scale.

Role definitions, warehouse configurations, and database naming conventions should be standardized across all tenants. Consistent patterns simplify access management, monitoring, and troubleshooting, and they make it significantly easier to apply changes or enforce policies uniformly.

When cross-tenant or centralized analytics is required, Snowflake’s secure data sharing capabilities should be used instead of copying data between accounts. Secure sharing enables controlled access to tenant data while preserving isolation and minimizing data duplication.

Pros

Cons

Option 2: Shared Account, Separate Databases per Tenant

A shared account with separate databases per tenant is well suited for a medium number of tenants with moderate compliance requirements. This approach is particularly useful when per-tenant backup, restore, or data lifecycle operations need to be handled independently without the overhead of multiple accounts.

Description

All tenants live in one Snowflake account, but each tenant gets its own database.

ACCOUNT
 ├── TENANT_A_DB
 ├── TENANT_B_DB

Best Practices

Each tenant should be assigned its own database while maintaining an identical schema structure across all tenant databases. This consistency simplifies development, testing, and operations, and allows changes to be applied predictably across tenants.

Database roles should be used to control tenant access at the database level. Leveraging database roles provides clear separation of privileges, reduces the risk of misconfiguration, and aligns well with Snowflake’s role-based access control model.

Databases should be tagged with metadata such as tenant, environment, and cost_center to support cost tracking, governance, and operational visibility. Tags make it easier to analyze usage, implement chargeback or showback models, and apply policies consistently.

Schema migrations should be fully automated across all tenant databases using standardized deployment pipelines or migration tools. Automation ensures schema changes are applied reliably and uniformly, minimizing drift and reducing the operational burden as the number of tenants grows.

Pros

Cons

Option 3: Shared Database, Separate Schemas per Tenant

Tenant isolation using a share databases with schema separation per Tenant within a single Snowflake account is well suited for smaller tenants and internal multi-team environments where strong logical separation is needed without the overhead of multiple accounts. It is also a good fit for early-stage SaaS platforms that want to balance isolation, simplicity, and operational efficiency while they scale.

Description

A single database hosts multiple schemas, one per tenant.

DATABASE
 ├── TENANT_A_SCHEMA
 ├── TENANT_B_SCHEMA

Best Practices

Strict role-to-schema mappings should be enforced so that each role has access only to the schemas it is explicitly responsible for. This minimizes the risk of accidental data exposure and makes access boundaries clear and auditable.

Cross-schema references should be avoided wherever possible, as they weaken isolation guarantees and make it harder to reason about data ownership and access paths. Keeping schemas self-contained improves security, maintainability, and portability.

Consistent naming conventions for schemas, roles, and objects should be used across all tenants. Standardization simplifies automation, monitoring, and troubleshooting, and reduces cognitive overhead for operators and developers.

Grants and permissions should be periodically audited to ensure they still reflect intended access patterns. Regular reviews help detect privilege creep, misconfigurations, and potential security gaps before they become issues.

Pros

Cons

Option 4: Shared Tables with tenant_id Column

This approach is well suited for environments with a large number of small tenants and primarily read-heavy analytics workloads, where sharing infrastructure provides significant efficiency gains. It allows the platform to scale to many customers without excessive operational overhead.

Because isolation is largely enforced logically, it works best when strong application-level controls are in place to prevent cross-tenant access and enforce correct query patterns. It is also an attractive option for cost-sensitive environments, as shared storage and compute help minimize overall platform expenses.

Description

All tenants share the same tables, distinguished by a tenant_id column.

SELECT *
FROM orders
WHERE tenant_id = 'tenant_123';

Isolation is enforced through application logic and Snowflake features such as row access policies.

Best Practices

Access to shared tables should be enforced using row access policies to ensure that each tenant can only see its own data, regardless of how queries are written. This provides a critical safety net and reduces reliance on application logic alone for isolation.

The tenant_id should always be included in primary keys and used consistently in table design, as this makes tenant boundaries explicit and prevents accidental key collisions. Including tenant_id also enables more efficient pruning and predictable query behavior.

Clustering tables by tenant_id should be considered, especially when tenants frequently query their own data in isolation. Proper clustering can significantly improve query performance and reduce unnecessary data scanning in large shared tables.

Automated tests should be added to validate that all queries include the appropriate tenant filters and that row access policies are correctly applied. Testing helps catch regressions early and prevents subtle mistakes from leading to cross-tenant data exposure.

Finally, query patterns should be continuously monitored to detect any signs of cross-tenant access or anomalous behavior. Regular analysis of query and access logs helps identify misconfigurations, misuse, or potential security issues before they escalate.

Pros

Cons

Operational Considerations Across All Models

1. Cost Management

2. Performance Isolation

3. Security and Auditing

4. Data Lifecycle Management

5. CI/CD and Schema Changes

Choosing the Right Model

Requirement Recommended Approach
Maximum isolation Separate accounts
Strong isolation, fewer ops Database per tenant
Moderate isolation Schema per tenant
High scale, low cost Shared tables with tenant_id

In practice, you often implement hybrid approaches where one or more customers are grouped within a single Snowflake account, while each customer is isolated using a dedicated database. This model strikes a balance between strong logical isolation and manageable operational overhead, allowing teams to scale without fully committing to an account-per-tenant strategy.

This pattern is often aligned with a cell-based architecture, where each account represents a cell that hosts a bounded set of customers with similar characteristics, such as region, compliance profile, or service tier. Within a cell, customers are isolated at the database level, while additional cells can be added over time to limit blast radius, control growth, and support horizontal scaling. This approach enables predictable operations, clearer governance boundaries, and a gradual path toward stronger isolation for customers that outgrow their current cell.

Additional Documentation