Need advice about which tool to choose?Ask the StackShare community!

Datadog

9.4K
8K
+ 1
860
Kibana

20.4K
16.2K
+ 1
262
Add tool

Datadog vs Kibana: What are the differences?

Both Datadog and Kibana are widely used tools for monitoring and analyzing data in the field of IT operations and management. Let's explore the key differences between Datadog and Kibana:

  1. Architecture: Datadog is a fully managed cloud-based monitoring platform, whereas Kibana is a component of the Elastic Stack, which includes Elasticsearch and Logstash. Datadog provides a ready-to-use solution with an intuitive user interface, while Kibana requires the setup and configuration of the entire Elastic Stack.

  2. Data Collection: Datadog supports a wide range of data collection methods, including various agent-based integrations, APIs, and client libraries. On the other hand, Kibana relies on Logstash for data ingestion and Elasticsearch for data storage.

  3. Visualization Capabilities: Datadog offers a comprehensive set of visualization tools, allowing users to create interactive dashboards, charts, and graphs. Kibana specializes in data exploration and visualization, providing advanced filtering, aggregations, and time series analysis options.

  4. Alerting and Notification: Datadog provides a robust alerting system that allows users to set up custom alerts based on predefined conditions. It offers various notification channels such as email, Slack, and PagerDuty. Kibana, on the other hand, lacks a built-in alerting mechanism. Users usually rely on third-party tools or custom solutions for setting up alerts.

  5. Community and Integration Ecosystem: Datadog has a large and active community, constantly developing and sharing integrations with popular technologies. It provides seamless integration with various third-party services and tools. Kibana benefits from the extensive Elastic Stack community and its integration ecosystem, which includes Elasticsearch, Beats, and Logstash.

  6. Cost Structure: Datadog follows a subscription-based pricing model, offering different plans based on the size and needs of the organization. Kibana, being open-source, is free to use, but the associated infrastructure costs (Elasticsearch, Logstash, etc.) might increase depending on the scale and requirements.

In summary, Datadog is a fully managed cloud-based monitoring platform with comprehensive visualization and alerting capabilities, while Kibana is a component of the Elastic Stack, known for its powerful data exploration and visualization features. Datadog simplifies the setup process with ready-to-use solutions, whereas Kibana requires the configuration of the entire stack. Datadog has a wide range of data collection methods and integrations, while Kibana relies on Elasticsearch and Logstash.

Advice on Datadog and Kibana
Farzeem Diamond Jiwani
Software Engineer at IVP · | 8 upvotes · 1.5M views
Needs advice
on
AppDynamicsAppDynamicsDatadogDatadog
and
DynatraceDynatrace

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!

See more
Needs advice
on
DatadogDatadogNew RelicNew Relic
and
SysdigSysdig

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?

See more
Replies (3)
Recommends
on
DatadogDatadog

Can't say anything to Sysdig. I clearly prefer Datadog as

  • they provide plenty of easy to "switch-on" plugins for various technologies (incl. most of AWS)
  • easy to code (python) agent plugins / api for own metrics
  • brillant dashboarding / alarms with many customization options
  • pricing is OK, there are cheaper options for specific use cases but if you want superior dashboarding / alarms I haven't seen a good competitor (despite your own Prometheus / Grafana / Kibana dog food)

IMHO NewRelic is "promising since years" ;) good ideas but bad integration between their products. Their Dashboard query language is really nice but lacks critical functions like multiple data sets or advanced calculations. Needless to say you get all of that with Datadog.

Need help setting up a monitoring / logging / alarm infrastructure? Send me a message!

See more
Maik Schröder
Recommends
on
InstanaInstana

Hi Medeti,

you are right. Building based on your stack something with open source is heavy lifting. A lot of people I know start with such a set-up, but quickly run into frustration as they need to dedicated their best people to build a monitoring which is doing the job in a professional way.

As you are microservice focussed and are looking for 'low implementation and maintenance effort', you might want to have a look at INSTANA, which was built with modern tool stacks in mind. https://www.instana.com/apm-for-microservices/

We have a public sand-box available if you just want to have a look at the product once and of course also a free-trial: https://www.instana.com/getting-started-with-apm/

Let me know if you need anything on top.

See more
Attila Fulop
Management Advisor at artkonekt · | 2 upvotes · 338.7K views

I have hands on production experience both with New Relic and Datadog. I personally prefer Datadog over NewRelic because of the UI, the Documentation and the overall user/developer experience.

NewRelic however, can do basically the same things as Datadog can, and some of the features like alerting have been present in NewRelic for longer than in Datadog. The cool thing about NewRelic is their last-summer-updated pricing: you no longer pay per host but after data you send towards New Relic. This can be a huge cost saver depending on your particular setup

https://docs.newrelic.com/docs/accounts/accounts-billing/new-relic-one-pricing-billing/new-relic-one-pricing-billing

I'd go for Datadog, but given you have lots of containers I would also make a cost calculation. If the price difference is significant and there's a budget constraint NewRelic might be the better choice.

