Cloud costs continue to increase, and as a result, businesses are taking a step back to reconsider, and in many cases, are forced to implement a hybrid cloud strategy—that means going back to managing an on-premises server farm.
Relational compute is key to getting business insights from data. However, the cost of processing data needs to make business sense, especially as data continues to grow non-linearly. JitsuAI leverages advances in cloud computing, containers, open-source software, and cloud microservices, to provide 100% open, cloud-native query processing for 1000s of concurrent users while delivering enterprise grade SLAs at ~5x lower cloud costs.
Jitsu was founded by Vikram Joshi, a serial entrepreneur, and comprises a team of world-class database engineers. It is backed by prominent Silicon Valley angels and Monta Vista Capital.
Vikram Joshi's technical background spans multiple disciplines including databases, operating systems, parallel and distributed systems, storage, video streaming, and computer graphics. His company ioTurbine specialized in software to accelerate storage using SSDs (acquired by Fusion-io). Xcalar, a relational compute platform, is used by top-tier Wall St. banks and financial institutions. PixBlitz Studios developed video advertising technology for broadcast sports and entertainment. Vikram's work at Oracle included doubling database performance on 12 to 64 way SMPs, and laying the foundation, architecting, and shipping Exadata. Prior to that, Vikram worked on video on demand and game servers at Silicon Graphics. At Sun Microsystems, he worked on the Solaris virtual memory subsystem and Spring Microkernel (SunLabs). He holds an MS (Hons.) in Physics and a BE (Hons.) in Engineering from the Birla Institute of Technology and Science, Pilani, India.
Vikram’s social good causes include eliminating sex trafficking & child sex abuse. He loves rock climbing, mountain biking, trail running, and riding dirt bikes.
JitsuAI delivers the power of AI/ML transparently for high quality business decision making. Querying large amounts of data is expensive and slow.