Datadog vs Dynatrace: Which is Better in 2026? [Hands-On Testing]

Datadog and Dynatrace are popular tools when it comes to monitoring and observability. In this article I have compared Datadog and Dynatrace across different aspects like getting started, infrastructure monitoring, log monitoring, pricing, etc.

💡 This article is my personal take based on the experience I had when integrating Datadog and Dynatrace. Some takeaways are subjective based on my personal preference

Datadog vs Dynatrace: Overview

Here's a comparison of the main features between Datadog and Dynatrace.

✅ - Available     ❌ - Not Available

Note: Both platforms accept OpenTelemetry logs, metrics, and traces, but their setup paths differ. Dynatrace provides native OTLP endpoints and Collector or ActiveGate options. Datadog prioritizes its proprietary Agent, while also supporting OTLP through the Agent, DDOT, the OpenTelemetry Collector, and direct OTLP ingest.

For other matchups, Datadog vs New Relic and Datadog vs CloudWatch weigh Datadog against other platforms, and Splunk vs Dynatrace covers Dynatrace on the log side.

Datadog vs Dynatrace: Testing Environment

In this hands-on test, I used a Python application that was connected to a MongoDB database. In order to have a level playing field, I did not want to host them on my own machine. Instead, I opted for t2.micro EC2 instances from AWS. These come with 1 vCPU and 1 GiB of RAM. For storage, I opted for 8 GB.

I created 2 EC2 instances (one for testing Datadog, the other for Dynatrace). Next, I installed MongoDB and had my Python application running in both. After a round of basic testing to see if everything was working fine, it was time to collect some metrics.

Sign up Process

Datadog

Going to the Datadog website, I was prompted to create an account by providing my email and other details. There was no need to provide credit card details. After verifying my email with the security code sent, my account was created, and I was taken to the next page. This asked me about my tech stack. I found this easy to follow, and you get the option to skip this.

Proceeding from here, it took me directly to the Agent Setup page. I'll talk about this process shortly.

Dynatrace

Dynatrace has a similar signup process. I provided my email and other details. Once verified, my account was created. There was no prompt to enter my billing details.

One thing to note about Dynatrace is that unlike Datadog, the account creation isn't instant. It takes a while for the signup process to complete. I was presented with a "Processing your signup" page, and that took about 4 minutes to get done.

Once completed, I was taken to the landing page. But I didn't find the UI very intuitive. Moreover, the Dynatrace Hub takes a very long time to load. Once the Hub was loaded, it showed direct integration with AWS. But that needs IAM role for access and would monitor my entire AWS account. That wasn't my plan. And it took quite some time for me to figure out how to proceed from there. More on that later.

Agent Installation

Datadog

As part of the sign-up process, I was taken to the Agent Installation page. I selected Amazon Linux on the left, and it gave me the required command to run.

I went to my Datadog EC2 and ran the command. After downloading and installing the necessary packages, the Datadog Agent was up and running on my machine. That was easy.

Once the Agent had started, I came back to the website to check. As expected, I could see that Datadog had started monitoring 1 host. This showed me all the infrastructure metrics.

But Datadog did not start auto capturing the application metrics. I was prompted to install another application-specific Agent. For Python, I had to install the Datadog client ddtrace.

Next, in order to start with the instrumentation, I had to pass additional arguments to my application startup command. The UI provided all the necessary steps to build the final command.

Dynatrace

I didn't find the Dynatrace UI to be intuitive or user-friendly. It kept showing role-based monitoring for AWS, but I just needed to monitor the single instance. I knew that I needed to install an agent, but I couldn't find the instructions anywhere. After Googling for answers, I finally ended up on the Agent installation page.

For Dynatrace, you need the OneAgent. Since I am running a Linux instance, I navigated to the corresponding page. I was asked to generate a PaaS token (since I didn't have one already). It then showed me a set of commands that I had to run.

The first command downloads the installer. The second one verifies the signature. I could run those without any issues.

Next, it was time to run the OneAgent. I had to run the command with sudo as it required root privileges. Took a minute, and then it showed that the OneAgent was successfully connected to Dynatrace.

I went to the Dynatrace website to check. The OneAgent tab showed that I had one host connected, and it was being monitored.

Unlike Datadog, I didn't have to install anything additional (for now). Dynatrace automatically started monitoring both the Python application, and the MongoDB I was running.

I will talk about the monitoring aspects next.

Infrastructure Monitoring: Both provide detailed views

Datadog

Datadog presented an easy-to-follow dashboard for my EC2 instance. It showed the basics like CPU, Load, and Memory, along with the processes that were running on the instance.

