Reducing Splunk costs and getting out of “ingest hell”

Reducing Splunk costs and getting out of “ingest hell”

Observability has become a fundamental part of IT operations strategy. As a result, organizations deploy solutions like Splunk to gain visibility into the health of their infrastructure, applications, services, edge devices, and operations. A core aspect of observability is building machine data pipelines from all systems that generate data to the Splunk system so that operations teams can gain meaningful insights. However, connecting all your data sources to Splunk creates a huge volume of data streams that eventually drive up the TCO of running Splunk.

The costs of running Splunk rise rapidly and steadily due to the proliferation of data. These rising costs can be broken down into the cost of additional licenses, the cost of index storage, and the cost of retention storage. 

Keeping Splunk costs in check using LogFlow

Fortunately, you can keep these costs in check using LOGIQ.AI’s LogFlow. LogFlow addresses this using a unique observability pipeline model that enables the

  • filtering of data streams and volumes
  • routing and channeling of data to the right targets
  • shaping and transforming data in transit
  • optimal retention of data
  • real-time data search and recovery, and
  • virtualization and replay of data

With all of these capabilities combined, LogFlow can help reduce the TCO of Splunk by as much as 95%. 

Getting out of “ingest hell”

Organizations that have deployed Splunk have helplessly fallen into “ingest hell”. “Ingest hell” is a situation where a customer has to pay high licensing and infrastructure costs as a direct result of their ingest volumes. The more they ingest, the more they pay. And it is no secret that Splunk charges a very high premium for their licenses.

However, not all observability and machine data generated across distributed environments are entirely valuable. Machine data is often filled with noise – event logs can contain duplicate and NULL values, can be overly wordy and could be generated in an inefficient format. Several organizations using Splunk might witness that 95% of the data it ingested is all noise. Thankfully, Splunk does not need to ingest data in its crude form to generate all its wonderful insights. While Splunk does a splendid job in uncovering insights, it gets a free pass in charging its customers the same premium cost for ingesting data that is noise/junk as data that is valuable. Even with its workload-based pricing model, the effective dollar cost per GB of valuable data that needs analysis is astronomically high.

LogFlow free organizations from this “ingest hell” without the need to replace Splunk. By acting as a filter and shaper that sits in the observability pipeline of an organization, LogFlow dramatically reduces the cost of licensing and running Splunk by intelligently eliminating all the redundant data streams and cutting the noise within each event log. Through this process, LogFlow reduces ingest volumes by up to 95%, directly reducing the costs associated with Splunk licenses and infrastructure. 

Routing data to where it’s needed

The market’s current observability pipeline control solutions let you route your data to a data lake for low-cost, long-term storage. Data lakes are known to be very slow. However, LogFlow’s unique engineering innovation creates, what we like to call, data dams that can be queried, analyzed, and mined in real-time. Besides reducing ingest traffic and serving to reduce costs, LogFlow enables organizations to route 100% of all the original observability data to a real-time data lake built on any S3-compatible object storage for maintaining compliance. Would you like to know the cost of this real-time data lake? It won’t cost you more than the cost of object storage – a penny or two per GB, depending on your choice of object storage.

Observability data is only helpful if available to the teams and target systems that need it in real-time and on-demand. LogFlow, along with the InstaStore data dam, allows teams to route observability and machine data from any source to any target. With LogFlow, teams can use any combination of data volume and streams between source and target systems like Splunk, S3, Snowflake, Databricks, QRadar, home-grown data lakes, DBs, Elastic, Azure, GCP, etc.

Furthermore, LogFlow completely frees up end-users from the burden of modifying configurations at either the source or the target. LogFlow is the magic wand that allows organizations to magically enhance the value of their existing observability, security, and compliance investments. With LogFlow, the end-user gains total control over their observability system, pipelines, and data. Not Splunk.

Datastream equality

Besides cost reduction, effective routing, and efficient storage, organizations can unlock faster remediation times, real-time compliance, and enhanced security. The worst thing you could do for security and compliance is to analyze only a subset of data, systems, or processes. Several organizations prioritize specific data streams over others or even turn off data ingestion for extensive periods to keep their Splunk costs in check. However, doing so introduces far-reaching business, compliance, and security risks.

With LogFlow, you’ll never need to block data streams or drop data ingestion. Regardless of how fast your data volumes grow, LogFlow can ingest, filter, enhance, and store logs at an infinite scale without influencing Splunk infrastructure costs. LogFlow performs at an endless scale with any type of observability data without the need to “understand” a customer’s environment. LogFlow’s AL and ML capabilities make it highly intuitive and easy to use, delivering value in minutes instead of days or weeks. With LogFlow, Splunk admins no longer need to invest time or resources in managing SmartStore and running through its various configuration permutations. Instead, LogFlow lets you treat all of your data streams with equal priority and log, store, and process all the data you need without worrying about costs or infrastructure. 

Infinite storage for Splunk

LogFlow’s storage layer, InstaStore, uses any object store or S3-compatible object store as its primary storage layer. This capability of using object stores as primary storage means that LogFlow can handle any growth in data volumes (even theoretically infinite) using simple API calls. LogFlow can retain any volume of data, whether TBs or PBs of data, precisely the same way. This capability allows you to not only use LogFlow to manage, refine, and unify your data streams but also as a storage sidecar for Splunk. With its real-time data forwarding and replay capabilities, you can forward any volume of data to Splunk in real-time while unlocking infinite retention for that data within InstaStore.

LogFlow is the first real-time platform to bring together benefits of object storage-like scalability, one-hop lookup, faster retrieval, ease of use, identity management, lifecycle policies, data archival, and other capabilities.

For companies processing TBs of data per day on Splunk, LogFlow can help save millions of dollars per year purely on licensing, storage, and infrastructure terms. 

Related articles

Observability Best Practices

The term “observability” has gone viral in the cloud industry, at least among IT professionals.

Gathering logs from Google Autopilot

In any modern containerized workload setting, container orchestration is imperative. Purportedly, the majority of contemporary

The LOGIQ blog

Let’s keep this a friendly and inclusive space: A few ground rules: be respectful, stay on topic, and no spam, please.

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.

More insights. More affordable.
Less hassle.