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Reduce index and resource requirements in ELK deployment

Get control over ballooning log management costs, tiered storage headaches with the data fabric

"FREE" with a very high TCO

Companies turn to ELK with the assumption that they can keep costs and complexity low. While the ELK stack has several useful capabilities, the cost and complexity of managing and using ELK is among the highest among observability platforms.

FREE ELK is like the tip of an iceberg. The TCO of ELK reveals itself as the use of the platform continues and you look below the surface.

The key to running ELK is to keep data volumes low. If you let the index sizes grow and disk storage is not enough, you need lots of memory and clustering and you really need a Phd. to manage that. We try to keep ELK deployments to as small as possible and don't turn on all data. We like Kibana and how quickly we can build dashboards so we continue to use it. It's not ideal but we are worried about it constantly.

Shaking off vendor lock-in

The knee-jerk reaction of controlling costs typically leads the engineering teams down the path of looking for alternatives. The search for alternatives usually ends up with the following conclusions: they are locked into using several of ELK’s feature sets and introducing point-solutions to alleviate the logging cost problem introduces fragmentation and creates overlaps.

With’s Data fabric, vendor lock-in is no longer a concern. Just like ELK, many other platforms can be enabled on demand to comply with your business needs to consume data. Want to gather all data but send security data to Splunk while routine developer data to ELK, no problem! 1-Click data route management make this a breeze.

Drastically reduces costs and improve agility’s data fabric provide all the knobs and meters to engineering teams to stream all relevant data streams to ELK. A critical aspect of logging is the fact that 95% of data streams tends to be noise in a given context.

Teams can filter data in real-time to optimize the data volume being sent to ELK. This will keep ELK disk-usage and index sizes in control. Powerful extraction and reduction rules allow dynamic management of data attributes to augment or reduce unwanted data getting indexed in ELK.

The data fabric does all of this without ever loosing any of you data. Our InstaStore always keeps a master copy of 100% of your data streams in any object store of your choice and keeps it fully indexed for fast retrieval.

This provides the immediate and dual cost benefits of reducing ingest and indexing costs of ELK by 70 to 95 per cent and also provides a limitless and active retention layer for pennies eliminating the need to use ELK’s inefficient and non-agile tiered storage architecture.

Application and Infrastructure optimizers

Using a combination of AI/ML and rules-based capabilities, understands outliers, patterns, and anomalies and streams the noiseless/useful data to ELK while also providing other key capabilities like data enrichment for better analytics using the ELK engine. 100% of all data streams are parallelly indexed in Apica/AI’s industry-first data fabric that uses any low-cost object storage.

As an example, if you are collecting logs from a Kubernetes cluster, using a couple of simple clicks in the Apica rule pack for K8s, teams can save 70% of license spend and index size on ELK instantly!

Manage long term retention and compliance with ease

It is essential for enterprises to have a system that can ingest, store and retrieve data at scale and speed. Datadog tiered storage layer means older data can only be retrieved as a slow archive. Teams need to plan for data rehydration, reindexing while face additional costs 10X cost for this indexed data.’s unique storageless architecture built on any object storage allows the enterprise to store copious amounts of data with zero impact on performance and reliability. Data retrieval is instantaneous.

By moving your long term retention storage to vs Datadog, you can free your data and manage costs better. You also get a purpose built automation engine for retrieving data on demand into Datadog. Save time and money on indexed long-term retention with

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