Why Splunk customers face a choice for observability and modernization

Elastic is built as a search analytics platform that allows ingesting, indexing, and analysis, with generative AI, of any data (logs, metrics, traces, security events, etc.) at scale. 

Elastic’s search analytics platform is built with the Elasticsearch Relevance Engine (ESRE). ESRE combines the best of AI with Elastic’s text search. Elastic’s search analytics platform processes and stores data in Elastic’s vector database. Elastic’s Learned Sparse EncodeR (ELSER) model delivers highly relevant semantic search out of the box across large volumes and low latency.

Additionally, ESRE provides a full suite of sophisticated retrieval algorithms and the ability to integrate with large language models (LLMs) such as OpenAI and others. This accelerates problem resolution, improves analysis of business data, and increases operational productivity. 

These innovations provide not only a fast, contextually relevant search for analysis but also a significant number of AI capabilities that discover, predict, and provide prescriptive guidance, accelerating operational efficiency and improving customer experiences.

Ultimately, you want to increase your productivity and focus on products and operations. Elastic allows you to:

• Improve predictability: Finding issues after the fact is the norm, but being able to prevent or even predict them is ideal. But how, and with what?

• Reduce MTTx: Whether it's mean time to repair, response, or failure, as an SRE your ultimate goal is to not only find the issue but repair it and ensure customer experience has improved. 

Elastic Observability achieves this with the following advantages:

A new AI Assistant, leveraging generative AI, helps teams respond and interact more fluidly to solve problems. The AI Assistant not only uses generative AI LLM information but also, with Elastic’s search capabilities, leverages internal information, such as run-books, customer issues, and other internal contextual information. The mechanism of using relevant internal documents with trained public LLM information is called retrieval augmented generation (RAG). This can only be achieved with an advanced search analytics platform like Elastic. 

The AI Assistant enables the following:

• Accelerated incident management and root cause analysis by analyzing logs, metrics, security, traces, profiling, code errors, customer issues, and more

• Interactive exploration of problems and execution remedies with generative AI context-aware, business-specific, and organization-specific output you can trust, based on your proprietary data and runbooks

The AI Assistant is essentially another user who can do any of the following:

• Use a natural language interface such as “Are there any alerts related to this service today?” or “Can you explain what these alerts are?” as part of problem determination and root cause analysis processes

• Offer conclusions and context, and suggest next steps and recommendations from your internal private data (powered by ELSER), as well as by information available in the connected LLM

• Analyze responses from queries and output from analysis performed by the Elastic AI Assistant

• Recall and summarize information throughout the conversation

• Generate Lens visualizations via conversation 

• Execute Kibana® and Elasticsearch® APIs on behalf of the user through the chat interface

Splunk doesn’t have a search analytics platform or the components of ESRE to enable an AI Assistant that provides all these capabilities, especially one that also uses internal data.

Elastic Observability is a full-suite solution that delivers integrated log analytics, application performance monitoring (APM), metrics, profiling, and traces in a single, fully unified platform that is part of a single deployment. All your data is in one fully distributed scalable data lake in Elastic. This allows you to:

• Eliminate data silos and gain full-scale visibility across all your environments from one place, without add-on products or pricing

• Improve collaboration and visibility into problems and issues as a team

Splunk customers, on the other hand, would need to purchase sometimes several products (multiple Splunk observability products, Splunk Cloud, and Splunk Enterprise) to achieve full observability functionality. Additionally, the data logs, for instance, are separated from traces and metrics in Splunk, potentially causing swivel chair operations (switching from screen to screen).

As we mentioned in the previous section, the AI Assistant, leveraging generative AI and ESRE, not only helps teams respond and interact more fluidly to solve problems but also helps you analyze your data more rapidly using publicly trained LLM information and internal data stored in Elastic.

Here’s how the AI Assistant helps answer some of the questions we listed above:

• What is the application's throughput, latency, etc.? AI Assistant will search all the application's APM data as well as the logs to analyze the throughput and latency.

• Is my infrastructure optimized? It will search your infrastructure metrics to see if CPU, storage, etc. are being underutilized.

