Data is the most crucial asset of any organisation, it is the lifeblood of modern business both domestically and internationally. In today’s world, information is captured and manipulated to make it more coherent, training machine learning models and to support real time analysis. To meet the ever-increasing needs for speed, scalability, and reliability, platforms have been developed that help organisations handle big data. These tools have enabled enterprises to leverage cloud computing to the fullest extent, but this has not come without its drawbacks.
As data processing workloads grow, so do cloud bills. Businesses are now presented a paradox: the tools that enable growth and efficiency also create a new challenge – spiralling cloud costs. Scaling data infrastructure on cloud platforms has become both a necessity and a burden, as enterprises watch their budgets get consumed by rising compute expenses. To complicate matters, this is often considered the “price of progress,” a trade-off accepted by default. But what if there was a smarter way to process data—one that delivered the same speed, reliability, and scalability while dramatically cutting costs?
The Success of Databricks and Cloudera: A Story of Growth Without Cost Innovation
Over the past decade, innovations in data processing frameworks have enabled businesses to thrive. Apache Spark emerged as the preferred framework for handling the ever-increasing volume and variety of data. Spark had the ability to scale data processing across multiple nodes in a cluster. However, managing spark for performance and reliability often becomes a significant burden for a processing team. Leaving a void in the market for a more polished axe.
These platforms harness the power of cloud computing to provide seamless scalability, enabling organizations to handle even the most demanding workloads. The platforms had a remarkable impact across the board.
These tools quickly became industry standards, paving the way for the new age for data-driven solutions. Behind this success lies an inconvenient truth: while these platforms deliver exceptional performance, their pricing models are not designed with cost optimization in mind.
The total cost of operating these platforms include the license fees for the platform in addition to the cloud costs associated with running the workload. The license costs for these platforms, while being usage based, is linked to the consumption of cloud resources, i.e. number of cores of compute used and hours for running the compute.
Higher cloud consumption equals higher revenue: Data platforms from Independent software vendors such as Databricks and Cloudera, on top of other cloud service providers, do not have the incentive to optimize compute usage because it directly benefits their bottom line. In other words, these tools are built to process more data, not necessarily to process it efficiently.
This inherent misalignment—between enterprises seeking cost reductions and platforms profiting from increased usage—has left businesses trapped. Cloud costs continue to rise exponentially as workloads grow, with little incentive for incumbent platforms to offer cost-saving innovations. Compounding this issue is the lack of meaningful innovation in data processing over the past decade. While incremental improvements have been made, there has been no significant breakthrough to challenge the status quo. Enterprises have had no real choice but to accept rising costs as the unavoidable trade-off for performance and scale. Enter Yeedu-
Yeedu: Reducing Data Processing Costs Without Compromise
Yeedu disrupts the status quo by offering a smarter, cost-efficient alternative to traditional data processing platforms. Furthermore unlike other innovative products across industries, It breaks through the traditional limitations imposed by the iron triangle of speed, cost, and reliability—proving that businesses do not need to sacrifice even a single element to achieve their data processing goals.
At the core of Yeedu’s value proposition is its ability to deliver measurable and immediate cost savings. Enterprises can achieve 50-60% reductions in cloud spend while maintaining the speed and reliability required for their business-critical workloads. Yeedu achieves this by addressing key inefficiencies in the way data processing jobs consume compute resources:
Through optimizations in cloud spending, Yeedu allows the opportunity for capital that would have been spent on cloud to be spent in other essential areas such as investments in other products, potential expansions and even year-end bonuses. Rather than having to worry about how to reduce costs for specific workloads, data teams can concentrate on creating tangible benefits for the entire business.
Conclusion: A Smarter Future for Data Processing
The data processing industry has thrived on performance and scale, but it has left cost efficiency behind. Enterprises deserve a solution that balances performance with affordability—one that doesn’t force them to choose between growth and fiscal responsibility. Yeedu is that solution.
By delivering unparalleled cost savings without compromising on performance, Yeedu empowers businesses to take control of their cloud spend. It challenges the status quo, offering a fresh alternative in an industry long overdue for disruption. Companies need not accept rising cloud costs as the price of doing business. With Yeedu, they can cut cloud costs, not corners.
In a world where data drives success, Yeedu ensures that success remains sustainable, scalable, and cost-efficient—today, tomorrow, and beyond.
