Datadog vs Dynatrace

Overview

Datadog

Stacks9.8K

Followers8.2K

Votes861

Dynatrace

Stacks337

Followers348

Votes28

Datadog vs Dynatrace: What are the differences?

Comparison between Datadog and Dynatrace

Datadog and Dynatrace are both leading companies in the field of application performance monitoring (APM) and observability. While they have similar goals, there are a number of key differences between them that set them apart from each other.

Ease of use: Datadog offers a user-friendly interface and is known for its easy setup process, making it a popular choice for organizations with limited technical expertise. On the other hand, Dynatrace is more comprehensive and feature-rich, but its complexity may require more technical proficiency to fully utilize its capabilities.
Deployment flexibility: Datadog provides a wide range of deployment options, including on-premises, cloud, and hybrid cloud deployments. This flexibility allows users to choose the best option for their specific needs. In contrast, Dynatrace focuses primarily on a Software as a Service (SaaS) model, providing a cloud-based solution.
Monitoring capabilities: Datadog offers a broad range of monitoring capabilities, including infrastructure monitoring, application monitoring, and log management. It also supports a wide variety of technologies, platforms, and frameworks. Dynatrace, on the other hand, is known for its advanced AI-powered monitoring capabilities, which focus on providing deep insights through automatic root cause analysis and problem detection.
Scalability: Both Datadog and Dynatrace are highly scalable, capable of handling large volumes of data and providing real-time insights. However, Dynatrace is particularly renowned for its ability to scale effortlessly and handle complex distributed environments and microservices architectures.
Integrations: Datadog offers an extensive library of integrations with popular tools and platforms, making it easy to consolidate and analyze data from multiple sources. Dynatrace also provides a range of integrations, but it primarily focuses on its AI and automation capabilities to deliver insights and intelligent automation within its platform.
Pricing model: Datadog follows a usage-based pricing model, where users pay based on the volume of data ingested and the features used. This can be advantageous for organizations with unpredictable or fluctuating workloads. In contrast, Dynatrace generally utilizes a subscription-based pricing model, which may be more suitable for organizations with stable and predictable workloads.

In summary, Datadog and Dynatrace both have their own strengths and are suitable for different use cases. Datadog offers ease of use and flexibility, making it a popular choice for organizations with limited technical expertise. On the other hand, Dynatrace is known for its advanced AI-powered monitoring capabilities and scalability, making it a preferred option for organizations with complex environments.

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Advice on Datadog, Dynatrace

Farzeem Diamond

Software Engineer at IVP

Jul 21, 2020

Needs adviceon

Datadog

Dynatrace

AppDynamics

Hey there! We are looking at Datadog, Dynatrace, AppDynamics, and New Relic as options for our web application monitoring.

Current Environment: .NET Core Web app hosted on Microsoft IIS

Future Environment: Web app will be hosted on Microsoft Azure

Tech Stacks: IIS, RabbitMQ, Redis, Microsoft SQL Server

Requirement: Infra Monitoring, APM, Real - User Monitoring (User activity monitoring i.e., time spent on a page, most active page, etc.), Service Tracing, Root Cause Analysis, and Centralized Log Management.

Please advise on the above. Thanks!

1.59M views1.59M

Comments

Medeti

Jun 27, 2020

Needs adviceon

Amazon EKS

Kubernetes

AWS Elastic Load Balancing (ELB)

We are looking for a centralised monitoring solution for our application deployed on Amazon EKS. We would like to monitor using metrics from Kubernetes, AWS services (NeptuneDB, AWS Elastic Load Balancing (ELB), Amazon EBS, Amazon S3, etc) and application microservice's custom metrics.

We are expected to use around 80 microservices (not replicas). I think a total of 200-250 microservices will be there in the system with 10-12 slave nodes.

We tried Prometheus but it looks like maintenance is a big issue. We need to manage scaling, maintaining the storage, and dealing with multiple exporters and Grafana. I felt this itself needs few dedicated resources (at least 2-3 people) to manage. Not sure if I am thinking in the correct direction. Please confirm.

You mentioned Datadog and Sysdig charges per host. Does it charge per slave node?

1.51M views1.51M

Comments

Benoit

Principal Engineer at Sqreen

Sep 17, 2019

Decided

I chose Datadog APM because the much better APM insights it provides (flamegraph, percentiles by default).

