A Guide to Datadog Cloud Cost Recommendations

In most large organizations, there's a natural gap between engineering and finance. Engineers focus on building reliable, high-performance systems, while finance focuses on the bottom line. This can lead to tension when cloud bills spiral. The key to bridging this gap is shared visibility. Datadog's cloud cost recommendations provide a common language for both teams. By connecting technical decisions directly to their financial impact, the platform empowers engineers to become more cost-conscious. It helps everyone understand the trade-offs, turning cost optimization into a shared responsibility. This guide explains how to use these insights to foster a stronger FinOps culture and align your technology investments with your business goals.

Key Takeaways

• Treat cloud costs like a performance metric: Integrate financial data directly into the engineering dashboards your teams already use, making cost a visible and shared part of daily operations.
• Focus on foundational savings first: Use Datadog's automated analysis to pinpoint the most common sources of cloud waste, such as over-provisioned workloads and idle resources, giving you clear, actionable steps to reduce spend.
• Turn insights into action with automation: Create workflows that automatically alert teams, create tickets, or remediate issues, ensuring that valuable cost-saving recommendations are consistently implemented.

What Are Datadog's Cloud Cost Recommendations?

If you're using Datadog, you're likely already monitoring your infrastructure's performance. But what about its cost-effectiveness? Datadog's Cloud Cost Recommendations feature is built to answer that question, turning observability data into actionable financial insights. It helps you pinpoint where your cloud budget is going and how you can rein it in without sacrificing performance. The feature is designed to give you clear, data-backed suggestions for reducing your cloud spend across your environments.

How the recommendation engine works

Think of the recommendation engine as an automated financial advisor for your cloud environment. It continuously scans your AWS infrastructure to find practical ways to save money. Instead of making you hunt for this information, Datadog surfaces these recommendations directly on the dashboards your teams already use every day. This integration means your engineers can see cost-saving opportunities right alongside performance metrics, making it easier to connect operational decisions to financial outcomes. The goal is to make cost optimization a natural part of your team's workflow, not a separate, time-consuming task that gets pushed to the end of the quarter.

The types of insights you'll get

The recommendations you receive are more than just simple alerts; they're detailed insights based on a comprehensive analysis. Datadog combines your usage and performance metrics with your cost data to give you a full picture of your cloud spending. This allows you to spot inefficiencies that are easy to miss, like legacy services that are no longer needed, idle resources consuming budget, or over-provisioned workloads. A key benefit is the ability to get a unified view of your cloud spend across AWS, Azure, and Google Cloud. This multi-cloud perspective helps you identify waste and act on optimization opportunities without having to jump between different provider consoles, streamlining how you manage your overall cloud investment.

How Datadog Finds Cloud Cost Savings

Datadog combines several powerful techniques to uncover potential savings in your cloud environment. It doesn’t just look at your bill; it analyzes workload behavior, monitors spending in real time, and integrates directly with your cloud providers to give you a complete picture of your spending and where you can optimize. This multi-pronged approach helps you move from simply tracking costs to actively controlling them.

Using machine learning for cost analysis

Datadog’s recommendation engine isn’t just based on simple rules. It uses machine learning to analyze historical usage data and identify subtle inefficiencies. For example, it can automatically spot over-provisioned workloads and suggest new request values for CPU and memory. This approach helps you trim costs without compromising application performance. By learning your applications' typical behavior, the platform can make smarter recommendations than manual analysis alone, finding the perfect balance between resource allocation and budget. This is especially useful for complex, dynamic applications where resource needs fluctuate, ensuring you only pay for what you truly need.

Collecting data with real-time monitoring

One of the biggest challenges in managing cloud costs is the delay between spending and reporting. Datadog addresses this with its Cloud Cost Management (CCM) tools, which provide real-time insights into your spending as it happens. You can build custom dashboards to track key cost metrics across different teams, projects, or services. More importantly, you can set up alerts to notify you immediately of unusual spending spikes, allowing you to investigate and resolve issues before they turn into a major budget overrun. This proactive monitoring transforms cost management from a reactive, end-of-month exercise into an ongoing, dynamic process of optimization for your distributed data.

