Both Datadog and New Relic belong among the frontrunners when it comes to observability products. I've researched, deployed, and tested both. See my side-by-side comparison below.
One of the challenges when deploying a reliable service is picking a suitable observability suite. There's a lot of “fine print” one has to go through before making the final decision, and it's rarely about the pricing or contract. Key differences, at least how I see them, are in the service itself.
I've deployed both tools on Ubuntu 22.04 server distro. Independently with the same hardware and network resources. I've coded a simple script and deployed a default, out-of-the-box Django server in a virtualenv. From there, I tried to rely solely on vendor-provided documentation and other onboarding resources.
Datadog's main focus is cloud security and monitoring. There are multiple tools such as Cloud Workload Security, Sensitive Data Scanner, Cloud SIEM, Application Security monitoring, etc.
New Relic, on the other hand, is a powerful Application performance monitoring tool. You can see that with Mobile, Synthetics, and Browser monitoring, each being a tool on its own.
| Feature | Datadog | New Relic |
|---|---|---|
| Infrastructure monitoring | ✓✓ | ✓✓ |
| Log management | ✓✓ | ✓ (logging add-on limited) |
| APM | ✓ (APM needs other products to work) | ✓✓ |
| RUM | ✓ | ✓✓ |
| Incident Management | ✓✓ | ✓✓ |
| Security monitoring | ✓✓ | ✓ (features are in the early-access phase) |
| On-boarding platform | ✓✓ | ✓✓ |
| Freemium plan | X | ✓✓ |
| SAML SSO | ✓✓ | ✓✓ |
| SLA Monitoring | ✓✓ | ✓✓ |
| User-based access | ✓✓ | ✓✓ |
✓ - feature is present but somehow is limited
✓✓ - feature is present
X - the platform does not offer this feature
New Relic has been around since 2008. Since then, it grew from a set of “simple” monitoring tools to an end-to-end observability suite comprised of 16 main tools. All data is easily accessible from a well-designed UI. What I especially liked about the UI were notifications-like alerts and tips, addressing observability gaps in my data collection.
Datadog was founded just two years later and started out as a cloud infrastructure monitoring tool. Essentially, as of 2023, both tools offer almost the same features, but New Relic put a lot of work into AI in the data analysis.
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In the case of New Relic, you can sign up for a free-forever, full-access account to test it around. That way, I was not limited by a trial period and could really get to know the features it offers.
To deploy New Relic on my host, I followed the guided installation for APM. This tab included everything I needed, including a code snippet for an infrastructure agent with enabled logging. However, I could not rely on the code snippets entirely since New Relic still does not offer support for logging plugins on Ubuntu 22.04. I found some community workarounds, but 22.04 still does not have official support.
Go to Logtail and start your log management in 5 minutes.
New Relic's installation script is fully automated. After a successful
installation, you get access to newrelic CLI, allowing you to check, config,
or troubleshoot your installation. The agent started to report on the
performance of my host immediately, and New Relic's UI suggested I move on to
setting up python.
Everything, including the issues I've encountered due to my script's lack of complexity, was easy to fix, thanks to documentation. After some minor configuration and script touchups, New Relic received the first data from the APM.
Since I've already researched Datadog before, for my Sentry vs. Datadog article, the entire process felt easier. Nevertheless, when recalling the initial process, I must say that New Relic still beats Datadog in this domain.
The process of deployment is approximately the same. I needed to download and install Datadog's infrastructure monitoring agent. I did this by simply copypasting the generated bash command into the console.
I installed the agent by executing pip install ddtrace. To initialize the APM
agent, you need a script like the one you can see in the screenshot below. It
can be easily generated in Datadog's APM setup. Both setups are a bit
over-engineered on my side, but I figured I could be slightly sloppy with how I
spend my resources.
In terms of onboarding support, personally, I find Datadog's documentation
really badly structured but well-written. What I mean by that is that finding
certain important pages is unnecessarily dreadful, but once you get there,
you're golden. However, some issues, including the deployment and
troubleshooting of ddtrace, were solved only after consulting the world wide
web and GitHub issues.
Both UIs are well designed and offer a light and dark mode. I noticed some color and contrast issues with the dark mode in the case of Datadog's UI, but apart from that, it's only a matter of preference.
New Relic released its new UI at the end of June 2023. A quick google search revealed some initial onboarding issues, mainly users not seeing critical data in the new UI. But apart from that, I think New Relic's new UI is visually pleasing, minimalistic, and purpose-built.
I've spent some time in the Datadog UI and noticed a few things. It takes some time to find your way around, and it's the little things that spoil the entire experience. For example, I only accidentally stumbled upon their academy, which offers complete, browser-based coding exercises for various products, including a complete APM tutorial. This issue is shown in the documentation where individual pages are weirdly linked together, internal hyperlinks do not really lead where they should, or Further reading does not offer a logical succession or context.
Compared side-by-side, New Relic's UI is a bit more intuitive. From querying data to setting up alerts, everything felt easier. New Relic is extremely complex and collects any data from anywhere and yet, has a way of serving you said data in a human digestible way.
All of the data I provided to New Relic was available from the UI. A simple, click-based environment. For more advanced data workflows, New Relic built its query language - NRQL, which feels a lot like the standard SQL for the queries. On top of that, New Relic supports PromQL queries and interprets them to the NRQL language as best as possible.