APM isn't a part of this dashboard. That resides as a separate tab.

Dynatrace

Dynatrace provided a single window where all the metrics were present. As I scrolled down the page, I could find all the necessary metrics that one might need to get started with.

One major differentiator with Datadog is the fact that in Dynatrace I could navigate to the APM from this dashboard itself. The Process analysis section showed all the processes that were running on the EC2 instance.

On clicking the Python app, I was taken to a detailed view where it not only showed the application metrics but also showed how it was connected to the MongoDB process. I found this rather interesting.

Application Monitoring: OpenTelemetry setup was simpler in Datadog in this test

Datadog

Datadog has a separate tab for APM. On navigating there, I could see both my Python application and the MongoDB database being monitored.

Clicking on the app opened up a quick view of the Performance metrics. This included Requests and Errors, Latency, and % of time spent across services.

The app specific service page provided detailed view for the monitored metrics.

Even the database monitoring for MongoDB showed important metrics, including the database resources being called, along with latency and errors.

Although everything had a separate panel, it was detailed enough to check all the necessary metrics thoroughly.

Dynatrace

After checking the infrastructure and process metrics, I wanted to look at the application metrics, along with Logs and Traces. I once again found myself lost in the UI, trying to figure out where to go next. After searching for a while, I came across the documentation for setting up application metrics.

Dynatrace offers two options for Python instrumentation: OneAgent or OpenTelemetry. Current Dynatrace documentation states that OneAgent can automatically inject into supported Python processes. For this test, I used the OpenTelemetry route.

Next, it was time for me to set up the required configuration. Going through the documentation, I had to find the OTLP endpoint and the access token. Generating the access token was tricky because I had to know the required access scopes. I had to go to another document to find them.

With these in place, I had to install the python packages for opentelemetry. After that, I had to restart the Python app with the opentelemetry-instrument command.

Back in the Dynatrace UI, I had to look for the Distributed Traces option. Finally, I could see the traces being generated.

The UI is quite detailed. Clicking on any trace shows the relevant calls, as well as the option to see the logs for the trace.

Setting up the Request tracking for the REST endpoints needed additional configuration.

Logs Monitoring: Easier Setup with Dynatrace

Datadog

By default, the log exporter is disabled in the Datadog Agent. I had to manually make the required changes. The Datadog documentation provided the necessary steps to take.

First, I had to open the datadog.yaml configuration file. This was present in the /etc/datadog-agent directory. I had to use sudo in order to add the following changes.

logs_enabled: true
logs_config:
  force_use_http: true

After this, I had to restart the agent.

But this didn't start capturing the application logs. I had to figure out a way to mention the log file, which for me was present in /tmp/server.log for the Python application.

I navigated to the /etc/datadog-agent/conf.d directory. Here, I created a python.d folder. Inside this folder, I created a conf.yaml file with the following content.

logs:
  - type: file
    path: /tmp/server.log
    service: flaskapp
    source: python

I restarted my application. Using the datadog-agent status command, I could see that the Logs Agent was active and that it was sending logs from the log file.

Dynatrace

In contrast to Datadog, Dynatrace started capturing logs by default. In order to set the custom log file, I did not need to make any changes from the EC2 terminal. Rather, every configuration was driven from the Dynatrace UI.

From the Hosts Classic page, I opened the Settings for my EC2 instance. Scrolling down, I found the Log Monitoring option, inside which was the option for custom log sources. Here, I just had to provide the full file path for my server.log file.

With this, I was able to see the application logs.

AI Capabilities: Davis AI vs Watchdog

One of the key differentiators between these platforms is their approach to artificial intelligence and automation.

Dynatrace Davis AI

Dynatrace Intelligence, which includes Davis AI capabilities, uses baselines, topology, and dependency context to detect anomalies and group related alerts into problems. Its problem view can show the affected services and a probable root cause. Teams can connect these problems to workflows for notifications or predefined remediation steps.

Datadog Watchdog

Datadog Watchdog also detects unusual behavior and provides investigation context. It creates proactive alerts and insights, and it can run a preliminary root-cause analysis for supported APM anomalies. It also shows the potential user and service impact of an issue.

The main difference is the operating model. Dynatrace puts more emphasis on topology-based problem analysis and enterprise workflow automation. Watchdog works inside the broader Datadog platform to detect issues and speed up the first part of an investigation. Neither product removes the need to validate the evidence or configure the response workflow.

