Splunk vs. Elastic

Need to speed MTTx? Splunk delivers earlier detection capabilities and greater security coverage compared to Elastic, so you can respond faster.

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Elastic lacks advanced user and entity behavior analytics (UEBA), SOAR, and third-party endpoint detection and response (EDR) integration,¹ which leads to slower threat detection and response — and reduces ecosystem flexibility.

Reduce risk and improve operational efficiency with Splunk’s advanced, automated, and flexible SIEM. Splunk's user and entity behavior analytics (UEBA) quickly detects insider threats, anomalies, and sophisticated attacks, so you can identify potential risks and respond faster with SOAR to automate response where needed. In addition, Splunk integrates with most third-party endpoint detection and response (EDR) tools, giving you the flexibility to use best-in-class solutions.

When it comes to security, you don’t want to take more risks. Elastic is an emerging player in the SIEM and Security space. In the 2024 Gartner® Magic Quadrant™ for SIEM, Elastic is recognized as a Visionary, while Splunk is in the top right as Leader with both the ability to execute and completeness of vision. Forrester, IDC, and customer rating sites like PeerSpot also rank Splunk as a Leader.

Splunk is a ten-year Leader in the Gartner® Magic Quadrant™ for SIEM, recognized for our ability to execute and completeness of vision. Splunk was ranked first in every use case in the 2024 Gartner® Critical Capabilities for Security Information and Event Management report. IDC named Splunk a Leader in the IDC MarketScape: Worldwide SIEM for Enterprise 2024 Vendor Assessment Report.

Managing self-hosted Elastic deployments can be complex and requires specialized expertise to ensure smooth deployment and scaling.² These challenges can increase the total cost of ownership and delay the time to value, so it’s harder for organizations to achieve their objective quickly.

Splunk’s simpler architecture minimizes the complexity of managing deployments, so you can get up and running fast. With all-in-one data management, fewer agents, AI Assistants within your workflow, and comprehensive support, Splunk reduces the burden on IT teams and lowers the total cost of ownership.

We get so much value from Splunk. It maximizes the insights we gain from analyzing detection use cases, rather than wasting time creating rules or struggling with a tool that’s too complicated.

Romaric Ducloux, SOC Analyst, Carrefour

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Splunk vs Elastic

	Splunk	Elastic
Query Language	Splunk's SPL & SPL 2 are the only languages you need to use. Splunk is standardized on the well-documented and robust SPL standard to query and build pipelines. Splunk AI Assistant for SPL makes it easy to use and learn.	Elastic requires multiple languages, each with a different purpose depending on the use case or area of the ELK stack you are managing. That’s a steep learning curve and can quickly become cumbersome for your users, especially those new to Elastic.
Advanced Security	Splunk SOAR is mature, stable, and easy to use. Our pre-built playbooks and automated responses save time and effort. If you need more than what comes pre-built, you can customize playbooks with drag-and-drop elements and incorporate custom code to build advanced custom integrations.	Elastic does not offer a packaged SOAR product with pre-built playbooks and automated responses and integrations like Splunk. The user experience is more challenging, requiring that you write a large number of custom alerts and rules that can be difficult to manage at scale.
Edge Processing & Federated Search	With Edge Processing in Splunk Cloud and Federated Search for S3, Splunk gives you flexibility and control over where and you ingest and store your data. Splunk processes data before indexing, so you can filter low-value data to cost-efficient storage and retrieve it later with Federated Search.	Elastic’s Edge Processing requires multiple tools to enable simple routing and preprocessing of log data, slowing time to value.
Complexity & Costs	Splunk is easier to implement and manage at scale with its built-in tools for centralized management, configuration, and monitoring of large-scale deployments. Pre-built integrations, dashboards, and apps reduce the time and effort to configure Splunk for specific use cases. Splunk’s architecture is optimized for scaling, allowing you to easily add more data inputs, indexing capacity, or search head instances as your environment grows. This makes scaling predictable and manageable, so you avoid the complexities of manually tuning and configuring infrastructure for large volumes of data.	Elastic can be hard to implement and becomes very complex to scale, especially in on-premise environments. Effectively scaling Elastic requires deep technical knowledge of the product, which is not always easy to find. The niche skillsets required to scale Elastic mean organizations can see significant delays in the implementation and scaling of their elastic solutions.
OpenTelemetry Support	Splunk Observability Cloud products are fully native to OpenTelemetry (OTel), and Splunk actively contributes to the OTel project. You can seamlessly collect, process, transform, visualize, and alert on OpenTelemetry data without concerns over exceptions or specific constraints. You also have the opportunity to contribute to the community and fully leverage OpenTelemetry’s advantages.	Elastic implementation requires a proprietary agent. Elastic has opted to accept the OTel protocol within its Elastic Agent. However, being tied to a single vendor can make it harder to collect and analyze critical application data. Elastic cannot export OTel-compliant datasets, thus disabling OTel's greatest benefits: data portability and vendor agnosticism.