Splunk vs ELK comparison

Splunk vs
ELK comparison

Splunk and ELK comparison

Decoding log management: Splunk and ELK stack face-off.

Log files are an essential data source because of the information about the health of your systems and applications. A typical a logfile data file is a text record of every event in the system with a detailed description and the exact time when it occurred. In such a way, when something goes wrong inside the operating system logs, you know exactly what happened before the malfunction.

It was never easy to manage log files. Switching to a cloud-native application for log analysis became even harder since cloud solutions for log analytics are dynamic, distributed, and ephemeral. Unlike traditional applications, cloud-native software runs in containers and emits logs to standard output instead of recording them to log files. The storage, aggregation, and import data parsing, search, and analysis of cloud-generated logs require a centralized log management solution.

There are currently two leading providers of log management services on the market: Splunk vs. Elastic (ELK) Stack. Both are mature technologies with a long history, various features, and their pros and cons. As is demonstrated by Google trends, both Splunk and Elastic are equally desirable by the IT community. The excellent reputation of both technologies makes it hard to choose between them. Read further to find a detailed comparison of ELK Stack vs. Splunk that will help with a conscious decision.

What is Splunk?

Splunk Inc. is an American technology company founded in 2003 that provides services of machine-generated data management. The company’s main product is the Splunk platform – a cloud-based solution for expansive data access, robustness, and automation.

The platform’s features include data streaming, federated search and an analytics engine, remote orchestration, and machine analytics engine and learning. Splunk platform has excellent integration with AWS and Cisco.

Analysis and visualization of data loss the pre-built dashboards and reports from AWS services like:

  • AWS CloudTrail
  • AWS Config
  • AWS Config Rules

These are organized through a particular Splunk App for AWS. Mapping Cisco ASA device events to the Splunk CIM is facilitated with a Splunk Add-on for Cisco ASA. The other Splunk products are Splunk Security, IT Operations, and Observatory.

What is ELK stack?

ELK is an acronym for three products connected in one stack: Elasticsearch, Logstash, and Kibana. The project’s history starts in 2000 when Shay Banon designed and open-sourced a distributed search- engine called Elastic. The tool attracted a strong community of developers who founded a distributed search- company.

In 2012, Jordan Sissel created Logstash, a server‑side data processing pipeline that simultaneously sends information from several data sources together, modifies it, and stores it at Elasticsearch. In the same year, Rashid Khan developed Kibana – a UI for data visualization with charts and graphs. In 2015, the ELK stack turned into a SaaS business model and became available as a service on AWS through Elastic Cloud.


Data loading and log data

To start processing the data, you should take machine data and upload it to the software. The mechanics of data loading works differently in Splunk and ELK.

In Splunk, the data can be uploaded in three different ways:

  • Upload an existing file or archive to the platform for indexing. Keep in mind that it doesn’t monitor the file for updates.
  • Monitor option to keep track of files, directories, network streams, and scripts.
  • Forward option to ingest information from forwarders.

Within the ELK Stack, the software responsible for data streaming is Elastic Beats and Logstash. The first is a lightweight data shipper designed for devices with insufficient hardware resources, like IoT or embedded devices. The price of the possibility to run in resource-constrained systems is the cut of functionality.

If you need more features, like enriching files by performing lookups against external data sources, you may also use Logstash. It is a solid and agile tool to read, process, and ship data. However, since Logstash requires powerful hardware, it can’t be deployed on low-resource devices.

Opposing the data loading mechanics of Splunk vs. ELK, we can say that the Splunk user interface is more convenient and requires fewer computing resources.

Data mapping and log management

Every document is a collection of fields. Those fields may contain data of different types, like a number, boolean, date, keyword, spatial point, etc. Data mapping defines how areas of json document that collect data are stored, indexed, and identified.

Elasticsearch supports two different ways of mapping: dynamic and explicit. Each method provides its benefits depending on where you are in your data research. Dynamic mapping offers more possibilities for experimenting and is especially useful at the beginning of the study.

No prior knowledge about the structure of the data you own is required. The new fields are added automatically just by indexing a document. However, sometimes Elasticsearch fails to identify the data type correctly. In such cases, explicit mapping is recommended. With detailed mapping, you determine which fields should be treated as text, numbers, dates, and so on by yourself.

In Splunk, the data is indexed automatically. You must set up only a few minor things like source, host, and source type to get started. The Splunk platform can index data of any kind: network events, feeds, Microsoft Windows event logs, live application logs, web server logs, metrics, change monitoring, data breaches, etc. While indexing data, Splunk breaks the document into events based on the timestamps. A single event usually corresponds to a single line in the input. However, some lines indexed data, like XML logs, have multiline events. Related events are classified together as the same event types.

A disadvantage of ELK vs. Splunk is that ELK requires more space on the drive because of its dynamic data exploration and mapping strategy. Since ELK data mapping transforms the raw data and adds extra fields and tags during search queries, the initial file size may grow ten times! In contrast, Splunk stores only the raw data. All of the additional information is added during the search.

Search processing language capabilities

The search function is a core feature of every log files management software. Both Splunk and ELK Stack have a dedicated search field. The search syntax is derived from Lucene query syntax. For those familiar with scripting languages, like Ruby or Python, learning ELK query syntax will be easy.

In contrast, using the proprietary search language of Splunk will require additional effort. The platform applies its proprietary Search Processing Language, which must be studied. The good news is that mastering SPL is worth the struggle. It is more convenient and guarantees better performance. SPL provides the search pipeline in which successive commands are tied together. A pipe character permits the use of the output of one command as the input of the next one.

