6 Best Datadog Cost Optimization Tools to Use Now

Controlling your Datadog spend isn't about turning off monitors and flying blind. It’s about spending smarter. True optimization means eliminating waste, not visibility, and that starts with controlling your data volume at the source. Many organizations pay a premium to ingest and store noisy, low-value data that doesn't contribute to critical alerts or dashboards. By implementing a strategy to filter, aggregate, and transform data before it ever leaves your environment, you can dramatically lower costs while improving the quality of the insights you receive. This guide will cover everything from native Datadog features to the best datadog cost optimization tools that enable this intelligent, source-level processing, putting you back in control of your budget.

Key Takeaways

• Process Data at the Source: Slash ingestion fees by filtering, aggregating, and transforming logs and metrics in your own environment. This sends a smaller, higher-value dataset to Datadog, cutting costs without sacrificing critical insights.
• Give Engineers Financial Context: Go beyond basic dashboards by using tools that connect technical activity directly to financial impact. When engineers can see the real-time cost of their code, they are empowered to build more efficiently from the start.
• Embed Cost Management into Your Workflow: Treat cost optimization as an ongoing practice, not a one-time fix. Create a system of clear governance, real-time alerts, and team-specific budgets to make financial accountability a core part of your engineering culture.

Why Your Datadog Bill is So High (and What to Do About It)

If you’ve ever opened your monthly Datadog bill and felt a jolt of surprise, you’re not alone. It’s one of the most powerful observability platforms available, but that power comes with a complex pricing model that can easily lead to sticker shock. The costs aren't arbitrary; they're a direct result of how you use the platform. Every host you monitor, every gigabyte of logs you ingest, and every custom metric you track contributes to the final number.

The core issue is often data volume. Many teams send everything to Datadog by default, creating a flood of noisy, redundant, or low-value data that inflates ingestion and storage fees. Datadog's costs are tied directly to its usage-based services, meaning expenses can quickly add up, especially for storing logs or monitoring a large number of containers. This isn't just a budget problem—it's an efficiency problem. Your engineers spend valuable time managing brittle pipelines, and you pay a premium to store data that might not even be useful for analytics or troubleshooting.

The good news is that you can regain control without sacrificing the visibility your teams rely on. The solution involves a smarter approach to your data. By implementing strategies to filter, aggregate, and process data before it ever reaches Datadog, you can significantly cut down on volume. This means you only pay to store and analyze the clean, high-value data that matters. Adopting a distributed computing approach allows you to handle this processing at the source, making your entire data pipeline more efficient and cost-effective. The following sections will walk you through specific, actionable steps to make that happen.

How Does Datadog Pricing Actually Work?

If you’ve ever been surprised by a Datadog bill, you’re not alone. The platform’s pricing model is powerful because it’s flexible, but that same flexibility can make it incredibly complex to predict and control. Your final bill isn’t based on a single metric; it’s a blend of several factors that can each scale independently. The total cost depends on which services you use, how many servers you monitor, how much data you send, and—crucially—how long you decide to keep it. For large enterprises with dynamic, multi-cloud environments, these variables can fluctuate wildly, making budget forecasting a serious challenge. Many teams find themselves in a reactive position, trying to understand a massive bill after the fact rather than proactively managing the costs as they accrue. This lack of predictability can strain budgets and create friction between engineering and finance teams. Getting a handle on these moving parts is the first step to reining in your spending. To do that, you need to understand the three biggest drivers of your bill: host-based fees, data retention policies, and the sheer volume of custom metrics and logs your systems generate. By dissecting each of these components, you can start to identify the specific areas where costs are spiraling and develop a targeted strategy to bring them back in line without sacrificing the visibility your teams need.