Integrating with cloud provider APIs

To provide accurate recommendations, Datadog integrates directly with the APIs of major cloud providers like AWS, Azure, and Google Cloud. This direct line of communication allows it to pull detailed billing and usage data, giving you a unified view of your entire multi-cloud spend in one place. Instead of juggling multiple consoles and reports, you get a single source of truth. Datadog even offers out-of-the-box dashboards and "Powerpacks" that group key cost widgets and display specific recommendations for services like Amazon EC2, S3, and DynamoDB. This deep integration is what enables a truly comprehensive approach to cost governance across your entire infrastructure.

Key Cost-Saving Strategies Datadog Recommends

Once Datadog has analyzed your environment, it will start flagging specific opportunities to cut costs. Most recommendations fall into a few key categories that target the most common sources of cloud waste. Think of these as your foundational pillars for building a more cost-efficient infrastructure. By focusing on these core strategies, you can address the low-hanging fruit and establish good financial hygiene across your teams. These aren't just one-time fixes; they're ongoing practices that help you maintain control over your cloud spending as your organization scales.

Understanding these recommendations is the first step, but acting on them consistently is what truly drives down costs. The platform provides the data, but it's up to your engineering and FinOps teams to turn those insights into action. This often involves creating new workflows, setting clear policies, and fostering a culture of cost awareness. The strategies Datadog suggests are universal principles of cloud financial management, and mastering them will pay dividends far beyond just your Datadog bill. They help you build more resilient, efficient, and predictable systems from the ground up. By implementing these suggestions, you're not just trimming fat; you're making a strategic investment in the long-term health and scalability of your entire tech stack.

Right-size overprovisioned resources

It’s incredibly common for teams to provision more computing power than an application actually needs, just to be safe. While well-intentioned, this "insurance policy" leads to significant waste. Datadog helps you tackle this by automatically identifying over-provisioned workloads. It analyzes actual usage and suggests new request values that trim your cloud costs while still giving your applications the CPU and memory they need to perform well. This is about paying only for what you use. By fine-tuning your instances, you can ensure your distributed data warehouse and other critical systems run efficiently without breaking the bank.

Eliminate unused and idle resources

Cloud environments can quickly become cluttered with forgotten resources—old test servers, unattached storage volumes, or idle database instances. These "zombie" assets sit in the background, quietly adding to your monthly bill. Datadog is great at finding these unused items so you can confidently delete them. For example, the platform might flag an unused Amazon RDS instance that, once removed, could save you over $1,800 a month. Regularly sweeping for and eliminating these idle resources is one of the fastest ways to see a tangible reduction in your cloud spend.

Optimize your storage

Data storage is another area where costs can spiral if left unchecked. Datadog makes it easier to manage this by surfacing storage cost data and recommendations directly on your team's dashboards. Using tools like its Cost Optimization Opportunities Powerpack, you can get specific advice for services like EBS, S3, and DynamoDB. This might involve moving infrequently accessed data to cheaper storage tiers or deleting outdated snapshots. This is especially critical for teams dealing with massive amounts of data from sources like log processing, where optimizing storage from the start can prevent major expenses down the line.

Use reserved instances and commitments

For workloads with predictable, long-term needs, committing to reserved instances (RIs) or savings plans can offer substantial discounts compared to on-demand pricing. The challenge is knowing which resources are safe to commit to. Datadog helps you make these decisions with confidence by using tags to attribute usage to specific teams or services. This visibility allows you to see stable, ongoing usage patterns that are perfect candidates for commitment-based discounts. It’s a strategic move that requires good forecasting, but the savings can be significant for your core, always-on applications.

Applying Recommendations Across AWS, Azure, and GCP

Managing costs in a multi-cloud environment can feel like you’re trying to balance three different checkbooks in three different currencies. Each cloud provider has its own services, pricing models, and optimization tools. Datadog helps by giving you a single, unified view of your spending across all of them, so you can spot savings opportunities without constantly switching between dashboards.