Datadog offers a custom query syntax that is based on terms and operators.
Terms represent general or unique identification, operators take care of the
searching logic. To create a search query, I needed to combine these terms with
logic operators AND, OR, and - instead of NOT. When creating custom
queries, I mostly leveraged the click-based query builder, which I find easy to
work with. The only limitation of Datadog I can find here is the 15-minute cap
on live data queries.
When it comes to Dashboard creation, both tools offer click-based creation tools, which make it really easy to create custom dashboards and even throw some memes in.
Especially in the case of New Relic, understanding user roles is crucial for pricing, platform integrity, and security. User access is also important in incident management and impacts how well teams can collaborate within the platform.
New Relic reinvented its user model and, as of 2023, offers three main user roles, defined in the documentation like this:
In the case of Datadog, users are key in Incident management, which is priced per user per month. Essentially, you get an unlimited amount of users for free and then have to set up user roles for each, and those who are active in incident management are billed at the end of the month. Once again, there are three tiers:
During the initial setup, asked me if I want to automatically monitor the Four Golden Signals Thanks to this setup option, New Relic automatically generated UI-alerts when it lost contact with my application.
Google SRE handbook in the chapter on Monitoring Distributed Systems defines these as the following key performance metrics:
I could address this incident directly from the UI by drilling down to my python service and then addressing issues, errors, and incidents. After acknowledging the incident, I manually closed it and created a dummy post-mortem. The entire process felt easy and seamless. I did not, however, integrate New Relic with a third-party on-call scheduling, or status pages solution.
The incident management is more or less the same in the case of Datadog, but this time, the setup was super easy. Both incident declaration, alerting policies, and remediation, including the auto-generated postmortem, were user-friendly.
In terms of incident management, I think Datadog has the upper hand, mainly with little details, making the workflow more coherent, such as the option to add a link to a video call or the incident declaration and workflow itself.
Go to Better Uptime and start managing your incidents in 2 minutes.
New Relic offers a rather generous free subscription tier, offering one full platform user and 100Gbs of ingested data. This tier can be elevated to a subscription with additional data or users. To get more enterprise-standard tools like SAML SSO, SLA, and Advanced admin capabilities, you'd need to subscribe to PRO or Enterprise tiers. In summary, New Relic's pricing strategy is based on data, where you pay $0.30/GB for anything above the 100GB cap and the number and type of users.
Datadog has a decentralized pricing model, each product has its own pricing logic. For example, APM & Continous profiler is priced per host, the same applies to Infrastructure monitoring, a requirement for APM. Other features, like log management, are billed based on ingested data, and incident management is billed per contributing user. Datadog has no full-platform pricing publicly advertised, it's arranged with their sales team, with the option of volume discounts.
Third-party references and community reviews are a great source of really use-specific reviews. A quick search of major platforms like G2, Reddit, or Twitter gives the general idea of what a hands-on experience of real users is like.
In the case of New Relic, the first thing I thought of (and I was not alone) was what's the catch with the free subscription. There's literally a Reddit thread asking the same question. In terms of positive reviews, users mostly brought up APM features and accuracy, some also liked how New Relic processes data, reliability and availability, and onboarding support.
Regarding negatives, users found the UI & UX overwhelming with a very stiff learning curve. A subcategory of this issue was tool grouping, where one tool is a part of multiple categories, and users do not find the grouping logic intuitive. However, I can't say with certainty if they're addressing the newly redesigned UI or the previous one. Other, less recurring complaints were about the NRQL language and “lacking fine-tuning when compared to SQL”, and more control over the metrics sent to New Relic, which is important since New Relic bills users per data ingested.
In Datadog's positive reviews, users highlight a wide range of integrations and the convenience of monitoring everything from one place.
Datadog's users complain mainly about issues related to pricing, documentation, and tech support. In the case of pricing, it's mainly about the fact that the pricing is quite complex, but also Datadog is expensive in general. Users find documentation either chaotic or outdated.
Generally, Datadog gets a lot of almost grotesque hate on the internet. There are a bunch of running jokes on Reddit about the tactics of sales teams, but I think there's a lot of bandwagoning going on.
In a side-by-side comparison, I could conclude that Datadog wins in the fields of Infrastructure and cloud monitoring, security monitoring, integrations, and community. New Relic, on the other hand, is preferred for real-time performance data analysis, support, and pricing (it's not cheaper, just easier to grasp).
Personally, from a solo user standpoint, I prefer New Relic thanks to the generous free full-access tier, documentation, and support. However, when trying to empathize with engineers on a bigger scale, I think for infrastructure monitoring, I'd rather invest in Datadog. And to monitor complex applications, I would go with New Relic. As for incident management, I'd integrate everything with Better Uptime.
Anyway, that's it for today, if you got all the way here, thank you for reading my article, and see you at the next one. In the meantime, check out my side-by-side comparison of Sentry and Datadog.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Python APM tools enable code-level observability, faster recovery, troubleshooting, and easier maintenance of any python project.
APM tools enable code-level observability, faster recovery, troubleshooting, and easier maintenance of digital services.
I have deployed, tried and tested Datadog and Sentry, to help you pick the right APM/error tracking tool.
Infrastructure monitoring gives you insight into the overall health of your project. By collecting and analyzing data coming from IT infrastructure, systems, and processes, you can prevent incidents, evaluate performance, better optimize and scale, or find a root cause of everything that's happening within your system.
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