The Deal with Host-Based Pricing

A core component of your Datadog bill is its per-host pricing for infrastructure monitoring. You pay a set rate for each host you monitor, which sounds simple enough. However, the definition of a "host" can include physical servers, virtual machines, and even a certain number of containers. In dynamic cloud environments that rely on autoscaling and container orchestration, the number of billable hosts can fluctuate dramatically throughout the month. A temporary spike in traffic that spins up dozens of new instances can lead to a permanent spike in your bill if you’re not careful. This model requires you to maintain constant awareness of your infrastructure's scale to avoid unexpected charges. You can find the specific rates on Datadog's pricing page, but the real challenge is managing the underlying usage.

How Data Retention and Storage Add Up

The costs don't stop once your data is ingested. Another key factor is how long you store your metrics and logs, as Datadog charges progressively more for longer retention periods. While keeping data for a few days or weeks might be included in the base price, holding onto it for months or years for compliance or long-term trend analysis comes at a premium. For enterprises in regulated industries like finance, healthcare, or government, long-term data retention isn't optional—it's a requirement. These escalating storage fees can quickly become a significant and recurring expense. Failing to create and enforce clear data retention policies means you could be paying to store low-value data that you no longer need.

The Impact of Custom Metrics and Log Volume

Custom metrics and log volume are often the biggest sources of runaway Datadog costs. Custom metrics give you deep, application-specific insights, but they are one of the most expensive data types you can send to the platform. Many applications and integrations are configured to send a high volume of these metrics by default, leading to bill shock if they aren't properly managed. Similarly, the more log data you ingest, the more you pay. Noisy applications, debug-level logging in production, and duplicate data streams can inflate your log volume and your bill. To get costs under control, you have to be deliberate about what data you send and reduce metric cardinality wherever possible.

Use Datadog's Built-In Cost Management Features

Before you look at third-party tools, your first step should be to make the most of what Datadog already offers. The platform has several built-in features designed to give you a clearer picture of your spending. While they may not solve every cost challenge, especially around massive data ingest volumes, they provide a critical baseline for understanding where your money is going.

Think of these tools as your command center for initial cost reconnaissance. They help you attribute spending to the right teams, get alerts on budget overruns, and make smarter decisions about data retention. Mastering these native features is a foundational step that gives you the data you need to justify more advanced optimization strategies later on. By starting here, you can pick the low-hanging fruit and build a business case for any additional tools or architectural changes you might need.

Use Dashboards for Cost and Usage Attribution

You can’t control what you can’t see. Datadog’s Cloud Cost Management dashboards are designed to solve this visibility problem by bringing your performance metrics and cloud spending into a single view. This allows you to directly connect technical activity to financial impact.

The real power here is in cost attribution. You can use these dashboards to break down expenses by team, service, or project, which is essential for creating accountability. When an engineering team can see exactly how their new feature is affecting the cloud bill, they’re more likely to build with cost-efficiency in mind. Make it a regular practice to review these dashboards with team leads to identify which services are driving the most cost and start a conversation about optimization.

Activate Automatic Savings Suggestions and Budgets

Datadog doesn’t just show you where you’re spending money; it also offers automated advice on how to spend less. The platform provides automatic savings suggestions for your infrastructure on major cloud providers like AWS, Azure, and Google Cloud. Activating these recommendations can help you quickly find and fix inefficient resource allocation without a ton of manual analysis.

Beyond suggestions, you can set hard budgets for your cloud and SaaS spending and assign them to the appropriate teams. This proactive approach helps prevent bill shock at the end of the month. When paired with alerts, your teams will get notified as soon as costs begin to trend over budget, giving them a chance to course-correct before it becomes a major issue.

Control Data Retention and Log Management Settings

One of the biggest hidden costs in any monitoring platform is data storage. Datadog charges progressively more the longer you retain logs and metrics, so your data retention policies have a direct and significant impact on your monthly bill. It’s critical to find the right balance between keeping the data you need for compliance and troubleshooting and avoiding paying for storage you don’t.