While the core principles of cost optimization—like eliminating waste and right-sizing resources—are universal, the specific tactics can vary from one cloud to the next. Let’s look at how you can apply Datadog’s recommendations to AWS, Azure, and GCP to get your spending under control. This approach is especially useful for organizations that need to process data efficiently, whether for log processing or large-scale analytics, as it ensures you’re not overspending on the underlying infrastructure. A centralized strategy helps you maintain financial governance and operational consistency, no matter where your workloads are running.

AWS-specific optimization strategies

Amazon Web Services is known for its vast portfolio of services, which also means there are many places for costs to hide. Datadog helps you cut through the noise by identifying inefficiencies in legacy services, unused resources, and over-provisioned workloads. It surfaces recommendations for some of the most common sources of AWS budget overruns, like idle EC2 instances, unattached EBS volumes, and underutilized S3 storage tiers. You can even pull this data directly into the dashboards your teams already use, making cost optimization a natural part of their daily workflow instead of a separate, dreaded task.

Azure cost reduction techniques

For teams running workloads on both AWS and Azure, one of the biggest challenges is the lack of a consistent view. Datadog’s cost recommendations solve this by providing a unified dashboard for all your cloud spending. This allows you to identify savings opportunities in your Azure environment using the same tool and metrics you use for AWS. You can spot underutilized virtual machines, optimize your storage accounts, and make better decisions about reserved instances. This single pane of glass is key for creating a standardized cost management practice that works across your entire organization, simplifying governance and reporting.

Google Cloud Platform savings opportunities

Just like with AWS and Azure, Datadog centralizes your Google Cloud spend so you can manage it from one place. The platform doesn’t just show you where you can save money; it helps you take action quickly. Using Datadog Native Actions, your team can implement recommendations—like shutting down idle projects or resizing machine types—with just a few clicks. This reduces the manual effort required to act on cost-saving insights, freeing up your engineers to focus on building better products. It’s a practical way to turn recommendations into real savings without disrupting your team’s momentum or existing cloud architecture.

Why You Should Use Datadog's Cost Recommendations

Using Datadog's cost recommendations is about more than just trimming your cloud bill. It’s about shifting from a reactive to a proactive stance on your cloud spending. When you have clear, automated insights at your fingertips, you can make smarter, data-driven decisions that align your infrastructure with your business goals. This approach helps you build a more efficient, predictable, and financially sound cloud environment. Let's look at the key reasons why these recommendations are so valuable for enterprise teams.

Gain financial visibility and transparency

It’s hard to control what you can’t see. One of the biggest challenges in managing cloud costs is connecting the dots between resource usage and your monthly bill. Datadog bridges this gap by combining performance metrics with your cost data, giving you a clear line of sight into your cloud spend. You can finally see exactly which services, teams, or projects are driving costs and why.

This level of transparency empowers your teams to understand the financial impact of their technical choices. When engineers can see how a specific configuration affects the budget, they can build more cost-aware applications from the start. This creates a culture of financial accountability and helps everyone make smarter decisions, turning cloud cost management into a shared responsibility rather than a top-down mandate.

Get automated cost optimization insights

Manually hunting for cost savings in a complex cloud environment is like looking for a needle in a haystack. It’s time-consuming and often requires deep expertise. Datadog’s Cloud Cost Recommendations automates this entire process. The platform’s engine constantly analyzes your environment to find opportunities to save money on your cloud services and presents them as clear, actionable steps.

This automation frees up your engineering and FinOps teams from tedious analysis, allowing them to focus on higher-value work. Instead of spending days sifting through billing reports, they receive a prioritized list of recommendations they can act on immediately. This accelerates your optimization cycle and ensures you’re continuously capturing savings without dedicating a full-time team to the task. You can explore the recommendations to see how they work in practice.

