Datadog Pricing Plans

Datadog’s subscription model is structured similarly to a “build-your-own” buffet that begins with the basic infrastructure, yet adds layers of costs based on what you consume. In terms of the foundation of the structure, Datadog has established a tiered pricing system – Free, Pro, and Enterprise — in order to group the standard features of infrastructure monitoring. To us, this appears to be an easy-to-understand way to organize the basic features of infrastructure monitoring for your servers; however, the true complexity of the structure comes from the modular add-ons. When you purchase "Datadog", you are purchasing Infrastructure, and then adding APM (Application Performance Monitoring) for your code, Log Management for your traces, and possibly RUM (Real User Monitoring) if you have a frontend.

In our view, the "Host" is the most granular level of pricing for Datadog; however, we believe it is also very important to keep track of how they define it. Regardless of whether it is a VM, a Kubernetes node, or a cloud instance, you typically pay a flat fee per host, per month. We would highly recommend that you monitor their "high-water mark" billing structure as well, as it will charge you extra for short-term increases in usage. From our perspective, the structure is designed for maximum flexibility, but it takes a skilled administrator to manage all of the different "meters" for items such as custom metrics, and log ingestion volume. To us, it is a scalable platform that matches your needs perfectly, as long as you can handle having multiple lines on your invoice at the end of the month.

Datadog helps organizations take an unmanageable mess of raw data and tell a coherent story of what their systems are doing in the world. Datadog essentially provides a "single pane of glass" that takes the three pillars of observability—metrics, logs, and traces—and combines them so that teams do not have to switch back and forth between ten tools to find one bug. We believe that the real power of Datadog comes from the ability to correlate events; since all of the data has been tagged with the same metadata, when you see a spike in latency in your Java application, you can go directly to the specific Kubernetes pod or database query that is causing the lag. From our standpoint, Datadog is not simply meant to allow you to watch things fail; it is designed to reduce the time it takes to resolve issues (Mean Time To Resolution or MTTR) from hours to minutes.

We believe that the optimization area is where Datadog really proves its value. With the Service Map and Application Performance Monitoring (APM), we believe that teams will be able to stop making educated guesses about which of their micro-services are creating the bottleneck. We recommend using Datadog's "Watchdog" AI to identify those silent failures such as a slow memory leak that are typically never detected by static alert mechanisms. Although we realize that the price can become expensive, especially if you are not careful with your log ingestion costs, the improved visibility into resource utilization will enable teams to make better decisions regarding capacity planning. From our viewpoint, the biggest difference is between throwing additional resources at a problem without understanding why the problem exists and identifying the inefficient code that is consuming your CPU.

Those teams that would get the most from Datadog are generally found in very large and very dynamic cloud native environments, where "visibility" is not just a word but an essential for survival. In general we consider DevOps and SRE teams as being the primary beneficiaries of Datadog, since these are the teams primarily responsible for the management of micro services that exist across various environments (AWS, Azure, GCP). If your architecture has hundreds of containers or serverless functions, then you cannot possibly be able to manage that with simple logging. We believe Datadog fills a void between the layers of infrastructure and applications which provides a major advantage to platform engineers in providing a solid base for the remainder of the company to operate on.

We also believe that another area that greatly benefits from Datadog — although we have seen them get less attention than other areas — is product and engineering leaders. If a release fails in a way that causes the development team to lose confidence, then we believe a single, shared dashboard that both developers and managers can use to visualize what happened will eliminate the "who's fault was it" argument. Based upon this philosophy, we recommend Datadog for any teams that are transitioning into a "you build it, you run it" type of model, where developers need to be able to see the performance implications of their code in production in near real time. While we believe that smaller and more static teams may find Datadog to be too much of a resource to utilize, we believe any company that values rapid delivery of products and wants to correlate business KPIs with the performance of systems, will find it to be a critical component of their environment. From our perspective, Datadog is for the teams that do not wish to continue to fly blind, and want to make visibility a core element of their organizational culture.

Whether you see observability as a "mission-critical" insurance policy or a "cost center", ultimately Datadog's value for scalability and reliable operation will depend on how you view the relationship with your observability platform. In our opinion, the best way to look at the value of Datadog for scalable and reliable operation is when your infrastructure is a large network of microservices where a five minute outage will cost you much more than your yearly SaaS subscription. The all-in-one nature of Datadog provides a great advantage for your SREs who can stop acting like private investigators, and focus on writing code. I would highly recommend this product to those organizations that are experiencing growth so rapid that it has become impossible to continue to support an ELK (ElasticSearch/Logstash/Kibana) stack or Prometheus servers.

