With the global cloud FinOps Market expected to grow from $832.2 million in 2023 to $2,750.5 million by 2028, with an average annual growth rate of 18.8%, companies offering data observability and FinOps have seen increasing prevalence in the market. One such player has been in the market for over a decade and leverages machine learning and automation to provide services to some of the largest cloud players, such as Databricks, snowflake, Big Query and others are Untangle the data.
AI is not new
Using AI-based tools and an insights engine, this US-based company, which also has an office in Bangalore, relies on in-house ML models and algorithms. “AI is not new to Unravel. We’ve used AI to automate data team tasks for the past 10 years after observing over 50 million data pipelines and queries. Today, AI is integrated into Unravel’s platform at every level,” said Kunal AgarwalCEO and Co-Founder of Unravel Data, in an exclusive interaction with AIM.
Unravel Data’s ML algorithms were developed in-house and have been trained on a wide variety of workloads for each specific platform to ensure maximum accuracy of insights and predictions. The company’s AI-powered Insights Engine uses a robust technology stack that begins with collecting data from a variety of sources, covering big data application performance, cloud spending and historical usage patterns.
If AI is at the heart of Unravel’s business operation, generative AI is not far behind. Talking about its implementation, Agarwal mentioned that Unravel has big plans and they will be shared with the market very soon.
Customer-centric solutions
The increasing adoption of data engineering teams guided by DataOps practices will lead to successful outcomes. By 2025, data teams supported by dataops tools and practices would be 10 times more productive than teams that don’t use Data Ops. Faced with the need to stand out and offer specialized solutions, Unravel also responded to this problem.
“AI is not just reactive; it uses predictive analytics, forecasting future cloud spending based on historical data and trends. This foresight allows companies to make proactive adjustments, thus avoiding budgetary pitfalls. This also means that our ML models are trained for each specific platform, across a wide variety of workloads to provide accurate insights,” said Agarwal.
Catering to specific needs, Unravel offers distinctive products for each of its large customers, including Databricks, AWS EMR and others. Unravel’s purpose-built AI provides real-time insights at the job, user, and workgroup level to help teams improve their cost allocation and workload efficiency. Additionally, a standout feature of Unravel’s Insights engine is its ability to act like a financial detective. It examines cloud spending patterns, identifying anomalies and inefficiencies in resource allocation. “It is an invaluable asset for organizations looking to streamline costs and improve operational efficiencies,” Agarwal said.
Agarwal believes that AI-based resource rightsizing recommendations are akin to having a personal trainer for one’s cloud resources. “Unravel Data ensures that your resources are neither under-utilized nor over-utilized, thereby optimizing costs with precision. AI also plays a crucial role in cost allocation, accurately assigning cloud costs to different business units or projects.
Data observability in 2024
With booming forecasts for cloud end-user spending expected to reach $600 billion in 2023 according to Gartnerforecasts will only increase, increasing the need for data observability and FinOps platform.
“In 2024 (and beyond), cloud data costs will be much higher because you will collect, store and process more data. Data observability to understand what is happening with applications/data pipelines will become table stakes. What businesses will really need are solutions that leverage data observability with FinOps and AI-driven recommendations that optimize the performance and costs of data workloads,” Agarwal said. However, the challenges of navigating generative AI in FinOps will continue.
“The full impact, both fiscal and environmental, will cause companies to pay more attention to their AI projects, for example: which models should they actually run, which projects require generative AI, which model can be reused/refined rather than being reused? starting from scratch and right-sizing jobs to ensure they do not waste resources or money,” said Agarwal, who believes these are some of the nuances that need to be taken into account.
Companies such as Dynatrace, Datadog, Microsoft System Center, among others, are some of the notable competitors of Unravel Data.