Improve resource allocation

Cloud waste often comes from two main culprits: over-provisioned resources and idle or "zombie" instances that are running but not doing any useful work. Datadog excels at identifying these inefficiencies across your infrastructure, from legacy services to forgotten test environments. The recommendations pinpoint exactly where you’re spending money on resources you don’t need, so you can either downsize them or shut them off completely.

Optimizing your resources isn't just about cutting costs; it's about reallocating capital to where it matters most. Every dollar saved on an oversized database or an unused virtual machine is a dollar you can invest in innovation, new features, or strategic AI projects. By right-sizing your environment, you ensure your cloud budget is directly supporting business growth, not just keeping the lights on for services that provide little value. This is a core principle of effective cloud resource management.

Forecast your budget more accurately

Unexpected spikes in your cloud bill can derail financial plans and create tension between technology and finance departments. Datadog's cost recommendations help you move beyond reactive budget management by providing granular insights into usage trends and cost anomalies. By understanding the patterns behind your spending, you can build more accurate and reliable budget forecasts.

This predictability is a game-changer for large enterprises. When you can confidently forecast your cloud spend, you can plan long-term projects, secure necessary funding, and avoid end-of-quarter budget scrambles. It transforms your cloud budget from a volatile operational expense into a predictable investment. This visibility is a cornerstone of a mature FinOps practice, enabling you to align your cloud strategy with your company's financial goals.

How to Automate Your Cost-Saving Actions

Getting a list of cost-saving recommendations is a great first step, but it’s only half the battle. The real value comes from acting on those insights quickly and consistently. Manually addressing every recommendation is time-consuming and prone to error, especially in large, complex environments. This is where automation comes in. By creating automated workflows, you can turn Datadog’s recommendations into tangible savings without adding significant overhead for your teams.

Automating your cost-saving actions ensures that opportunities aren’t missed and that your cloud environment remains optimized over time. It allows you to implement changes at scale, enforce cost-aware policies, and free up your engineers to focus on innovation instead of manual cleanup. From simple alerts to sophisticated, custom-built workflows, automation is the key to building a sustainable FinOps practice. Let's walk through how you can put these recommendations into practice.

Set up automated recommendation alerts

The first step in automation is making sure the right information gets to the right people at the right time. Instead of having engineers hunt for cost data, you can push recommendations directly into their existing tools. Datadog makes it easy to surface cloud cost data on the dashboards your teams already use. You can deploy out-of-the-box Powerpacks to display optimization opportunities for key services.

From there, you can configure alerts to notify specific teams via Slack, Microsoft Teams, or PagerDuty when a new cost-saving opportunity is identified. For example, you can create an alert that pages the on-call engineer for a specific service when an EC2 instance has been idle for more than 48 hours. This turns a passive recommendation into an actionable task that can be addressed immediately.

Integrate with your existing workflows

Effective cost management happens when it’s embedded in your team’s daily routines. Beyond simple alerts, you can integrate Datadog’s recommendations into your project management and CI/CD tools. For instance, you can configure a workflow that automatically creates a Jira ticket and assigns it to the appropriate team when Datadog flags an overprovisioned database. This ensures the task is tracked, prioritized, and resolved.

For businesses running in multi-cloud environments, Datadog’s cost monitoring tools simplify the challenge of managing spending across different providers. By integrating these insights into your standard operational workflows, you make cost optimization a shared responsibility. It becomes a natural part of the development lifecycle rather than a separate, periodic review process that slows everyone down.

Use custom automation tools

For more advanced scenarios, you can build custom workflows to automatically remediate common cost issues. Datadog Workflow Automation allows you to create custom, event-driven playbooks that execute a series of actions in response to a trigger. For example, you could build a workflow that automatically evaluates your services for proper team tag coverage to improve cost allocation.

Imagine a workflow that detects an unattached EBS volume, verifies it hasn't been accessed in 30 days, creates a snapshot as a backup, and then deletes the volume—all without human intervention. These custom tools empower you to enforce your organization’s governance policies automatically. This not only saves money but also reduces risk and ensures your environment stays clean and efficient.

