Customer Stories / Software & Internet

2022
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Lower Latency and Costs Using AWS Graviton2–Based Instances with Sprinklr

Learn how Sprinklr in the software and internet industry reduced latency for queries by 36 percent and saw a price performance benefit of 40 percent using Amazon EC2 Im4gn and Is4gen Instances powered by AWS Graviton2 processors.

36% reduction

in latency for queries

40%

price performance benefit

2 weeks

to migrate to new instances

More than one billion

queries processed daily

Reduced

carbon footprint

Overview

For Sprinklr, the world’s only unified customer experience management solution, compute is business critical. Analyzing petabytes of data from more than 30 digital channels, like social media and email, to drive near-real-time insights into its customers’ end-user experience is the cornerstone of the company’s purpose. So it’s always searching for innovative instances—which is why Sprinklr has used Amazon Web Services (AWS) since it was founded in 2009.

Sprinklr has used Amazon Elastic Compute Cloud (Amazon EC2), which offers a broad and deep compute solution, with over 500 instances and choice of the latest processor, storage, networking, operating system, and purchase model to help match the needs of specific workloads. When AWS released new instances on its AWS Graviton processor, which is designed by AWS to deliver outstanding price performance for cloud workloads running on Amazon EC2, Sprinklr became an early adopter, reducing latency, costs, and its carbon footprint to provide an even better customer experience.

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Opportunity | Using AWS Graviton2–Based Instances to Improve Performance

Sprinklr’s artificial intelligence creates insight-driven strategies that help companies improve their customer experience. The company ingests and analyzes end users’ data from digital channels to detect trends and problems before they materialize so Sprinklr’s customers can proactively address them. For example, Sprinklr Modern Care analyzes billions of end users’ conversations across various channels, identifies intent and sentiment, and routes end users to the right support, all in near real time.

Sprinklr serves 70 percent of Fortune 200 companies, such as Cisco, Procter & Gamble, and Disney, and has service-level agreements for throughput and performance speed. Yet processing a massive amount of data in near real time is challenging: the search workload is disk intensive and requires many input/output operations to transfer data between sources and servers, not to mention immense memory and processing power that’s expensive. Without enough compute, Sprinklr experienced bottlenecks at the level of its disks and central processing units. “Our main goal is to have better performance in terms of latency and the user experience while optimizing cost,” says Nitin Goyal, vice president of engineering at Sprinklr.

The company needed new instances to support its search infrastructure, which fuels search, aggregation, storage, and analytics capabilities. Sprinklr uses several storage engines, and Elasticsearch is the biggest one, comprising 40–50 percent of Sprinklr’s cloud spend. Knowing that Sprinklr is always searching for new compute, AWS reached out in early 2022 to suggest that the company try Amazon EC2 Im4gn and Is4gen Instances, next-generation, storage-optimized instances designed for running applications that require high throughput and low-latency access to large amounts of data on local solid state drive storage. Both instance types run on AWS Graviton2 processors.

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On AWS Graviton2–based instances, we can build more features that before were either expensive or not feasible.”

Nitin Goyal
Vice President of Engineering, Sprinklr

Solution | Reducing Latency by 36% Using AWS Graviton2–Based Instances

Moving to AWS Graviton2–based instances took just 2 weeks. Sprinklr developed an internal testing tool on AWS to compare instance types and configurations; using it, the company ran key complex queries to test the performance of Amazon EC2 Im4gn and Is4gen Instances. Because Sprinklr was an early adopter of the instances, it ran into capacity limitations as it tried to migrate its workloads to those instances. However, AWS support delivered the capacity that the company needed a few days after Sprinklr raised the issue. “Our experience with AWS support in terms of escalation and issue resolutions, especially when we’re evaluating new instances, has been outstanding,” says Jamal Mazhar, vice president of infrastructure and DevOps at Sprinklr.

For Elasticsearch, Sprinklr is using only Amazon EC2 Im4gn Instances, which offer excellent price performance for storage-intensive workloads. Query performance has improved by 36 percent, resulting in a 40 percent price performance benefit. Sprinklr also uses Amazon Elastic Kubernetes Service (Amazon EKS), a managed Kubernetes service to run Kubernetes in the AWS Cloud and on-premises data centers. Amazon EKS pairs with AWS Graviton2 processors to power Arm-based Amazon EC2 Instances, delivering increased performance and cost savings.

Sprinklr, which processes more than one billion queries daily, reduced latency for queries by 36 percent, and its query time went from 700 to 450 milliseconds on read-heavy clusters—all by migrating to AWS Graviton2–based instances. The company has better disk performance and higher throughput on central processing units, which has reduced bottlenecks during input/output operations and accelerated recoveries. Now Sprinklr customers can perform analytics faster, improving their experience and increasing their usage of the solution. “On AWS Graviton2–based instances, we can build more features that before were either expensive or not feasible,” says Goyal. For example, Sprinklr now provides analytics on high-cardinality fields, which facilitates deeper analytics for customers.

Because Sprinklr sees a higher throughput on the same number of instances, it has a lower carbon footprint, appealing to environmentally conscious customers. The company’s report shows that its carbon footprint was reduced by 56 metric tons between January and June 2022. “AWS Graviton2–based instances run at less clock speed, thus using less heating and electricity,” explains Mazhar.

Outcome | Benefiting from the Innovative Compute of AWS Graviton2–Based Instances

Switching to AWS Graviton2–based instances was quick and resulted in higher performance, reduced costs, improved customer experience, and a better carbon footprint for Sprinklr. The company plans to run other pieces of its analytics pipeline, including persistent storage NoSQL databases and data streaming services, on Amazon EC2 Im4gn Instances. It will continue to seek new compute from AWS that will improve the customer experience. Goyal says, “We are constantly innovating for customer needs and for the company.”

About Sprinklr

Founded in 2009, Sprinklr is the only unified customer experience management solution. Its artificial intelligence creates insight-driven strategies that help companies worldwide improve their customer experience across more than 30 digital channels.

AWS Services Used

Amazon EC2

Amazon Elastic Compute Cloud (Amazon EC2) offers the broadest and deepest compute platform, with over 500 instances and choice of the latest processor, storage, networking, operating system, and purchase model to help you best match the needs of your workload.

Learn more »

AWS Graviton processor

AWS Graviton processors are designed by AWS to deliver the best price performance for your cloud workloads running in Amazon EC2..

Learn more »

Amazon EC2 Im4gn and Is4gen instances

Amazon EC2 Im4gn and Is4gen instances are next-generation, storage-optimized instances designed for running applications that require high throughput and low-latency access to large amounts of data on local SSD storage such as SQL databases, NoSQL databases, search engines, analytics, streaming, and large distributed file systems.

Learn more »

Amazon EKS

In the cloud, Amazon EKS automatically manages the availability and scalability of the Kubernetes control plane nodes responsible for scheduling containers, managing application availability, storing cluster data, and other key tasks. 

Learn more »

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