Single vs Multi Org Structure: A Decision Making Guide

For large enterprises starting their journey with Datadog, a fundamental question often emerges: How should you structure your monitoring solution? Specifically, should you organize everything into a single organization or adopt a multi-organization approach? This decision is not merely technical—it can have far-reaching implications for observability, governance, cost allocation, and compliance.

In this article, we’ll dive deep into the pros and cons of both approaches and explore practical strategies to help you make the best choice for your business needs.

Organizations in Datadog: The Basics

Before we evaluate each structure, let’s first define what an organization means in the context of Datadog.

What is an Organization in Datadog?

In Datadog, an organization is a logical unit where your monitoring data, users, dashboards, and alerts reside. For enterprises operating across multiple regions or divisions, these organizations can be further structured into parent organizations and child organizations. For example:

• A parent organization in one region might oversee multiple child organizations in the same region.
• Child organizations can also exist across different regions, ensuring data is stored and managed in compliance with local regulations.

What is Shared Between Organizations?

One of the most important aspects of Datadog’s organization model is that data is not shared between organizations by default. By default:

• Data is siloed: Users in one organization cannot access data or resources in another without explicit permissions.
• Billing and usage information are the only exceptions—these are visible across parent and child organizations.

This strict separation has significant benefits in industries where compliance and legal mandates require isolation, such as banking, insurance, or healthcare.

The Case for a Single Organization Structure

Datadog’s strength lies in its ability to deliver holistic observability. A single-organization structure leverages this strength to its fullest by consolidating all monitoring data into one central source of truth.

Advantages of a Single Organization

1. Unified Observability for Better Insights
   • When all teams and systems operate within the same organization, you gain comprehensive visibility into your infrastructure, applications, and services.
   • Dependencies between systems are easier to map, allowing teams to quickly identify root causes and reduce mean time to resolution (MTTR).
2. Improved Collaboration Across Teams - A single organization eliminates silos, fostering collaboration. All teams work from the same source of truth, reducing misunderstandings and preventing “finger-pointing” during incidents.
3. Streamlined Training and Governance
   • Centralizing data makes it easier to standardize practices, such as tagging conventions and deployment pipelines.
   • Training becomes more effective since all users work within a unified environment, making it easier to identify knowledge gaps and areas for improvement for each individual teams.
4. Accelerated Best Practice Adoption - With all data in one place, teams can share dashboards, monitors, and alerts. This reduces duplication of effort and accelerates the deployment of new solutions.
5. Efficient Cost Allocation - By using tagging, you can allocate costs to specific teams, projects, or departments without needing separate organizations. (Article on Infra Tagging Best Practices)

Real-World Impact

Enterprises that adopt a single-organization approach often see dramatic improvements in their observability maturity. For example, as a Technical Account Manager, during 5 years of supporting large enterprise in their observability strategy, we’ve observed that organizations with a single org achieve:

• Faster time-to-value from their Datadog implementation.
• Higher engagement from teams who see observability as a strategic asset rather than an operational burden.
• Easier and higher quality adoption of advanced features like custom metrics, log analysis, and anomaly detection which ultimately lead to better observability.

Tagging: The Key to Separation in a Single Org

One of the most powerful tools for managing complexity within a single organization is tagging. Tags can be used not just for technical categorization (e.g., by cluster, environment, or service) but also for governance and cost management.

Best Practices for Tagging in Datadog

1. Categorize by Business Function
   • Add tags that reflect organizational structures, such as project names, cost centers, or teams.
   • Use Datadog’s team tags to group resources by ownership and responsibilities.
2. Enable Cost Attribution - Properly tagged resources allow you to track costs at a granular level. Datadog’s usage attribution feature enables admins to:
   • Summarize monthly usage by tag.
   • Visualize usage trends over time.
3. Consistency is Key - The effectiveness of tagging depends on its consistency. At Dataiker, we include automated checks to ensure tags are applied correctly and train teams on best practices for tagging.

When a Multi-Organization Structure is Necessary

While a single-org approach is often ideal, certain scenarios necessitate a multi-org setup.