Monitor for continuous optimization

Automation isn’t a "set it and forget it" solution. It’s part of a continuous feedback loop. After you automate a cost-saving action, you need to monitor its impact. Did downsizing a resource affect application performance? Did your spending in that area actually decrease as expected? Using dashboards and scorecards helps you track the effectiveness of your automation efforts.

This practice of continuous optimization ensures your automated workflows are delivering the intended results without causing unintended consequences. By regularly reviewing performance and cost metrics, you can fine-tune your automation rules and identify new opportunities for improvement. This iterative approach helps you build a resilient and cost-effective cloud infrastructure that evolves with your business needs.

How to Measure Your Cost-Saving Success

Implementing Datadog's cost recommendations is a huge step, but your work isn't done yet. To build a truly cost-conscious engineering culture, you need to measure the impact of your changes. Tracking the right metrics proves the value of your efforts to leadership and helps you refine your optimization strategy over time. It turns cost management from a one-time project into a continuous, data-driven practice. Here are the key performance indicators you should be monitoring.

Track cloud spend reduction

This is the most straightforward metric for success. You need to see your cloud bills actually going down. Datadog makes it easy to visualize this by providing dashboards that show your spending trends over time. You can use the Cloud Cost Analytics page to get a detailed breakdown of where your money is going and how that changes after you implement a recommendation. For a deeper analysis, you can export this data and compare it against historical spending. Set up a recurring report to share with stakeholders so everyone can see the direct financial impact of your optimization work.

Monitor resource utilization improvements

True optimization isn't just about cutting costs—it's about eliminating waste and improving efficiency. Are your teams using resources more effectively? Datadog helps you answer this by tracking utilization metrics for CPU, memory, and storage. After right-sizing instances based on recommendations, you should see utilization rates improve without harming application performance. The platform can automatically identify over-provisioned workloads and suggest better values, ensuring you pay only for what you truly need. This shift from "spend less" to "spend smarter" is a critical step in maturing your cloud financial management.

Check for budget variance

Effective cost management requires a plan. Once you set a budget for your cloud spending, you need to track how your actual costs compare. This is known as budget variance analysis, and it’s a core practice of FinOps. A significant variance can signal unexpected usage spikes or that cost-saving initiatives aren't being followed. According to one report, a staggering 84% of organizations struggle to manage cloud spend, making proactive budget tracking essential. Use Datadog to set spending alerts and monitor your run rate, allowing your team to make adjustments before you go over budget, not after.

Measure recommendation implementation rates

Datadog can surface hundreds of cost-saving recommendations, but they only create value if your teams act on them. That's why you should track your implementation rate—the percentage of recommendations that are reviewed and applied. A low rate might indicate process bottlenecks, a lack of clear ownership, or technical challenges. You can even use Datadog Workflow Automation to monitor related governance practices, like ensuring services are properly tagged for cost allocation. Tracking this metric helps you identify and resolve friction in your optimization workflow, ensuring that good advice consistently turns into real savings.

Overcoming Common Implementation Challenges

Getting a list of cost-saving recommendations from Datadog is a great first step, but turning those insights into actual savings is where the real work begins. In a large enterprise, you can’t just flip a switch. Acting on a recommendation often involves untangling complex dependencies, getting buy-in from multiple teams, and navigating intricate cloud pricing models. It’s a process that requires careful planning and coordination to avoid disrupting critical services. Let's walk through some of the most common hurdles you'll face and how to clear them.

Consider resource dependencies

Before you act on a recommendation to downsize or terminate a resource, you need to know what else relies on it. A single virtual machine might be supporting multiple applications, or a storage bucket could be a critical data source for an analytics pipeline. Simply shutting it down based on a low utilization report could cause a cascade of failures. Gaining true visibility into these relationships is key. You need a clear map of your architecture to understand the full impact of any change, ensuring that a cost-saving measure for one team doesn’t create a production outage for another.

