Sync Computing, an MIT spinout exits stealth with $6.1 million in funding and a $1M contract from the Department of Defense
With virtually every organization moving their IT resources to the cloud, today companies are massively overspending on provisioning cloud resources for their big data jobs, a problem that even those who use Databricks run into. To solve this problem, one new startup has figured out how to avoid overspending on provisioning cloud resources for big data jobs.
Enter Sync Computing, a deep tech distributed cloud infrastructure startup that recently spun out of MIT. Sync Computing is harnessing the computational power of physics to find mathematically the best way to provision cloud infrastructure for data, machine learning, and scientific workloads.
Today, Sync Computing came out of stealth mode with $6.1 million in funding. The funding round was led by Moore Strategic Ventures and National Grid Partners, with participation from existing investor The Engine. Sync Computing will use the new cash infusion to advance its leadership in the modern data infrastructure space and support further development of its accelerated data infrastructure engine and solution lines.
Alongside active pilots with both public and private enterprise customers in SaaS, finance, and data sectors, the company recently was awarded a $1 million contract from the Department of Defense for large, distributed workload optimization.
Spun out of MIT Lincoln Laboratory by Jeff Chou and Suraj Bramhavar, Activate Fellows (Cohort 2020) supported by DARPA’s Microsystems Technology Office, and MIT Startup Exchange Accelerator veterans, Sync Computing is harnessing the computational power of physics to find mathematically the best way to provision cloud infrastructure for data, machine learning, and scientific workloads. Sync’s technology empowers organizations that run thousands of data pipelines daily to automatically optimize low-level compute resources to make running the cloud easier, faster, and cheaper.
The technology challenges addressed by Sync Computing are a fundamental part of the massively growing data infrastructure market that Gartner values at over $66B. Cloud costs are exploding – with annual enterprise spending on cloud infrastructure services estimated at $130B (Synergy Research), and over 36% of enterprises spend more than $12 million per year on public clouds, 61% of organizations plan to optimize existing use of cloud (cost savings), making it the top initiative for the fifth year in a row (Flexera). Solving for the problem of managing growing big data workloads against their optimal cloud compute is an urgent, formidable, and yet-to-be-solved challenge until now.
“Companies with data-intensive cloud workflows struggle to hire data engineers while their engineers spend unproductive time manually configuring and tuning rather than important work to move the needle on the business top line. We believe Sync automation can add thousands of hours of data engineering productivity every year,” said Reed Sturtevant, general partner at The Engine. “What Sync offers is transformative – an automatic configuration and cost-performance optimization solution for distributed cloud applications. Previously it was thought to be too mathematically complex and dynamic to solve this challenge, but Sync has figured it out.”
“With today’s constantly expanding use of large-scale cloud computing, we’ve seen companies who have only a dozen engineers responsible for managing up to 10,000 data pipelines per day – it’s physically impossible to optimize cloud infrastructure at such large scales – until now,” said Jeff Chou, co-founder and CEO of Sync Computing. “We’ve essentially converted large-scale cloud infrastructure into a math problem, and then solve it in seconds. We are also excited to do our part in reducing the wasteful use of cloud resources and their impact on the global carbon footprint. We are bullish on what 2022 will bring for us, for our customers, and for the cloud space.”
As part of its public launch, Sync is announcing two solutions – the Sync Autotuner for Apache Spark, and the Sync Orchestrator – a major step in large-scale cloud multi-tenant orchestration of complex data pipelines, inspired by Sync cofounders’ Ph.D. research on solving complex combinatorial optimization problems, which was published in Nature. The Sync Autotuner for Apache Spark eliminates provisioning friction for EMR and Databricks on AWS infrastructure while reducing runtimes and job costs dramatically.
The Sync Autotuner has already been field-tested and its performance validated by a number of marquee customers, leading to key partnerships. Duolingo, the world’s #1 language learning platform, serving more than 40 million monthly active users, cut daily data job costs on the cloud in half with the Sync Autotuner, with only a negligible increase in run time. “We run many big data jobs, so optimizing our cloud processing performance is critical to our success as a business,” said Kevin Wang, analytics engineer at Duolingo. “We were impressed by Sync Computing’s delivery and appreciated their ability to predict optimized results, even before running our jobs. We are confident they can continue to help us forecast performance and reduce costs for our Spark workloads.”
Founded by PhDs Jeff Chou (CEO) and Suraj Bramhavar (CTO), Sync Computing is the first company to harness the computational power of physics to find mathematically the best way to provision cloud infrastructure for data, machine learning, and scientific distributed-based workloads. Sync provides the only solution, which globally optimizes and automates application configurations, cloud infrastructure, and scheduling to achieve business goals of performance or cost.
With a growing team joining from MIT, UC Berkeley, Harvard, Stanford, IBM, Intel, and Cloudability, Sync Computing is working with leading enterprises to optimally tune big data processing across its optimal cloud compute.
The Engine is a Cambridge, MA-based venture capital firm. It was conceived and created by MIT in 2016 to address the unmet need for sustained support for startups with the potential to solve complex, existential challenges and make a material, positive impact on society. The Engine provides access to long-term capital, knowledge, connections, as well as the infrastructure these Tough Tech companies need to thrive.