• Are we spending too much money? If you are ingesting your cloud costs in Elastic, the AI Assistant can search your spending and indicate what your trend lines are.

• Are dev pipelines optimized? It can help analyze if your CICD pipelines are efficient.

Even with the AI Assistant, one advantage Elastic has is its ability to get answers to queries rapidly, either directly from the user or through the AI Assistant. 

Elastic’s real-time search queries take milliseconds, not seconds, and historical queries take minutes, not hours. Additionally, you might need to also cross-reference data from different organizations. 

With Elastic, data tiering is available for all observability data in any silo, providing greater flexibility in how you store, search, and analyze. Elastic search, analytics, and machine learning run efficiently on all data tiers and across silos.

Consistent low-latency search regardless of data size or tier: Elastic provides the ability to search across TBs of data across hot/warm/cold/frozen with query results in the 10s(ms) range. That level of consistent search results can only be achieved due to a more advanced ingest, index, and storage model. Splunk’s queries require rehydration (which can be slow) with frozen tiers.

• Real-time search for newly ingested data: Elastic stores ingested data in a high-performance, low-latency storage layer, enabling real-time search capabilities within milliseconds of data being ingested, without the need for any additional configuration. This makes it possible for organizations to derive insights and actionable information from their data in real time, without any delay or lag. Splunk’s search is varied and dependent on the commands used in its query language.

• Cold and frozen-tier low latency search: Elastic provides the ability to search cold and frozen snapshots in 10s(ms) due to its ability to keep relevant indices with the need for rehydration. Splunk requires archived data to be restored prior to querying. Data in Splunk's frozen tier must be restored before searching, and users may have to wait up to 24 hours for the data to be searchable. This time can have serious consequences when you’re facing issues that impact your customers and revenue. Splunk Cloud also doesn’t allow real-time queries by default — you would need a support ticket for that.

Consistent low-latency search across disparate silos: Elastic provides the ability to search and analyze data across multiple Elastic deployments (called cross-cluster search).

• Splunk’s federated search is built mainly for large-scale enterprise customers and only works for specific query commands. This requires you to know SPL and you aren’t guaranteed this even works across its disparate products.

Elastic’s entire platform is sold as a single SKU and priced via a transparent resource-based consumption model. This simplified approach can save you money on both licensing and infrastructure. And resource-based pricing enables cost predictability so that you don’t have to compromise on long-term data retention.

Splunk may have higher costs and a more complex pricing and licensing structure, which may come with additional infrastructure costs. 

Learn more about how Splunk compares to Elastic >>

Openness, transparency, and collaboration are at the heart of all that we do. You can get started for free and even build self-managed full solutions at no cost. (Did you know that the free version of Elastic has been downloaded over 3.6 billion times!?) Elastic is an API-first solution that supports open standards and data transformation, which means we can scale with you and adapt to shifting strategies.

Elastic has also recently contributed Elastic Common Schema (ECS) to OpenTelemetry’s semantic conventions in order to help drive standardization for data definitions, ingest, and parsing across Observability and security. ECS is the foundation of Elastic Observability and Security solutions and is a proven and widely adopted schema that has evolved and grown over the years since its inception in 2019. 

Elastic is committed to preventing vendor lock-in through the use of proprietary agents and semantic data definitions from a specific vendor. Additionally, Elastic supports OTel natively. Elastic users can send OTel data directly from applications or through the OTel collector into Elastic APM, which processes both OTel SemConv and ECS. As OTel adds more logging and infrastructure metrics support, such as Kubernetes, Elastic will be able to ingest any of this data.

With this native OTel support, you can use native OTel agents without having to use Elastic or any other vendor agents. This also allows for the migration to Elastic easily.

See Elastic documentation to learn more about OTel integration.

Splunk’s approach centers on proprietary technology and can lead to vendor lock-in.

Take the next step by replacing your logs with Elastic. Then, set your sights on the future by focusing on the long-term benefits of a unified observability solution with end-to-end visibility, decreased mean time to resolution (MTTR), and lower total cost of ownership (TCO).