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Migrating to Datadog: What to Know Before You Make the Move

10/23/25 | EverOps

If your team has ever scrambled during an outage, jumping between five different monitoring tools just to pinpoint the issue, you are not alone. For many companies, visibility gaps, alert fatigue, and slow incident response have become the norm rather than the exception.

That’s where Datadog comes in. Designed for modern, distributed environments, it combines metrics, traces, logs, and security insights into a single, intuitive platform. With advanced AI-driven features and real-time collaboration tools, Datadog helps reduce mean time to resolution (MTTR) times by 15% and has successfully reduced the number of issues reported by its users by more than 30%. 

But migrating to Datadog isn’t necessarily a plug-and-play process. Whether you are moving away from legacy systems, struggling with disjointed tooling, or preparing to scale, a successful migration requires the proper strategy and organizational alignment. This guide will provide insights into why organizations are making the shift to Datadog, how to time your migration effectively, what a smooth transition looks like, and how to ensure a successful long-term adoption and measurable ROI.

The Case for Datadog: Why Migrate

The observability platform you choose today will shape how your organization responds to incidents, improves performance, and delivers reliable customer experiences in the future. For engineering teams stuck juggling disconnected monitoring tools, Datadog offers more than just a technical upgrade. It provides real business value through faster incident resolution, deeper system visibility, and better cross-team collaboration. 

Industry leaders like Samsung, Peloton, Whole Foods, and Sony rely on Datadog for immediate operational improvements, long-term scalability, and strategic advantage. For organizations ready to move beyond fragmented monitoring, Datadog represents a clear investment in stability, efficiency, and growth. Here’s why it’s worth migrating: 

Unified visibility across systems 

Datadog brings together metrics, logs, traces, events, and security insights into one fully integrated platform. This unified and continuous integration (CI) visibility eliminates silos between teams and tools, allowing organizations to see their infrastructure, applications, and services in real time. With everything in one place, teams can troubleshoot faster, collaborate more effectively, and make decisions based on complete, accurate data.

Proven financial impact 

According to IBM Global Services, large enterprises lose an average of $400,000 per hour or $1.5 million per quarter during unplanned application outages. Even more staggering, 35% of enterprises experience such outages monthly. Datadog helps reduce both the frequency and duration of downtime, directly impacting operational costs and business continuity. For many organizations, the investment in Datadog quickly pays for itself.

Measurable operational gains

Datadog’s AI-powered observability platform enables teams to detect, diagnose, and resolve issues faster, often before they even impact users. According to Forrester, organizations that pair observability with AIOps can increase application availability by 15%, reduce MTTR by 50%, and cut the time spent resolving false positives by as much as 80%. By automatically correlating signals across metrics, logs, and traces, Datadog equips teams with the real-time context they need to reduce noise, improve reliability, and increase operational gains overall. 

Scalability and performance enhancements

Built for cloud-native environments, Datadog is designed to scale with your infrastructure as it grows. Whether you are managing hundreds or thousands of hosts across multiple regions, the platform delivers high performance with low latency. Its flexible architecture makes it ideal for fast-growing companies and large enterprises alike.

Deep integration and ecosystem flexibility

Datadog integrates with over 800 technologies, from cloud providers and databases to CI/CD tools, security platforms, and collaboration apps. This extensibility allows teams to plug Datadog into their existing workflows without disruption. With robust APIs, prebuilt dashboards, and automation-ready features, Datadog is more than just a monitoring tool and is now a critical part of your engineering stack.

Enterprise adoption and growth 

As of late 2023, Datadog reported 396 customers with annual recurring revenue (ARR) of $1 million or more, a 25 percent increase from 317 the year before. The company also generated over $2.13 billion in total revenue for the fiscal year, reflecting strong momentum and enterprise trust. This level of adoption highlights Datadog’s role not only as a developer-friendly tool but as a mission-critical platform for some of the most mature and complex organizations in the world.