The drawbacks of this decision are we had to move our production monitoring to TimescaleDB + Telegraf instead of NR Insight

NewRelic is definitely easier when starting out. Agent is only a lib and doesn't require a daemon

457k views457k

Comments

Detailed Comparison

Datadog	Dynatrace
Datadog is the leading service for cloud-scale monitoring. It is used by IT, operations, and development teams who build and operate applications that run on dynamic or hybrid cloud infrastructure. Start monitoring in minutes with Datadog!	It is an AI-powered, full stack, automated performance management solution. It provides user experience analysis that identifies and resolves application performance issues faster than ever before.
14-day Free Trial for an unlimited number of hosts;200+ turn-key integrations for data aggregation;Clean graphs of StatsD and other integrations;Slice and dice graphs and alerts by tags, roles, and more;Easy-to-use search for hosts, metrics, and tags;Alert notifications via e-mail and PagerDuty;Receive alerts on any metric, for a single host or an entire cluster;Full API access in more than 15 languages;Overlay metrics and events across disparate sources;Out-of-the-box and customizable monitoring dashboards;Easy way to compute rates, ratios, averages, or integrals;Sampling intervals of 10 seconds;Mute all alerts with 1 click during upgrades and maintenance;Tools for team collaboration	-
Statistics
Stacks 9.8K	Stacks 337
Followers 8.2K	Followers 348
Votes 861	Votes 28
Pros & Cons
Pros 140 Monitoring for many apps (databases, web servers, etc) 107 Easy setup 87 Powerful ui 84 Powerful integrations 70 Great value Cons 20 Expensive 4 No errors exception tracking 2 External Network Goes Down You Wont Be Logging 1 Complicated	Pros 4 Automated RCA 4 Real User Monitoring 3 Out-of-the-box distributed transaction tracing 2 AI-powered platform 2 Extensible via SDK Cons 0 AI-powered platform 0 Applications & Microservices 0 Infrastructure Monitoring 0 Application Security 0 Real User Monitoring
Integrations
NGINX Google App Engine Apache HTTP Server Java Docker Pingdom MySQL Ruby Python Memcached	No integrations available

What are some alternatives to Datadog, Dynatrace?

New Relic

The world’s best software and DevOps teams rely on New Relic to move faster, make better decisions and create best-in-class digital experiences. If you run software, you need to run New Relic. More than 50% of the Fortune 100 do too.

Raygun

Raygun gives you a window into how users are really experiencing your software applications. Detect, diagnose and resolve issues that are affecting end users with greater speed and accuracy.

AppSignal

AppSignal gives you and your team alerts and detailed metrics about your Ruby, Node.js or Elixir application. Sensible pricing, no aggressive sales & support by developers.

AppDynamics

AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.

Stackify

Stackify offers the only developers-friendly innovative cloud based solution that fully integrates application performance management (APM) with error and log. Allowing them to easily monitor, detect and resolve application issues faster

Skylight

Skylight is a smart profiler for your Rails apps that visualizes request performance across all of your servers.

Librato

Librato provides a complete solution for monitoring and understanding the metrics that impact your business at all levels of the stack. We provide everything you need to visualize, analyze, and actively alert on the metrics that matter to you.

Keymetrics

PM2 is a production process manager for Node.js applications with a built-in load balancer. It allows you to keep applications alive forever, to reload them without downtime and to facilitate common system admin tasks.

SignalFx

We provide operational intelligence for today’s elastic architectures through monitoring specifically designed for microservices and containers with: -powerful and proactive alerting -metrics aggregation -visualization into time series data

Scout

Scout APM helps developers quickly pinpoint & resolve performance issues before the customer ever sees them. Spend less time debugging & more time building with a streamlined interface & tracing logic that ties bottlenecks to source code.

Related Comparisons

Datadog vs Dynatrace: What are the differences?

Comparison between Datadog and Dynatrace

Ease of use: Datadog offers a user-friendly interface and is known for its easy setup process, making it a popular choice for organizations with limited technical expertise. On the other hand, Dynatrace is more comprehensive and feature-rich, but its complexity may require more technical proficiency to fully utilize its capabilities.
Deployment flexibility: Datadog provides a wide range of deployment options, including on-premises, cloud, and hybrid cloud deployments. This flexibility allows users to choose the best option for their specific needs. In contrast, Dynatrace focuses primarily on a Software as a Service (SaaS) model, providing a cloud-based solution.
Monitoring capabilities: Datadog offers a broad range of monitoring capabilities, including infrastructure monitoring, application monitoring, and log management. It also supports a wide variety of technologies, platforms, and frameworks. Dynatrace, on the other hand, is known for its advanced AI-powered monitoring capabilities, which focus on providing deep insights through automatic root cause analysis and problem detection.
Scalability: Both Datadog and Dynatrace are highly scalable, capable of handling large volumes of data and providing real-time insights. However, Dynatrace is particularly renowned for its ability to scale effortlessly and handle complex distributed environments and microservices architectures.
Integrations: Datadog offers an extensive library of integrations with popular tools and platforms, making it easy to consolidate and analyze data from multiple sources. Dynatrace also provides a range of integrations, but it primarily focuses on its AI and automation capabilities to deliver insights and intelligent automation within its platform.
Pricing model: Datadog follows a usage-based pricing model, where users pay based on the volume of data ingested and the features used. This can be advantageous for organizations with unpredictable or fluctuating workloads. In contrast, Dynatrace generally utilizes a subscription-based pricing model, which may be more suitable for organizations with stable and predictable workloads.

Datadog vs Dynatrace

Overview

Datadog vs Dynatrace: What are the differences?