See more
Needs advice
on
GrafanaGrafana
and
KibanaKibana

From a StackShare Community member: “We need better analytics & insights into our Elasticsearch cluster. Grafana, which ships with advanced support for Elasticsearch, looks great but isn’t officially supported/endorsed by Elastic. Kibana, on the other hand, is made and supported by Elastic. I’m wondering what people suggest in this situation."

See more
Replies (7)
Recommends
on
GrafanaGrafana
at

For our Predictive Analytics platform, we have used both Grafana and Kibana

Kibana has predictions and ML algorithms support, so if you need them, you may be better off with Kibana . The multi-variate analysis features it provide are very unique (not available in Grafana).

For everything else, definitely Grafana . Especially the number of supported data sources, and plugins clearly makes Grafana a winner (in just visualization and reporting sense). Creating your own plugin is also very easy. The top pros of Grafana (which it does better than Kibana ) are:

  • Creating and organizing visualization panels
  • Templating the panels on dashboards for repetetive tasks
  • Realtime monitoring, filtering of charts based on conditions and variables
  • Export / Import in JSON format (that allows you to version and save your dashboard as part of git)
See more
Recommends
on
KibanaKibana

I use both Kibana and Grafana on my workplace: Kibana for logging and Grafana for monitoring. Since you already work with Elasticsearch, I think Kibana is the safest choice in terms of ease of use and variety of messages it can manage, while Grafana has still (in my opinion) a strong link to metrics

See more
Bram Verdonck
Recommends
on
GrafanaGrafana
at

After looking for a way to monitor or at least get a better overview of our infrastructure, we found out that Grafana (which I previously only used in ELK stacks) has a plugin available to fully integrate with Amazon CloudWatch . Which makes it way better for our use-case than the offer of the different competitors (most of them are even paid). There is also a CloudFlare plugin available, the platform we use to serve our DNS requests. Although we are a big fan of https://smashing.github.io/ (previously dashing), for now we are starting with Grafana .

See more
Recommends
on
KibanaKibana

I use Kibana because it ships with the ELK stack. I don't find it as powerful as Splunk however it is light years above grepping through log files. We previously used Grafana but found it to be annoying to maintain a separate tool outside of the ELK stack. We were able to get everything we needed from Kibana.

See more
Recommends
on
KibanaKibana

Kibana should be sufficient in this architecture for decent analytics, if stronger metrics is needed then combine with Grafana. Datadog also offers nice overview but there's no need for it in this case unless you need more monitoring and alerting (and more technicalities).

See more
Recommends
on
GrafanaGrafana

I use Grafana because it is without a doubt the best way to visualize metrics

See more
Povilas Brilius
PHP Web Developer at GroundIn Software · | 0 upvotes · 631.8K views
Recommends
on
KibanaKibana
at

@Kibana, of course, because @Grafana looks like amateur sort of solution, crammed with query builder grouping aggregates, but in essence, as recommended by CERN - KIbana is the corporate (startup vectored) decision.

Furthermore, @Kibana comes with complexity adhering ELK stack, whereas @InfluxDB + @Grafana & co. recently have become sophisticated development conglomerate instead of advancing towards a understandable installation step by step inheritance.

See more
Decisions about Datadog and Kibana
Kamil Kowalski
Lead Architect at Fresha · | 3 upvotes · 234.4K views

Coming from a Ruby background, we've been users of New Relic for quite some time. When we adopted Elixir, the New Relic integration was young and missing essential features, so we gave AppSignal a try. It worked for quite some time, we even implemented a :telemetry reporter for AppSignal . But it was difficult to correlate data in two monitoring solutions, New Relic was undergoing a UI overhaul which made it difficult to use, and AppSignal was missing the flexibility we needed. We had some fans of Datadog, so we gave it a try and it worked out perfectly. Datadog works great with Ruby , Elixir , JavaScript , and has powerful features our engineers love to use (notebooks, dashboards, very flexible alerting). Cherry on top - thanks to the Datadog Terraform provider everything is written as code, allowing us to collaborate on our Datadog setup.

See more
Leonardo Henrique da Paixão
Junior QA Tester at SolarMarket · | 15 upvotes · 382.1K views

The objective of this work was to develop a system to monitor the materials of a production line using IoT technology. Currently, the process of monitoring and replacing parts depends on manual services. For this, load cells, microcontroller, Broker MQTT, Telegraf, InfluxDB, and Grafana were used. It was implemented in a workflow that had the function of collecting sensor data, storing it in a database, and visualizing it in the form of weight and quantity. With these developed solutions, he hopes to contribute to the logistics area, in the replacement and control of materials.