Pricing

After testing both platforms, I wanted to understand the actual costs organizations face in production. The pricing models are quite different, and this can significantly impact your total cost of ownership.

Datadog

Datadog prices many products and capabilities as separate modules. Depending on the products you select, the bill can include hosts, containers, indexed spans, ingested or indexed logs, retention, and other usage units. This gives teams control over what they buy, but it also means that an estimate must include each required module. See the current Datadog pricing list and our Datadog pricing guide for the units to check.

Dynatrace

Dynatrace now presents the Dynatrace Platform Subscription as a consumption-based model with an annual commitment. Different capabilities consume that commitment at different rates. A cost model must therefore include the amount of data and the capabilities that the team will use. See the current Dynatrace licensing documentation for the active units and rates.

Documentation: Datadog is better

Both Datadog and Dynatrace have a good amount of official documentation. Between the two, I personally found Datadog to be more straightforward. For Dynatrace, I had to open multiple documents and hop from one to the other to figure out how to do things.

Datadog also has a lot of explainer videos on YouTube that might help you set things up faster.

Datadog vs Dynatrace: Final Verdict

After using both tools and analyzing real-world experiences, here's the bottom line:

Dynatrace wins on:

• Topology-based problem analysis
• Automated enterprise workflows
• A connected view of hosts, processes, services, and dependencies
• Enterprise deployment and governance needs

Datadog wins on:

• Developer experience and ease of use
• Ecosystem and integrations
• Dashboard flexibility
• Community and documentation

There's no universal "best" choice. Dynatrace can be a better fit when topology-based analysis and automated enterprise workflows are the main requirements. Datadog can be a better fit when onboarding, integrations, dashboards, and developer experience are more important.

Remember: Both are expensive enterprise tools. If you're looking for a cost-effective managed alternative, consider SigNoz Cloud. If you need to run the platform on your own infrastructure, consider Self-Hosted SigNoz.

Related comparisons worth a look: the Datadog alternatives and Dynatrace alternatives roundups, plus Datadog vs Splunk and Dynatrace vs AppDynamics.

A Better Alternative to DataDog and Dynatrace - SigNoz Cloud

The challenge with both Datadog and Dynatrace is their complex pricing models and potential for bill shock. If you're looking for a more transparent, managed pricing model, consider SigNoz Cloud.

Migration and OpenTelemetry

Teams that migrate from Datadog can use the Datadog migration guides for data, dashboards, and alerts. The guides include an automated tool that converts Datadog dashboard JSON into a SigNoz dashboard that teams can review and refine. Enterprise plans also include guided migration support.

SigNoz is built on OpenTelemetry, so teams can move instrumentation and telemetry transport away from proprietary agents in stages. SigNoz keeps OpenTelemetry resource and span attributes, span events, links, trace IDs, and other context available for investigation. For Prometheus workloads, the OpenTelemetry Collector can scrape Prometheus endpoints, and SigNoz can query Prometheus metrics with PromQL.

Cost and Deployment Choice

SigNoz Cloud uses telemetry volume and retention instead of per-host or per-user prices. It does not add a separate price for custom metrics. Compare the same data volume, retention, and users in the SigNoz pricing calculator before you estimate savings.

Deployment options include managed SigNoz Cloud, dedicated Enterprise Cloud, managed BYOC, Enterprise Self-Hosted, and the open-source Community Edition. This range is useful when data residency, isolation, or infrastructure control is a requirement.

Daily Investigation Workflows

The product brings logs, metrics, traces, exceptions, infrastructure data, dashboards, and alerts into one interface. The common workflow is to start with one signal and move to related telemetry without changing tools. A single query builder provides a consistent way to query logs, metrics, and traces. Features such as query history, quick filters, saved views, autocomplete, dashboard variables, and alert history support repeated investigations. Noz and the SigNoz MCP Server add natural-language and agent-led investigation paths. Enterprise plans also add RBAC and SSO options.

This is a good fit for teams that need correlated log analysis, detailed distributed tracing, and infrastructure monitoring for Kubernetes, AWS, Azure, GCP, and Linux hosts. SigNoz Cloud removes the need to operate the ClickHouse storage layer for high-volume logs. For tracing, SigNoz supports span aggregation and shows inline span percentiles.

Getting Started with SigNoz

The easiest way to start is SigNoz Cloud, which has a 30-day trial. Teams that need isolation or control over data location can use dedicated Enterprise Cloud, managed BYOC, or Enterprise Self-Hosted. Teams that want to operate the platform themselves can use the open-source Community Edition.