When to Choose a Multi-Organization Structure

1. Regulatory Compliance - Industries with strict regulations often require data separation between teams, business units, or regions. For example:
   • Data residency laws may mandate that certain data remain within specific geographic boundaries.
   • Sensitive information, such as financial or medical data, may need to be isolated to ensure compliance with standards like GDPR or HIPAA.
2. Handling Highly Sensitive Information - Some organizations prefer a multi-org structure to ensure that sensitive data is accessible only to specific teams or departments. In the healthcare industry, banks or insurances for instance, some departments have to be completely isolated due to the sensitivity of information.
3. Complex Organizational Hierarchies - Enterprises with multiple subsidiaries, joint ventures, or distinct business units may find it easier to manage governance and billing with separate organizations.

Emerging Cross-Organization Features

Datadog is actively investing in cross-organization visibility, which allows limited sharing of metrics between organizations. While these features are still evolving, they may enable hybrid models that combine the benefits of single-org and multi-org setups in the future. Note that for now, it is limited to metrics only and does not cover all the data sources available in a platform.

Real-World Challenges with Multi-Org Setups

While a multi-organization structure can address specific needs, it comes with significant drawbacks:

1. Fragmented Observability - Data silos make it harder to correlate information across teams, leading to slower incident response and reduced visibility into system dependencies. One common issues is when teams have instrumented their APM services across multiple orgs and cannot visualize and end to end flow.
2. Increased Complexity - Managing multiple organizations adds administrative overhead, from user management to tracking best practices.
3. Loss of Shared Resources - Dashboards, monitors, and alerts cannot be easily shared across organizations, leading to duplication of effort. Users also don't build a culture of sharing as much in a single organization setup.
4. Reduced Collaboration - Teams operating in isolated environments are less likely to share knowledge, reducing the overall value of observability.

Striking a Balance: Hybrid Strategies

For most large enterprises, the ideal solution lies somewhere in between. Even when multiple organizations are necessary, aim to consolidate as much data as possible into a few organizations.

Strategies to Optimize Multi-Org Setups

1. Minimize the Number of Organizations - Consolidate whenever possible. For example, regional organizations can aggregate data from smaller business units. Above all else, this is the rule to keep in mind! It will help get the benefit of a single org approach for most users and ultimately improve the overall observability maturity.
2. Adopt Consistent Tagging Standards - Ensure tagging conventions are standardized across all organizations to facilitate governance and cost attribution.
3. Create Shared Playbooks - Document best practices for observability and ensure they are implemented consistently across organizations.
4. Use Cross-Org Visibility - Leverage Datadog’s emerging cross-org features to share key metrics between organizations.

Why We Recommend a Single-Org Approach

Based on our experience, enterprises that consolidate their observability data into a single organization consistently achieve better outcomes. A single-org approach:

• Enhances visibility: All teams work from the same data, leading to faster troubleshooting and better decision-making.
• Fosters collaboration: Teams are more likely to share knowledge and assets in a unified environment.
• Supports growth: As organizations mature in their observability practices, the centralized model scales more effectively.

In addition, in the 5 years working in Datadog, we regularly supported companies wanting to merge organizations into a single one and have never seen any work being done to split organizations. Note that even though feasible, merging organization require various level of efforts depending on the automation in place and consistency across orgs.

TL;DR

Single Organization

• Best For: Unified observability, collaboration, and best practice adoption.
• Advantages:
  • Single source of truth.
  • Comprehensive visibility and faster troubleshooting.
  • Simplified governance and training.
  • Efficient cost allocation via tagging.

Multi-Organization

• Best For: Regulatory compliance and sensitive data handling.
• Advantages:
  • Data isolation for legal or business reasons.
  • Regional or functional separation.

Final Thoughts: Make Observability a Strategic Asset

While a multi-org structure may be necessary in certain cases, a single-org approach is the gold standard for most Datadog users. By consolidating data, fostering collaboration, and leveraging tools like tagging, you can transform observability from a technical function into a strategic advantage.

For enterprises grappling with this decision, start by understanding your compliance requirements, then evaluate the long-term benefits of holistic observability. With the right approach, you’ll not only maximize the value of your Datadog investment but also empower your teams to work smarter and faster. At Dataiker, we aim to provide guidance to make the best out of your Datadog investment.