If you are maintaining an ELK (Elasticsearch, Logstash, Kibana) stack on-premises or in the cloud, that solution might not be scaling with your needs. Engineering teams that grow out of their rudimentary log analysis process sometimes choose to build an ELK-based solution for their observability use cases. But with ELK, you may be trading off one problem for another. Our customers who swap their ELK stack for Scalyr do so for three principal reasons. First, they don’t get the performance they need with their ELK-based log management, especially when it comes to log ingestion and alerting. Second, whether they query in Elasticsearch or Kibana, the query language is not straightforward. And finally, it’s not an easily shareable solution, because as more users access and query the system, overall system performance suffers.

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The ELK stack comprises Elasticsearch, Logstash, and Kibana, three open-source projects for search and analytics, a data ingestion pipeline, and a visualization tool. Engineering teams like yours build ELK-based solutions for their observability use cases, especially when their software projects start to generate large amounts of data and performance requirements make rudimentary log access and review methods untenable.

As your data set grows and your team needs to move even faster than before, you may find yourself growing out of your ELK stack. Our customers for whom Scalyr is an ELK alternative switch primarily because they can’t get the performance they need without excessive investment in computing power and IT resources. Even though most engineers identify alerting speed as one of their top log management requirements, it is their major pain point in ELK, stemming from slow data ingestion. They describe the problem as needing to make a tradeoff between ingesting all of the data needed to populate the alert versus sending out the alert in a timely manner. Either they get a correct alert that’s too late, or they get it on time but it’s inaccurate, and therefore unusable!

Use Cases

Send real-time alerts

  • Ingest data and send alerts immediately
  • Generate alerts based on your queries
  • Integrate alerting with your third-party tool

Query simply

  • Search free text and wildcards
  • Point-and-click your way to the answer
  • Enable your users to self-serve

Share with the whole team

  • Share without extra cost
  • More users won’t slow you down
  • We scale as you scale

Second, whether you’re querying in the Elasticsearch or Kibana part of the solution, the query language is not always straightforward. Our customers for whom Scalyr is an ELK alternative say that the query language is a big obstacle to widespread adoption among their teams. Not everybody knows the query language, so what happens is that the few people who do know how to query efficiently become overloaded with requests from the rest of the team. That’s fine if the company can afford to have some of its highly-qualified engineers become ELK admins, but most find engineers in short supply and want to deploy that talent to activities that are core to the business. Moreover, given engineers’ parallel development work style, the far better choice is to encourage adoption across the whole team and have users self-serve.

Why Scalyr


Go fast. Blazing-fast. Ingest logs and alert on them in real time. Perform split-second searches and visualizations, across your entire environment.

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Skip the learning curve. Point and click to search, pivot or visualize your data.  No query language expertise required.

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Using our time-series database and massive compute capacity, Scalyr will easily scale with your systems.  You also won’t break the bank as you grow with us.

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“When we moved to the cloud and started using Kubernetes, we tripled our log data. Our ELK stack fell over. Fortunately, Scalyr scales beautifully with our needs.”

A customer who chose Scalyr as an ELK alternative

Finally, related to team adoption, you need to be able to share access to your log management solution with your team without paying a performance penalty. Our customers who switch from ELK describe a performance degradation as they scale data and add users to the system. The stack grows more difficult and expensive to maintain, and searches, pivots, and visualizations execute more slowly.

Unlike ELK, Scalyr scales with you. Both our data ingestion and queries are blazing fast, even across very large data sets. Our unique architecture, which combines a purpose-built, streamlined, no-keyword index database and massively-parallel cloud cluster, removes the bottlenecks in your alerting and troubleshooting processes. Our simple and straightforward query language, which you can actually invoke by pointing and clicking directly in the log lines on the screen, makes Scalyr easy to use for anyone. And finally, because we don’t charge by the user or slow down as you add users, your whole team can adopt Scalyr.

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