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NEOS

Oracle Customer Success

NEOS builds new cloud native CloudVane app on Oracle Cloud

Summary

Managed services provider creates SaaS application for cost visibility and automation using Oracle Cloud Infrastructure and Autonomous Database.

Business challenges

NEOS, a managed services provider and loyal member of the Oracle Partner Network, has successfully delivered more than 200 projects for clients across finance, telecommunications, utilities, and the public sector during the past 20 years. The company’s 80 certified consultants combine knowledge of business requirements with technical expertise in big data and analytics, DevOps, security, Oracle and Microsoft Azure cloud platforms, and on-premises hardware systems.

To add more business value with data-driven solutions and machine learning predictive technologies for its clients, NEOS decided to develop a cloud native application called CloudVane from scratch. CloudVane offers customers better visibility and control over optimizing costs and managing multicloud IT environments.

To develop its new SaaS app, NEOS required a platform that could collect and process multiple types of data in very large amounts, from traditional structured transactions to unstructured data in documents, JSON files, and spatial graphs, in a single storage bucket, for simpler and faster deployment. Secondly, administering a new DevOps database needed to be easier to provision, manage, and scale than an on-premises one.

We decided to design and architect our new native SaaS app, CloudVane, on Oracle Cloud Infrastructure with Oracle Autonomous Transaction Processing at its core from the get-go for fast time to market, low administration and costs, and high performance. Now, we can add more value for our customers with resource automation, as well as data and machine learning predictions that monitor and optimize their costs.

Davorin Capan

CEO, NEOS

Why NEOS Chose Oracle

NEOS decided to build a new cloud native SaaS application on Oracle Cloud Infrastructure (OCI) and Oracle Autonomous Transaction Processing, which is based on Oracle Exadata—something NEOS was already familiar with and knew what to expect in availability and performance.

The company chose Oracle for a combination of not only Exadata’s high performance, but the cloud’s faster time to value, lower maintenance, and autoscaling. 

Results

Using Oracle Autonomous Transaction Processing database service and Oracle Container Engine for Kubernetes on Oracle Cloud Infrastructure, NEOS found faster time from development to deployment. Customer deployment for CloudVane happens with a single click. Oracle’s Autonomous Database is so easy to use that it requires no experience to start. The company was able to provision and configure a new environment in minutes, compared to hours or even days before on-premises, simply by entering new customer data in the administrative console.

Zero maintenance in the database eliminated human labor for provisioning, monitoring, backup, tuning, patching, repairing, and troubleshooting. NEOS was also able to allocate resources more strategically toward development. Database administrator work hours have dropped from 80 hours per month to just 4 hours per month.

NEOS saw a 76% cost savings for DevTest and 30% cost savings for production environments from a combination of optimized storage and the per-second autoscaling billing for flexible cost control in Oracle Autonomous Database. Autoscaling computing power also allows the company to focus on customer growth. The company can onboard any project of any size with significant reduction in the need for DBA involvement or worry about database performance.

Oracle Machine Learning, an intuitive library of machine learning algorithms built into the autonomous database, allows the CloudVane app to automate advanced analysis of the vast amount of multicloud performance data. It can make precise projections and recommendations 40% to 60% faster without the cost of a dedicated AI service. Now, NEOS has a self-managed AI platform that can execute the predictive models faster and more accurately In-database Machine Learning, providing better insights and advice to customers.

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Published: April 13, 2021