When to Migrate

Migrating to Datadog is not just a matter of switching tools. It’s about recognizing key moments in your organization’s growth when better visibility, faster diagnostics, and stronger infrastructure alignment are essential. The right timing can amplify the value of the platform, minimize disruption, and help your teams move with greater speed and confidence. Here is when you should consider making the migration: 

Organizational growth or cloud transformation 

If your company is undergoing digital transformation, cloud migration, or expanding into new markets, it is the right time to evaluate your observability strategy. Datadog integrates seamlessly with AWS, Azure, GCP, and Kubernetes, making it an ideal choice for hybrid and cloud-native environments. Migrating during periods of growth ensures that your monitoring infrastructure can scale with your business without creating new silos or complexity.

Persistent visibility gaps 

If your teams are constantly reacting to incidents without clear root causes or spending hours stitching together data from different monitoring tools, you are operating in a visibility deficit. A unified observability platform like Datadog allows teams to move from reactive to proactive, shortening mean time to resolution, improving uptime, and enhancing the user experience.

Stronger compliance and security needs 

When regulatory requirements increase or your organization faces greater scrutiny around data privacy and system security, real-time monitoring and robust audit trails become essential. Datadog supports compliance with industry standards through built-in security tools, automated logging, and customizable alerting to help meet frameworks such as HIPAA, PCI-DSS, and GDPR.

Rising operational costs 

If your current observability setup is causing tool sprawl, duplicated effort, or unmanageable infrastructure spending, it may be time to consolidate. Datadog helps reduce overhead by combining monitoring, logging, and security into a single platform. With improved resource visibility and usage-based pricing, teams can better track and control cloud spending while eliminating waste and redundancy.

Recognizing these inflection points can mean the difference between reactive firefighting and proactive performance management. When your team is ready to evolve beyond existing limitations, Datadog provides the infrastructure to support that progression securely, efficiently, and at scale.

How to Migrate

A structured and intentional migration strategy is necessary to maximize the return on investment, avoid common pitfalls, and ensure a smooth transition to Datadog across the entire organization. The following steps will help reduce risk, accelerate adoption, and unlock value early in the migration process.  

Assess your current environment

Before any migration, conducting a complete audit of your existing monitoring landscape is essential. This includes identifying the tools currently in use, mapping out critical systems, and highlighting any potential visibility gaps. This baseline helps define your migration roadmap and ensures that Datadog will enhance your observability strategy rather than duplicating efforts. 

Build the business case and secure buy-in

Leadership alignment is essential. Make the case by focusing on business outcomes like less downtime, faster incident response, and better team collaboration. It may be helpful to share relevant benchmarks and highlight long-term benefits, including stronger retention when training and support are prioritized. Don’t hesitate to engage stakeholders from operations, development, and security teams to build a shared understanding of current pain points and priorities before making significant changes.  

Define KPIs and success metrics

Before you migrate, set clear, measurable goals. These may include system uptime, mean time to resolution (MTTR), frequency of critical incidents, alert response time, and adoption rates across teams. Defining these metrics early on helps track progress, show impact to stakeholders, and identify where additional support may be needed.

Launch a pilot

Begin your migration in a controlled environment. Select a non-critical system, sandbox, or internal service where teams can safely test Datadog’s capabilities. Use this phase to experiment with alerting, dashboards, integrations, and tagging. Then, gather user feedback, identify what is working and needs adjustment, and use early adopters as internal champions who can support the broader rollout. 

Operationalize and scale

Once the pilot is validated, you can begin to expand thoughtfully. Start by automating agent deployment using infrastructure-as-code tools like Ansible, Terraform, or AWS CloudFormation. Then, standardize alerting rules, dashboard templates, and tagging conventions to ensure consistency across teams. After that, teams can embed observability into the current CI/CD pipelines and deliver role-specific training for engineers, SREs, analysts, and security professionals. This continuous enablement ensures that Datadog becomes a deeply integrated part of your workflows.

By following this step-by-step approach, organizations can avoid the pitfalls of rushed migrations and fragmented adoption. A well-executed migration doesn’t just implement a platform, but establishes a long-term foundation for observability.