Review your log management settings and retention policies regularly. Ask your teams if they truly need 90 days of retention for every single log source, or if 30 days would suffice for less critical applications. Aligning your retention policies with both your business needs and compliance requirements is a simple but highly effective way to trim your Datadog expenses.

Go Beyond Datadog: The Best Third-Party Cost Tools

While Datadog’s native features provide a solid starting point for managing expenses, they often don’t tell the whole story. When you’re dealing with massive data volumes and complex, multi-cloud environments, you need more specialized tools to get a true handle on your spending. Third-party platforms can offer deeper visibility, more powerful data processing capabilities, and analytics tailored to specific parts of your tech stack.

Think of these tools not as replacements for Datadog, but as powerful additions to your cost optimization toolkit. Some help you shrink your data footprint before it ever reaches Datadog, while others give your engineering and finance teams a shared language for talking about cloud costs. By combining Datadog’s monitoring capabilities with the specialized functions of these platforms, you can build a much more effective and sustainable cost management strategy.

Expanso: Cut Data Processing Costs with Distributed Computing

A huge portion of your Datadog bill comes from ingesting, indexing, and storing massive volumes of raw log data. What if you could process that data more efficiently before sending it to be indexed? That’s the idea behind Expanso. By using a distributed computing approach, you can process data directly at its source—whether that’s in a different cloud, an on-premise data center, or at the edge. This allows you to filter out noise, aggregate metrics, and transform logs right where they’re generated.

The result is a much smaller, higher-signal dataset sent to Datadog, which can dramatically lower your ingest and storage costs. This approach is especially useful for global enterprises that need to manage data residency and compliance requirements. With Expanso’s distributed log processing, you can ensure sensitive information is handled correctly in its region of origin while still getting the observability you need.

CloudZero & nOps: Gain Engineering-Driven Cost Visibility

To truly control costs, your engineers need to see the financial impact of their work. Tools like CloudZero and nOps are built to provide this exact kind of visibility, helping you build a culture of cost awareness. According to one source, "CloudZero provides insights into cloud costs, allowing teams to break down spending by customer, product, or feature, and alerting users to unexpected cost changes." This helps your teams understand the cost of a specific feature, not just a generic server bill.

Similarly, nOps helps teams find savings by giving them a clear view of their spending patterns and highlighting areas for improvement. These platforms shift cost management from a reactive, finance-led activity to a proactive, engineering-driven one. When developers can immediately see the cost implications of their code, they’re empowered to make smarter, more efficient choices from the start.

Finout, Moesif & Coralogix: Get Advanced Cost Analytics

For organizations with complex, modern architectures—especially those using AI and microservices—standard cost dashboards may not be enough. That’s where advanced analytics tools come in. Finout, for example, gives you a unified view of your cloud spend, helping you "analyze spending across various services and optimize your budgets effectively." It’s designed to untangle the complexity of your entire cloud bill, not just one platform.

Other tools focus on specific, high-cost areas. Moesif is an API analytics platform that helps you track the cost of third-party AI services by monitoring usage and spend on LLM and GenAI APIs. Coralogix uses real-time anomaly detection to spot unexpected spending spikes as they happen, allowing you to react quickly before costs spiral out of control. These tools provide the deep, granular insights needed to manage the financial side of cutting-edge technology.

Set Up Effective Cost Monitoring and Alerts

You can’t optimize what you can’t see in real time. Waiting for the monthly invoice to understand your spending is like trying to drive by looking only in the rearview mirror. Effective cost management isn’t about reacting to a surprisingly high bill; it’s about creating a system that alerts you to potential issues the moment they happen. This proactive approach allows your team to investigate and resolve problems before they turn into significant budget overruns.

Setting up a robust monitoring and alerting strategy is one of the most impactful steps you can take to get your Datadog costs under control. It shifts the responsibility of cost-consciousness from a centralized finance or FinOps team to the engineers who are building and running the services. By providing clear, immediate feedback on the cost implications of their work, you empower them to make smarter, more efficient decisions. The goal is to create a feedback loop where cost data is just as important as performance metrics. Let’s look at a few practical ways to build this system.