Assess performance impact

Datadog’s recommendations are designed to reduce costs while maintaining performance, but it’s always wise to verify. Right-sizing an over-provisioned workload can save money, but getting it wrong can lead to slow applications and a poor user experience. Before implementing a change, establish a performance baseline. After the change, monitor closely to ensure you haven't negatively impacted response times or violated any service level agreements (SLAs). The goal is to find the perfect balance between efficiency and reliability, which requires thoughtful, data-driven log processing and analysis rather than blindly applying every suggestion.

Coordinate team and approval processes

In most organizations, cloud cost management is a team sport. A recommendation might be identified by the FinOps team, but it needs to be implemented by DevOps, approved by an application owner, and understood by Finance. Without a clear process, recommendations can get stuck in limbo. Establishing a workflow for review, approval, and implementation is critical. This creates a shared sense of accountability and ensures that decisions align with broader business goals. When everyone understands their role and the security and governance policies, you can move from insight to action much faster and more effectively.

Manage complex pricing models

Cloud pricing is notoriously complex. A recommendation to move data to a cheaper storage tier or a different region might seem straightforward, but hidden costs like data egress fees can quickly erase any potential savings. Each cloud provider has its own unique pricing for services, which can vary significantly based on factors like region and usage patterns. To make informed decisions, your team needs a solid understanding of these pricing models. This allows you to accurately forecast the financial impact of a change and avoid any unpleasant billing surprises down the road.

Why Datadog Works for Enterprise Cost Management

Managing cloud costs in a large enterprise is a complex puzzle. You’re not just tracking a few virtual machines; you’re dealing with intricate architectures spread across multiple cloud providers, dozens of teams, and strict regulatory requirements. This is where a platform designed for complexity can make a real difference. Datadog’s cost management tools are built to handle this scale, offering specific features that address the unique challenges large organizations face, from fragmented visibility to governance and compliance. It provides a framework for getting a handle on your spending without disrupting the workflows your teams already rely on.

Get multi-cloud visibility

Most enterprises don’t operate in a single cloud environment. Your teams might use AWS for its data services, Azure for its enterprise integrations, and Google Cloud for its machine learning capabilities. Datadog brings all of that spending data together, giving you a unified view of your cloud spend without forcing you to jump between different billing consoles. This consolidated view is the first step toward understanding your total cost of ownership and identifying global patterns. Instead of analyzing your cloud bills in silos, you can compare costs and pinpoint inefficiencies across your entire infrastructure from a single, consistent dashboard.

Integrate with existing monitoring tools

Adopting a new tool shouldn't mean overhauling your entire operational workflow. Datadog’s Cloud Cost Management (CCM) is designed to fit into your existing ecosystem. It allows you to surface cost data and recommendations directly on the monitoring dashboards your engineering and operations teams already use every day. This seamless integration makes cost a visible, shared metric rather than an isolated concern for the finance department. By embedding cost insights into daily operations, you empower your teams to monitor and manage cloud spending proactively as part of their regular performance tuning and incident response activities.

Address compliance and governance

For enterprises in regulated industries like finance and healthcare, cost management is closely tied to governance and compliance. You need to know who is spending what, where, and why. Datadog provides the detailed visibility required to enforce internal policies and meet external compliance mandates. By creating a shared sense of accountability, it supports a FinOps culture where financial discipline is a collective responsibility. This makes it easier to allocate costs accurately, justify technology investments, and ensure that your cloud usage aligns with both your budget and your broader business objectives.

Scale for large organizations

Enterprise-grade solutions need to handle enterprise-grade data, and Datadog is built for that level of scale. Whether you have thousands of hosts or process petabytes of data, its platform can handle the load. Datadog makes it easy to surface cloud cost data and recommendations through tools like the out-of-the-box Cost Optimization Opportunities Powerpack. This feature groups key cost-saving widgets and recommendations for services across your cloud providers, allowing you to quickly deploy pre-configured dashboards. It ensures that as your organization grows, your ability to manage costs effectively grows with it.