Managing Expectations: What to Know Before You Start

A successful Datadog migration is a strategic rollout that takes time, coordination, and iteration. While the benefits of unified observability are substantial, teams may run into a few common pitfalls that can delay progress or limit adoption. But being aware of these challenges early on can help you stay ahead of them.

Common migration pitfalls to avoid may include:

To set up migration for long-term success, organizations should:

As your systems evolve, your observability strategy should too. Regularly review your dashboards, alerts, and tagging standards to ensure they remain aligned with current priorities and architecture. Building flexibility into your rollout plan and creating a feedback loop between users and administrators will ensure that Datadog grows with your organization, not around it.

Key Takeaways on Migrating to Datadog, the Right Way

The observability market is growing rapidly, with projections estimating a value of $6.2 billion by 2032. As digital operations become central to business success, migrating to a unified observability platform is not just a matter of if, but when.

Datadog stands out as a platform that not only centralizes metrics, logs, traces, and security insights but also empowers teams to respond faster, reduce downtime, and scale confidently. When implemented with a thoughtful strategy and supported by clear goals, training, and leadership buy-in, the migration to Datadog delivers long-term value across every layer of your infrastructure.

Ready to migrate? Strategic support starts with EverOps

As a trusted Datadog partner, EverOps helps companies move from complexity to clarity. Through our embedded TechPod model, we act as an extension of your team, bringing senior engineering talent, cross-functional alignment, and deep observability expertise to every stage of the migration.

Led by experienced Pod Leaders and backed by a network of industry veterans, EverOps pods are designed to flex with your priorities while maintaining the technical depth needed to avoid pitfalls, accelerate adoption, and align outcomes to business goals. From setting meaningful KPIs to managing alert fatigue and scaling data retention, we help you turn observability into a strategic asset.

If your team is ready to confidently make the migration to Datadog, EverOps is here to help. Contact us today to begin the migration to a more efficient, more secure infrastructure. Let’s simplify the process, drive value faster, and build a smarter observability foundation, together! 

Frequently Asked Questions 

What should I audit before migrating to Datadog?

Begin by reviewing your current monitoring tools, identifying visibility gaps, understanding alerting pain points, and mapping out critical infrastructure. This baseline helps shape a migration strategy that complements your existing architecture.

Can Datadog integrate with our existing tools and workflows?

Yes. Datadog supports over 800 integrations, including AWS, Azure, GCP, Kubernetes, Jenkins, Slack, PagerDuty, and Terraform. Its APIs and prebuilt dashboards allow you to extend observability without disrupting workflows.

Does Datadog help reduce alert fatigue?

Yes. Datadog supports anomaly detection, historical baselines, and correlation rules to ensure meaningful and actionable alerts. Role-based alerting helps limit noise by sending the correct information to the right teams.

What KPIs should we track to measure success post-migration?

Key metrics include system uptime, mean time to resolution (MTTR), alert response time, incident frequency, and adoption rates. Tracking these indicators helps demonstrate ROI and pinpoint areas for refinement.

How long does a typical Datadog migration take?

Timelines vary, but most organizations can expect an initial implementation period of 4 to 8 weeks. Full adoption across teams, including training, dashboard standardization, and alert tuning, generally takes 3 to 6 months, depending on complexity and scale.

When is the best time to migrate to Datadog?

Ideal times include during cloud transformation, platform scaling, persistent visibility challenges, security audits, or when operational costs are rising due to tool sprawl or inefficient monitoring stacks.

Why should we partner with EverOps for a Datadog migration?

As official Datadog partners, EverOps embeds directly into your team through its TechPod model, providing experienced engineering leadership, flexible technical support, and a proven migration framework. This ensures faster adoption, fewer missteps, and long-term value.

How do we get started with EverOps for Datadog migration?

Contact us today to schedule an initial consultation. Whether you are early in planning or facing roadblocks mid-migration, EverOps can step in to support your team from day one.