In my experience, however, you need to be diligent with the "Datadog Tax". There are many examples of organizations that have experienced surprise billing from Datadog due to turning on all features of the service and failing to manage their log ingestion or custom metric cardinality. As such, we view Datadog as being similar to a high performance sports car - it will take you to your destination faster than any other vehicle available, but there is a cost associated with the maintenance and fuel. Therefore, I would recommend Datadog to organizations that consider developer velocity and uptime more important than the cost of tools. To me, Datadog represents a brilliant investment for reliability as long as there is one member of your team responsible for monitoring the usage of the service so that the ROI does not end up getting eaten away by the monthly bill.

We've seen (and heard from many) that the Datadog Pro plan is hands down the most commonly used plan. And honestly, it's easy to see why. The Pro plan fits right into what we call the "sweet spot" of the Goldilocks effect when considering all the available plans - a feature set that matches most every mid-sized startup and growing engineering team for the price. If the free version is a little too restrictive for companies and the enterprise version feels like an enormous jump in terms of cost and complexity, then Pro is that "just-right" option for most people running a production environment in that they get the basic toolset (15 months of metrics and some form of alerts). We consider this the base level for any organization that has made the move past the experimental stage and now has customers that rely on their uptime.

We also believe that the reason for its popularity is due to how well it integrates with the overall ecosystem. Teams that we speak with are not just purchasing infrastructure monitoring, but are integrating APM or log management at the same time and Pro is the best anchor point for these additional modules. We would recommend Pro to approximately 80% of the organizations we work with since it offers enough historical data for the ability to learn from your mistakes without forcing you to purchase high-end machine learning or expensive governance features that you may not be prepared for yet. In our view, unless you are a very large corporation that requires strict compliance or a single person operation, Pro will be the go-to choice for most teams because it simply works the way that most development teams today do.

To get the most out of your Datadog subscription, you need to balance high-level observability with sharp cost management—otherwise, that "single pane of glass" can become an expensive luxury. Whether you’re just starting out or scaling fast, the goal is to ensure every dollar spent translates into actionable insights rather than just storing "noisy" data that no one ever looks at.

Here are the expert best practices we recommend to keep your setup lean and effective:

1. Leverage our exclusive offer: We think the best way to start is by eliminating the initial cost barrier. You can get 1 year free on the Pro plan by using our Datadog offer. It’s a massive head start that lets you build your dashboards and alert logic without eyeing the bill for the first twelve months.

1. Commit annually for 15% savings: If you've moved past the experimental phase, we recommend switching from monthly to annual billing. Paying annually typically secures a 15% reduction (dropping the Pro price from $18 to $15 per host), which adds up significantly as your infrastructure grows.

1. Audit your pricing plan: We feel it’s vital to regularly review your usage. If you find you aren't using advanced features like Watchdog or complex RBAC, you can actually move to a more affordable pricing plan to save money. Conversely, don't let a "Pro" pricing plan hold you back if your team is wasting hours on manual monitoring that the Enterprise AI could automate.

1. Prune your extensions: From our perspective, Datadog’s modularity is a double-edged sword. We recommend strictly limiting unnecessary extensions like high-retention logs or excessive custom metrics. Use "Ingestion Controls" to drop 90% of your debug logs at the door so you only pay for the signals that actually matter.

When you're trying to pin down exactly how many "events" Datadog allows per host, it’s easy to get tangled up in their terminology. In the world of Datadog, "events" usually refers to one of two things: the system-level notifications (like a chef run finishing or a container crashing) or "log events." For infrastructure monitoring, the way they handle these is actually pretty flexible, but there are some guardrails you should know about.

From our perspective, here is how the limits actually shake out on the ground:

1. Infrastructure events (system notifications): For standard infrastructure monitoring, Datadog doesn't typically enforce a hard "per-host" quota on system events (like those seen in your Event Stream). We think of this as a "fair use" policy. As long as you aren't spamming the API with millions of low-value events, they generally don't bill you extra for these.

1. Log events: This is where the real limits live. If you’re talking about "log events," those are billed separately from your host plan. You’re usually looking at a cost of about $1.70 per million events indexed (for 15-day retention). In our opinion, this is the "meter" you really need to watch, as a single chatty host can easily blow through millions of events in a few hours.

1. Custom metrics vs. events: We’ve noticed many users confuse these. Your Pro plan includes 100 custom metrics per host (and Enterprise includes 200). If you're trying to use "events" to track numerical data, we recommend switching to custom metrics instead, as they are much more cost-effective for high-frequency data.

1. Monitor limits: Just a heads up—there is a default account-wide limit of 1,000 event monitors. While this isn't a "per-host" limit, we feel it’s a crucial ceiling to keep in mind if you have a massive fleet and want to alert on specific event patterns.