Create Spike Alerts for Unexpected Costs

Think of spike alerts as your financial smoke detector for cloud spending. These automated notifications fire when your costs suddenly jump beyond a normal, predictable pattern. A sudden surge in spending is rarely a good thing; it often points to a buggy deployment, a misconfiguration, or an unexpected traffic pattern that needs immediate investigation. Without an alert, a small issue could quietly drain your budget for days or weeks before anyone notices.

By setting up Datadog monitors, you can get notified via Slack, email, or PagerDuty the moment spending deviates from the baseline. This allows your team to jump on the issue right away, find the root cause, and fix it. This isn't about micromanaging—it's about creating a safety net that protects your budget from unforeseen events and helps maintain predictable cloud costs.

Monitor Log Usage and Traffic Patterns

Logs are frequently one of the biggest and most unpredictable drivers of your Datadog bill. Monitoring your log ingestion volume is critical, but it’s not just about watching the total number. You need to understand the patterns. Which services are the noisiest? Did a recent update cause a specific application to start generating ten times more logs than usual?

You can set up alerts that trigger when your indexed log volume crosses a certain threshold or when traffic from a particular source spikes unexpectedly. This proactive monitoring helps you spot inefficiencies and potential application issues early. Following best practices for log management and keeping a close eye on these patterns ensures you’re only paying to store and index the logs that provide real value.

Assign Budgets for Team Accountability

Cost optimization becomes much more effective when it’s a shared responsibility. Assigning budgets and attributing costs to specific teams, products, or services is essential for creating accountability. When an engineering team can see the direct financial impact of the services they manage, they are naturally incentivized to operate more efficiently. This transforms cost management from an abstract financial exercise into a tangible engineering problem to be solved.

Tools like Datadog Cloud Cost Management can help you allocate these costs accurately, even in complex containerized environments where resources are shared. By giving each team visibility into their spending against a set budget, you foster a culture of ownership. This simple act of tracking and reporting empowers teams to make informed trade-offs between performance and cost on their own.

How to Control Log Volume Costs

Log data is often the biggest culprit behind a surprisingly high Datadog bill. Every line from every application, server, and service adds up quickly, and before you know it, you’re dealing with massive ingestion volumes. The good news is that you can get these costs under control without sacrificing the visibility you need. It’s not about collecting less data, but about being smarter with the data you collect and where you process it. By focusing on filtering, edge processing, and smart retention, you can significantly reduce your log ingestion and storage expenses.

Apply Log Filtering and Sampling

A simple but effective first step is to stop sending every single log to Datadog. Much of the data from stable applications, like debug or informational logs, doesn't need to be indexed and stored in a premium platform. By implementing log filtering and sampling, you can ensure only the most relevant logs—such as errors or critical warnings—are ingested. You can configure your logging agents to drop noisy, low-value logs at the source. This approach immediately reduces the data volume you send over the network and pay to have indexed, making it a quick win for cost reduction.

Process Data at the Edge

Instead of sending massive volumes of raw data to a central platform for processing, you can handle it closer to where it’s created. Processing data at the edge allows you to filter, aggregate, mask, and enrich logs before they ever leave your environment. This means you send a smaller, higher-value dataset to Datadog, cutting ingestion and storage costs. This distributed approach is also ideal for meeting strict data residency and compliance requirements, as sensitive information can be handled locally without crossing borders. It gives you control over your data pipeline right from the start, turning a costly firehose into a manageable stream of valuable insights.

Optimize Retention Policies and Archive Strategies

Not all logs need to be instantly searchable for months on end. Your retention policies in Datadog have a direct impact on your storage costs. Take a close look at how long you’re keeping logs in hot, indexed storage and ask if it’s necessary. You can set different retention periods for different types of logs, keeping critical security logs longer while reducing the window for less important ones. For long-term storage and compliance, you can automatically archive logs to a more cost-effective solution like Amazon S3 or Google Cloud Storage. This tiered storage strategy gives you the right balance of accessibility and cost efficiency.

