Datadog is a monitoring service for IT, Dev and Ops teams who write and run applications at scale, and want to turn the massive amounts of data produced by their apps, tools and services into actionable insight.
$18
per month per host
Elasticsearch
Score 8.7 out of 10
N/A
Elasticsearch is an enterprise search tool from Elastic in Mountain View, California.
$16
per month
Splunk Cloud Platform
Score 8.0 out of 10
N/A
Splunk Cloud Platform is a data platform service thats help users search, analyze, visualize and act on data. The service can go live in as little as two days, and with an IT backend managed by Splunk experts.
N/A
Pricing
Datadog
Elasticsearch
Splunk Cloud Platform
Editions & Modules
Log Management
$1.27
per month (billed annually) per host
Infrastructure
$15.00
per month (billed annually) per host
Standard
$18
per month per host
Enterprise
$27
per month per host
DevSecOps Pro
$27
per month per host
APM
$31.00
per month (billed annually) per host
DevSecOps Enterprise
$41
per month per host
Standard
$16.00
per month
Gold
$19.00
per month
Platinum
$22.00
per month
Enterprise
Contact Sales
No answers on this topic
Offerings
Pricing Offerings
Datadog
Elasticsearch
Splunk Cloud Platform
Free Trial
Yes
No
No
Free/Freemium Version
Yes
No
No
Premium Consulting/Integration Services
No
No
No
Entry-level Setup Fee
Optional
No setup fee
No setup fee
Additional Details
Discount available for annual pricing. Multi-Year/Volume discounts available (500+ hosts/mo).
In terms of usability, I’ve found Datadog significantly more approachable and powerful compared to Elasticsearch, especially for day-to-day operational monitoring. Datadog offers a much more cohesive, user-friendly interface out of the box, with built-in support for metrics, …
Datadog crushed the competition on price and offering more solutions in one product cutting down on implementation time and effort while ensuring that the "integration" between one of their offerings was completely compatible with any of the others. I'm sure it's not the case …
Datadog has been harder to setup out-of-the-box compared to its alternatives, although it's graphs and dashboards have been more useful. Other tools handle individual tasks better. For example, Splunk has been the best logging tool I've used, and New Relic is great for CPU and …
From my perspective, there is nothing currently on the marker better than Datadog, but unfortunately, that's a pricey product, Elasticsearch deliver us part of Datadog functionalities being cheaper. Fluentd as a service (provided by the company behind Fluentd) looks like a …
Search and analytics capabilities of Elasticsearch are superior to its competitors. Being open source, it is a cheaper and faster solution than other competitors. Installation is straightforward and it can be potentially deployed anywhere and everywhere! There is no need for …
I think Elasticseach works less great compared to Splunk. Mainly the way the Splunk search head works is vastly superior to the way the Elasticsearch query language works. Furthermore, the Splunk architecture is in my opinion easier to roll out and scale-up. Splunk also has a …
We selected Splunk Cloud due to the simplicity to use and get data in. We found that Splunk Cloud gives a unified simple searching and dashboarding interface which can be used to search and visualise data from multiple systems with ease.
All the products in this category do log aggregation very well, however the winning factor was that we have experience with Splunk already and this has proved invaluable as Splunk has a steep learning curve. Especially the Splunk administration part of the tool as that is a …
I feel that the alternatives are great products and they are also things we use in our own monitoring in addition to Splunk. However, when it comes to finding things in the data as opposed to just looks for trends the competition just doesn't even come close. Splunk has become …
I believe there is no existing competitor of Splunk and the way Splunk Cloud provides support is way better than all the other competitors. No one can beat Splunk Cloud!!
Other solutions weren't able to consume the volume of logs that we were producing on a daily basis. Searching was difficult because of proprietary or simply confusing search mechanisms. Splunk simplified the searching by using regular expressions. Although the cost of Splunk …
Datadog may be better suited for teams that have a more out-of-the-box infrastructure, on the primary platforms Datadog supports. You may also have better results if you have a bigger team dedicated to devops and/or a bigger budget. We found that trying to adapt it to our use case (small team, .NET on AWS Fargate) wasn't feasible. We continually ran into roadblocks that required us to dig through documentation (and at times, having to figure out some documentation was wrong), go back and forth with support, and in my opinion, waste money on excessive and unintended usages due to opaque pricing models and inaccurate usage reports, as well as broken/non-functional rate sampling controls.
Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding & infrastructure you will need for your instance to be running correctly. Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly. Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it. Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.
Splunk is excellent when all your data is in one location. Its ability to correlate all that data is intuitive (once the hurdle of learning the query language is overcome). It is also easy to standardize the presentation of information to the company. When data is siloed/standalone, other systems can be cheaper and faster to implement.
The thing which Datadog does really well, one of them are its broad range of services integrations and features which makes it one step observability solution for all. We can monitor all types of our application, infrastructure, hosts, databases etc with Datadog.
Its custom dashboard feature which helps us to visualize the data in a better way . It supports different types of charts through those charts we can create our dashboard more attractive.
Its AI powered alerting capability though that we can easily identify the root cause and also it has a low noise alerting capability which means it correlated the similar type of issues.
As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.
This SIEM consolidates multiple data points and offers several features and benefits, creating custom dashboards and managing alert workflows.
Splunk Cloud provides a simple way to have a central monitoring and security solution. Though it does not have a huge learning curve, you should spend some time learning the basics.
Splunk Cloud enables me to create and schedule statistical reports on network use for Management.
Alert windows cause lag in notifications (e.g. if the alert window is X errors in 1 hour, we won't get alerted until the end of the 1 hour range)
I would appreciate more supportive examples for how to filter and view metrics in the explorer
I would like a more clear interface for metrics that are missing in a time frame, rather than only showing tags/etc. for metrics that were collected within the currently viewed time frame
There are so many features that it can be hard to figure out where you need to go for your own use case. For example, RUM monitoring us buried in a "Digital Experience" sidebar setting when this is one of our key use cases that I sometimes struggle to find in the application. It appears that ECS + Fargate monitoring was recently released which is great because we had to build a lambda reporting solution for ephemeral task monitoring. But this new feature was never on my radar until I starting clicking around the application.
To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.
The support team usually gets it right. We did have a rather complicate issue setting up monitoring on a domain controller. However, they are usually responsive and helpful over chat. The downside would be I don’t think they have any phone support. If that is important to you this might not be a good fit.
We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.
Splunk Cloud support is sorely lacking unfortunately. The portal where you submit tickets is not very good and is lacking polish. Tickets are left for days without any updates and when chased it is only sometimes you get a reply back. I get the feeling the support team are very understaffed and have far too much going on. From what I know, Splunk is aware of this and seem to be trying to remedy it.
Our logs are very important, and Datadog manages them exceptionally well. We frequently use Datadog services for our investigations. Use case: Monitor your apps, infrastructure, APIs, and user experience.
Key features:
Logs, metrics, and APM (Application Performance Monitoring)
Real-time alerting and dashboards
Supports Kubernetes, AWS, GCP, and other integrations
RUM (Real User Monitoring) and Synthetics
✅ Best for backend, server, and distributed systems monitoring.
As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.
Search Processing Language really is a game changer for writing easy-to-understand and maintainable queries on your data base logs. Once understood, setting up and validating a query can be done in no time- which leaves us the option to focus on more monitoring and improved services. We have no other tools that utilizes data this efficiently
We have had great luck with implementing Elasticsearch for our search and analytics use cases.
While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.
We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.