Visualization and data processing pipeline

The understanding of collected information depends a lot on the data visualization tool. A right graph or plot allows for communication of the result of the data study even to an unprofessional audience. A rich choice of data visualization tools provides more possibilities for adaptation and makes the data collection and representation more agile. With this thought in mind, both Splunk and ELK Stack offer sophisticated instruments for the graphical representation of search results from inline searches, reports, and pivots.

Splunk provides data analysis options such as area charts, Choropleth maps, tables, single-value visualizations, etc. Each dashboard panel supports one or more visualizations. In Splunk, visualizations are created with the help of Simple XML code, which you can edit and customize as you wish. Splunk’s data visualization possibilities are enforced by community-created add-ons that may be acquainted with the Splunk base. Currently, there are 79 applications available for download.

In ELK, data and business analytics are visualized with the help of Kibana software. The basic Kibana features include line graphs, scatter plots, pie charts, bar graphs, histograms, sunbursts, and more. Advanced data engineers may also perform location analysis, time series analysis, transfers data mine, and explore graphs and networks. The advantage of Kibana is the real-time updates and summary of the operating data. A weak side of the ELK is the absence of Solaris portability.Splunk-ELK-comparisonComparing Kibana vs. Splunk, one can say that both platforms are equally good at visualizing data. Created illustrations are bright, elegant, and comprehensive.


The primary purpose of log file management is to enhance the management of security information and event management (SIEM). Both ELK Stack and Splunk are recognized as industry chiefs and earned their place in Gartner’s Magic Quadrant for SIEM.

In Gartner’s opinion, Splunk is the leader in Quadrant. It is solid and reliable. Splunk’s main competitive feature is a variety of out-of-the-box integrations and community support provided through Splunkbase apps, Phantom integrations, APIs, and Mission Control Plug-in Frameworks. A disadvantage of Splunk is the need for an utterly cloud-native security operations suite. Although Enterprise Security is delivered via Splunk Cloud, the delivery of Splunk UBA and Phantom requires deployment on the client’s cloud.

As for the ELK Stack, Gartner’s experts call it the Niche Player with various sources for detection content. The ELK Stack includes its cyber-safety tool called Elastic Security (former Endgame) but also community add-on may also be acquainted. The features of Kibana Lens provide threat-hunting activities that combine drag-and-drop visualization ability with native and search capabilities.

The disadvantage of ELK Stack is the need for packaged compliance dashboards flexible controls and reports. Moreover, the user experience could be more consistent across the different elements of the ELK Stack. Some functions can be operated only via developer tools, while others are controlled through a task-specific graphic interface.


The price of a product is another critical factor for choosing a log management and analysis platform. Splunk vs. Elasticsearch: which log analysis one is cheaper?

Since its launch, Splunk has been distributed by the paid license. Each of the products provided by the company has its pricing plan. A monthly subscription for the Splunk Observability Cloud will cost $65 per host per month. The payment for Splunk Infrastructure Monitoring is $ 15$ per host per month, and the payment for Splunk Log Observer is $6.25.

Unlike Splunk, Elastic was an open-source software developer all-source product for a long time. The situation changed on January 21, 2021, when Elastic NV announced a switch in software licensing strategy. New versions of Elasticsearch and Kibana will not be released under the permissive Apache License, Version 2.0 (ALv2) license.

Instead, the Elastic License or SSPL will be used. These licenses are not open-source and do not offer users the same freedoms. The most convenient way to exploit Elastic is Elastic Cloud. The price of a Standard license is only $16 per month. The Enterprise license costs $30 per month. In such a way, although Elastic is a proprietary software no longer an open-source product, it is still cheaper than Splunk.


As an older platform, Splunk has gathered a larger community of supporters than ELK. For example, there are more than 1,700 Splunk-related questions on StackOverflow and only 750 ELK-related inquiries. At the same time, according to Stackshare, more enterprises use ELK as their tech stack than Splunk. 151 company applies ELK, including Robinhood, RD StationResearchGate, and iHerb. To compare, Splunk is used by 75 companies such as SlackLenovoIMDEXHonda.

It is equally easy to start working with Splunk and ELK. Both companies provide online studying and certificate programs for beginning and advanced users. You can organize a live training event as well. Conferences, where engineers share their experiences, are also held. The ElasticON Global conference usually happens at the beginning of October, and Splunk’s .conf is scheduled for the end of the month.

Elasticsearch vs. Splunk: which is better?

The answer to the question of which log management solutions play or platform is better depends on your business needs. ELK Stack suits well companies that are tight on budget. Splunk is more expensive and consumes more resources. DevOps, who wish more control over the setting might like ELK Stack. Those engineers who desire an out-of-the-box solution will appreciate Splunk. If a cloud SaaS satisfies your security needs, you should stick with Splunk. If you need to deploy the log management software on-premises, you should choose ELK. Both Splunk and ELK provide sophisticated instruments for graphic representation of the processed log data.

Whatever decision you make, be sure that you will receive a great tool to monitor the health and safety of ingested data in your system! Contact us right away and get your hands on one of such tools.

Other articles


Book a meeting

Call (Toll-Free*) +1 (800) 917-0207

Zoom 30 min

* US and Canada, exceptions apply

Ready to innovate your business?

We are! Let’s kick-off our journey to success!