See more
Attila Fulop

I haven't heard much about Datadog until about a year ago. Ironically, the NewRelic sales person who I had a series of trainings with was trash talking about Datadog a lot. That drew my attention to Datadog and I gave it a try at another client project where we needed log handling, dashboards and alerting.

In 2019, Datadog was already offering log management and from that perspective, it was ahead of NewRelic. Other than that, from my perspective, the two tools are offering a very-very similar set of tools. Therefore I wouldn't say there's a significant difference between the two, the decision is likely a matter of taste. The pricing is also very similar.

The reasons why we chose Datadog over NewRelic were:

  • The presence of log handling feature (since then, logging is GA at NewRelic as well since falls 2019).
  • The setup was easier even though I already had experience with NewRelic, including participation in NewRelic trainings.
  • The UI of Datadog is more compact and my experience is smoother.
  • The NewRelic UI is very fragmented and New Relic One is just increasing this experience for me.
  • The log feature of Datadog is very well designed, I find very useful the tagging logs with services. The log filtering is also very awesome.

Bottom line is that both tools are great and it makes sense to discover both and making the decision based on your use case. In our case, Datadog was the clear winner due to its UI, ease of setup and the awesome logging and alerting features.

See more
Benoit Larroque
Principal Engineer at Sqreen · | 4 upvotes · 436.6K views

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

See more
Manage your open source components, licenses, and vulnerabilities
Learn More
Pros of Datadog
Pros of Kibana
  • 139
    Monitoring for many apps (databases, web servers, etc)
  • 107
    Easy setup
  • 87
    Powerful ui
  • 84
    Powerful integrations
  • 70
    Great value
  • 54
    Great visualization
  • 46
    Events + metrics = clarity
  • 41
    Notifications
  • 41
    Custom metrics
  • 39
    Flexibility
  • 19
    Free & paid plans
  • 16
    Great customer support
  • 15
    Makes my life easier
  • 10
    Adapts automatically as i scale up
  • 9
    Easy setup and plugins
  • 8
    Super easy and powerful
  • 7
    AWS support
  • 7
    In-context collaboration
  • 6
    Rich in features
  • 5
    Docker support
  • 4
    Cost
  • 4
    Full visibility of applications
  • 4
    Monitor almost everything
  • 4
    Cute logo
  • 4
    Automation tools
  • 4
    Source control and bug tracking
  • 4
    Simple, powerful, great for infra
  • 4
    Easy to Analyze
  • 4
    Best than others
  • 3
    Best in the field
  • 3
    Expensive
  • 3
    Good for Startups
  • 3
    Free setup
  • 2
    APM
  • 88
    Easy to setup
  • 65
    Free
  • 45
    Can search text
  • 21
    Has pie chart
  • 13
    X-axis is not restricted to timestamp
  • 9
    Easy queries and is a good way to view logs
  • 6
    Supports Plugins
  • 4
    Dev Tools
  • 3
    More "user-friendly"
  • 3
    Can build dashboards
  • 2
    Out-of-Box Dashboards/Analytics for Metrics/Heartbeat
  • 2
    Easy to drill-down
  • 1
    Up and running

Sign up to add or upvote prosMake informed product decisions

Cons of Datadog
Cons of Kibana
  • 20
    Expensive
  • 4
    No errors exception tracking
  • 2
    External Network Goes Down You Wont Be Logging
  • 1
    Complicated
  • 7
    Unintuituve
  • 4
    Works on top of elastic only
  • 4
    Elasticsearch is huge
  • 3
    Hardweight UI

Sign up to add or upvote consMake informed product decisions

- No public GitHub repository available -

What is Datadog?

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!

What is Kibana?

Kibana is an open source (Apache Licensed), browser based analytics and search dashboard for Elasticsearch. Kibana is a snap to setup and start using. Kibana strives to be easy to get started with, while also being flexible and powerful, just like Elasticsearch.

Need advice about which tool to choose?Ask the StackShare community!

What companies use Datadog?
What companies use Kibana?
Manage your open source components, licenses, and vulnerabilities
Learn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with Datadog?
What tools integrate with Kibana?

Sign up to get full access to all the tool integrationsMake informed product decisions

Blog Posts

May 21 2019 at 12:20AM

Elastic

ElasticsearchKibanaLogstash+4
12
5296
GitHubPythonReact+42
49
40929
GitGitHubPython+22
17
14283
GitGitHubDocker+34
29
42688
What are some alternatives to Datadog and Kibana?
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.
Splunk
It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
Prometheus
Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
Grafana
Grafana is a general purpose dashboard and graph composer. It's focused on providing rich ways to visualize time series metrics, mainly though graphs but supports other ways to visualize data through a pluggable panel architecture. It currently has rich support for for Graphite, InfluxDB and OpenTSDB. But supports other data sources via plugins.
AppDynamics
AppDynamics develops application performance management (APM) solutions that deliver problem resolution for highly distributed applications through transaction flow monitoring and deep diagnostics.
See all alternatives