How to Get Started with Datadog's Cost Recommendations

Putting Datadog's cost recommendations into practice is a clear, three-step process. It starts with connecting your data, then establishing a clear picture of your current spending, and finally, building an automated workflow to act on the insights you uncover. This approach helps you move from simply observing costs to actively managing them, turning raw data into real savings for your organization. Let's walk through how to get it done.

Complete the initial setup and configuration

First, you need to get your cloud cost data flowing into Datadog. The platform is designed to surface this information on the dashboards your teams already use, which helps with adoption. You can start by deploying the out-of-the-box Cost Optimization Opportunities Powerpack. This is a great shortcut that groups key cloud cost widgets and immediately displays recommendations for services like EBS, EC2, and S3. The goal is to integrate cost visibility directly into your existing engineering and operations views, making it a natural part of your team's process rather than a separate, isolated task. This seamless integration is key for building cost-aware solutions across your infrastructure.

Establish your baseline metrics

Once your data is connected, the next step is to understand your starting point. You can't measure improvement without a baseline. Datadog ingests your cloud cost data and converts it into metrics that you can query on the Explorer page. This allows you to see exactly where your money is going, identify the most expensive services, and spot initial trends. This visibility is crucial for gaining control over your cloud spend. For organizations dealing with massive data volumes, it's also worth considering how to optimize costs before the data even reaches your monitoring platform. Reducing noisy or duplicate logs at the source is a powerful way to lower your overall log processing expenses.

Create your optimization workflow

With a baseline established, you can build a repeatable process for continuous optimization. This is where you move from one-time fixes to a sustainable FinOps practice. You can use Datadog Workflow Automation to automatically check for things like proper resource tagging. For example, you can set up a workflow that evaluates whether all services are using the team tag correctly, which is essential for accurate cost allocation. This ensures accountability and helps you maintain strong security and governance over your cloud environment. By automating these checks and actions, you create a system that consistently finds and flags opportunities for savings with minimal manual effort.

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Frequently Asked Questions

Will acting on these recommendations affect my application's performance? This is a valid concern, and the short answer is that the recommendations are designed to avoid performance issues. Datadog analyzes actual usage data to suggest changes, like right-sizing, that align resources with what your application truly needs. However, it's always a good practice to monitor your application's key performance metrics after implementing a change. The goal is to find the sweet spot where you're running efficiently without sacrificing the reliability your users expect.

How is this different from the cost-saving tools my cloud provider offers? While tools from AWS, Azure, or GCP are useful, they typically only show you one piece of the puzzle. Datadog's main advantage is that it brings everything together in one place. It gives you a single view of your spending across all your cloud providers and, more importantly, connects that cost data directly to performance metrics. This allows you to understand the "why" behind your spending and make more informed trade-offs between cost, performance, and reliability.

How much effort is required to start seeing results? You can start seeing potential savings quite quickly. The initial setup involves connecting your cloud accounts, and you can use pre-built dashboards called Powerpacks to get immediate visibility into low-hanging fruit like unused or idle resources. Addressing these can provide a fast return. More complex optimizations, like re-architecting a service for efficiency, will naturally take more time, but the platform gives you a clear, prioritized list to work from.

My company uses multiple clouds. Can Datadog handle that? Yes, absolutely. This is one of the core strengths of using Datadog for cost management. It's built to handle multi-cloud environments by consolidating your spending data from AWS, Azure, and Google Cloud into a single, unified dashboard. This saves your team from having to switch between different billing consoles and helps you create a consistent

Do I need a dedicated FinOps team to manage this? While a dedicated FinOps team can certainly get a lot of value from these tools, they aren't a prerequisite. Datadog is designed to make cost data accessible and understandable for engineering teams. By surfacing recommendations directly in the dashboards your developers and operations teams already use, it helps embed cost awareness into their daily workflows. This empowers everyone to take ownership of their spending and builds a culture of financial accountability from the ground up.