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.
That's why I've decided to compare these tools based on the following criteria:
Platform functionality overview
Ease of integration and onboarding
UI & UX
Data querying and visualization
Third-party and community reviews
Datadog vs. New Relic, a side-by-side comparison in 2022
⚖️ Testing conditions
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.
1. Platform functionality overview: Tie
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.
✓ (logging add-on limited)
✓ (APM needs other products to work)
✓ (features are in the early-access phase)
✓ - 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 2022, both tools offer almost
the same features, but New Relic put a lot of work into AI in the data analysis.
2. Ease of integration and deployment: Point New Relic
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
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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
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.
3. UI and UX: Point New Relic
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 2022. 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.
4. Data querying and visualization: Point Datadog
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
5. Team-management: Point New Relic
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
New Relic reinvented its user model and, as of 2022, offers three main user
roles, defined in the documentation like this:
Full platform user - has unlimited access to the entire platform
Core user (previously standard) - has access to more developer-centric
features, like CodeStream or Logs UI
Basic user - has access to basic features like setting up observability
tools or alerts and dashboards
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
Admin - An user with full-platform access
Standard - Users can access key workflow features such as events or
monitors. These users can also invite new users to the platform.
Read-only - This role is used for sharing view-only dashboards and
visualizations, e.g., access made for clients, etc.
6. Incident management and alerting: Point Datadog
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.
🔔 Four Golden Signals
Google SRE handbook in the chapter on Monitoring Distributed Systems defines these as the following key performance metrics:
Latency - A measure of time of a successful request takes
Traffic - How much demand is being placed on the system
Errors - The rate of failed and successful requests
Saturation - The amount of strain put on individual system resources
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
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.
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7. Pricing: Point New Relic
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
8. Third-party and community reviews and my personal preference: Point New Relic
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
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
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
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.
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