Optimize Custom Metrics Without Sacrificing Visibility

Custom metrics are fantastic for getting granular, application-specific insights, but they are often a primary driver of your sky-high Datadog bill. The goal isn’t to stop using them entirely—it’s to get strategic. Managing custom metrics doesn’t have to be a daunting task. With the right approach, you can efficiently manage your metrics, reduce costs, and ensure you’re only paying for the data that truly matters to your organization.

This isn't about flying blind; it's about focusing your visibility on what's most important. By being more intentional with metric selection, managing the data you send, and considering how different tools fit into your observability stack, you can rein in costs without sacrificing the insights your teams rely on. It starts with asking critical questions about what you’re tracking and why, and then implementing a few key practices to keep things in check.

Strategically Select and Consolidate Metrics

The first step is to treat your custom metrics like a curated collection, not a data dump. Not every piece of data needs to be a real-time, billable metric. Start by conducting a thorough audit with your engineering teams. Go through your metrics and ask the hard questions: Is this metric tied to a critical SLO? Does it power an essential dashboard? If a metric isn't actively used for alerting or decision-making, it’s a candidate for removal. You might find that many metrics were created for a one-time debug session and never touched again. To further control costs, look for opportunities to consolidate metrics. Instead of using multiple unique metric names, use one with different tags to reduce billable usage.

Manage Metric Cardinality and Your Tagging Strategy

High cardinality is the silent budget killer in Datadog. In simple terms, cardinality is the number of unique combinations of tags for a given metric. When you use tags with highly unique values—like user IDs, session IDs, or container IDs—you create a massive number of unique time series, and your bill explodes. The key is to reserve metrics for low-cardinality, aggregated data. High-cardinality data belongs in logs, where it can be searched when needed without incurring the same cost. By reducing the volume of high-cardinality data you send as metrics, you cut ingestion costs directly. Establish a clear, documented tagging policy for your teams to prevent accidental cardinality spikes and keep your metric usage predictable and affordable.

Consider Alternative Monitoring Approaches

While Datadog is a powerful platform, it doesn’t have to be your only tool. Some organizations explore building their own monitoring stack with open-source tools like Prometheus and Grafana. However, this approach isn't truly "free"—it requires significant engineering time and expertise to build and maintain, which can end up costing more than a SaaS solution. A more practical strategy is a hybrid model. You can use a distributed computing platform to pre-process, filter, and aggregate metrics at the source. This allows you to send only the most critical, low-cardinality data to Datadog for real-time alerting while handling less urgent analysis elsewhere. Expanso’s distributed data processing capabilities are perfect for this, enabling you to reduce data volume before it ever hits your observability platform, giving you the best of both worlds: deep visibility and controlled costs.

Proven Strategies for Reducing Datadog Expenses

Beyond specific tools, a few foundational strategies can make a significant impact on your Datadog bill. These approaches focus on changing how you monitor, not just what you use to do it. By being more intentional about your infrastructure, commitments, and data collection, you can create sustainable savings without compromising on the visibility you need to run your business. Let's walk through three of the most effective strategies you can implement right away.

Right-Size Your Monitoring Infrastructure

One of the quickest ways costs can spiral is by paying to monitor resources you don’t actually need. Right-sizing is all about aligning your monitoring footprint with your real-time operational requirements. Start by regularly auditing your environment. Are you paying for host monitoring on idle development servers or non-critical staging instances? Use Datadog’s dashboards to get a clear view of your spending broken down by product, team, or service. This helps you attribute costs and pinpoint exactly where your budget is going. By trimming this excess, you ensure you’re only paying for the monitoring that provides genuine value.

Implement Committed Use Discounts

If your usage is relatively stable, you can often get a better price by committing to a certain level of spending. Datadog, like many SaaS providers, offers discounts for annual commitments or upfront payments on services like host and container monitoring. Before you sign a long-term contract, analyze your past 6-12 months of usage data to create an accurate forecast. Committing to too much can lead to waste, but a well-calculated commitment based on your baseline needs can lock in significant savings compared to on-demand pricing. It’s always a good idea to discuss these committed use plans with your Datadog account manager to find the best fit for your organization.

Optimize Data Collection and Sampling Rates

Not all data is created equal, and you shouldn’t pay to ingest and store data that offers little value. Be strategic about what you send to Datadog in the first place. For logs, this means filtering out noisy, low-value entries and sending only what’s necessary for your observability goals. For APM traces, implement intelligent sampling to capture representative data without collecting every single trace. A powerful way to manage this is to process data at the edge, close to the source. This allows you to filter, mask, and aggregate data before it ever reaches Datadog, drastically reducing the volume you ingest and pay for while maintaining critical insights.

Common Cost Optimization Mistakes to Avoid

When you see a six-figure Datadog bill, the first instinct is often to start cutting things—anywhere and everywhere. But moving too quickly without a clear strategy can backfire, creating blind spots in your observability and putting your systems at risk. True cost optimization isn't about slashing your budget; it's about spending smarter. Let's walk through a few common missteps teams make and how you can steer clear of them.

Mistake #1: Cutting Costs Too Aggressively

It’s tempting to make deep cuts to your monitoring services to see an immediate drop in spending. The problem is, this often makes it much harder to see what’s happening with your systems. Blindly turning off monitors or reducing data collection can leave your engineering teams in the dark during a critical incident. Instead of just cutting, focus on optimizing. This means finding intelligent ways to reduce data volume without losing the crucial insights you rely on. The goal is to eliminate waste, not visibility, ensuring your team has the information it needs to keep services reliable and performant.

Mistake #2: Ignoring Usage Controls and Metric Limits

Custom metrics are powerful, but they can become a major source of cost overruns if they aren't managed carefully. Each unique combination of a metric name and its tags creates a new time series that you have to pay for. Without proper governance, development teams can accidentally create thousands of high-cardinality metrics that cause your bill to skyrocket. Managing custom metrics doesn't have to be a huge chore. By implementing clear usage controls and strategies for processing logs and metrics before they even hit Datadog, you can ensure you’re only paying for the data that truly matters to your business.

Mistake #3: Poor Communication Between Teams

Cost optimization often fails when it’s treated as just an engineering problem or just a finance problem. When technical and financial teams don't communicate, engineers may not understand the cost implications of their monitoring choices, while finance leaders may not grasp the business risk of cutting certain services. The best results happen when these teams work together. Creating shared dashboards and holding regular reviews can build a common understanding of spending and value. This collaborative approach helps everyone make better decisions and aligns your security and governance goals with your financial ones.

Build a Sustainable Cost Optimization Strategy

Getting your Datadog bill under control isn’t a one-time project; it’s about building a lasting practice. A sustainable strategy moves your organization from reactive, frantic cost-cutting to proactive, intelligent cost management. This means creating a framework where cost is a key consideration in every engineering decision, not an afterthought that finance has to clean up later. The goal is to embed cost awareness into your team's DNA, ensuring that you can maintain deep visibility into your systems without letting your monitoring budget spiral out of control. It’s about creating a culture of financial accountability that supports, rather than hinders, innovation.

Establish Cost Governance Policies and Regular Reviews

Think of cost governance as setting the rules of the road for your monitoring spend. It’s about creating clear policies that define who can spend what, which services are approved, and how new monitoring tools or metrics get provisioned. Once you have these policies, the key is to review them regularly. Set up monthly or quarterly check-ins with engineering leads and finance stakeholders to review spending against forecasts. Tools like Datadog’s Cloud Cost Management can help by bringing performance and cost data together, making these conversations much more productive. This process ensures spending stays aligned with business objectives and prevents surprises at the end of the month.

Find the Right Balance Between Monitoring and Cost

There’s a common fear that cutting monitoring costs automatically means sacrificing visibility. But the goal isn’t to blindly slash your budget; it’s to optimize. You can achieve significant savings by being smarter about the data you send to Datadog in the first place. Instead of paying to ingest, index, and store low-value, noisy logs, you can implement intelligent filtering and processing at the source. By handling log processing before the data ever hits your observability platform, you send only the high-signal information that’s critical for troubleshooting. This approach lets you reduce data volume and lower costs while actually improving the quality of your monitoring data.

Build Optimization Into Your Team's Habits

Lasting cost control is a team sport. It happens when engineers feel a sense of ownership over their service’s monitoring spend. You can foster this by breaking down the walls between engineering and finance and making cost data accessible to the teams writing the code. Assign budgets to specific teams or projects to create direct accountability. When developers can see the cost impact of a new feature or service in real-time, they’re empowered to make more cost-effective decisions. This shift turns cost optimization from a top-down mandate into a shared responsibility that becomes a natural part of the development lifecycle and strengthens your overall FinOps culture.

Related Articles

• 15 Proven Datadog Cost Optimization Strategies | Expanso
• A Guide to Datadog Cloud Cost Recommendations | Expanso

Frequently Asked Questions

Where's the best place to start if my Datadog bill is already out of control? Start with visibility. Before you can cut costs, you need to understand exactly where your money is going. Use Datadog’s own cost management dashboards to identify which teams, services, or applications are generating the most data. This isn’t about placing blame; it’s about finding the biggest opportunities. You’ll often discover that a huge portion of your spend comes from a single noisy service or a misconfigured logging setup. Once you’ve pinpointed the source, you can apply more targeted strategies like filtering or sampling.

If I reduce my data volume, won't I miss important information during an outage? This is a common and valid concern. The goal is to optimize your data, not simply eliminate it. True optimization means you send less noise and more signal. Instead of blindly dropping logs, you can implement intelligent rules that filter out low-value informational messages while always retaining critical error logs. You can also use a tiered storage strategy, archiving less critical data to a cheaper location for compliance while keeping essential troubleshooting data readily available in Datadog. This way, you maintain visibility where it counts without paying a premium for data you rarely use.

How is processing data with a tool like Expanso different from just using Datadog's built-in filtering? Datadog’s features are great for managing data once it has already arrived at the platform, but you’ve already paid the cost to send and ingest it by that point. Expanso allows you to process data at its source, before it ever leaves your environment. This means you can filter, aggregate, and transform massive data streams right where they are generated. The result is a much smaller, higher-value dataset sent to Datadog, which directly lowers your ingestion and storage costs. This approach also gives you far more control over data residency and compliance, as sensitive information can be handled locally.

My engineers are focused on building features, not managing costs. How can I get them to care about our monitoring spend? The most effective way is to make the cost tangible and connect it directly to their work. When cost is just an abstract number on a finance report, it’s easy for engineers to ignore. By using cost attribution tools, you can show a specific team the direct financial impact of the services they run. When a team has its own budget and can see how their choices affect it, cost becomes just another engineering metric to optimize, like latency or uptime. It shifts the conversation from a top-down mandate to a collaborative effort to build more efficient services.

Is it better to focus on optimizing custom metrics or log volume first? For most organizations, tackling log volume will give you the biggest and fastest return on your effort. Logs often represent the largest and most unpredictable portion of a Datadog bill, and simple changes like filtering out debug-level logs from production can have an immediate impact. While custom metrics can be expensive, they are often more deliberately created. Starting with logs usually provides a quick win that can build momentum and free up budget for more nuanced optimizations across your entire observability stack.