The current software delivery cycles are becoming increasingly complex. Organizations are being pressured to go to market quickly, but with higher quality and with zero downtime. The existing DevOps has already brought automation and collaboration, though even these approaches are not perfect. They need to address unpredictable infrastructure failures, rapid code debugging, and efficient scalability of CI/CD pipelines. Such bottlenecks may delay delivery and have an impact on the business performance.
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When GitHub revealed that developers using its AI-powered Copilot completed coding tasks 55% faster, the implications became clear. This isn’t just a productivity boost—it signals a fundamental shift in how development workflows and teams operate. And Copilot is only one example. Tools like Claude’s Artifacts and Cursor’s intelligent code completion are also reshaping how software gets built.
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Misplaced data in a multi-cloud or hybrid IT setup can significantly increase your cloud costs, whether due to increased data egress fees, over-provisioned resources, or other latency-induced issues. Keeping track of this data can become challenging, especially since the total data volume generated and consumed globally has exceeded 149 zettabytes as of 2024.
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We’ve moved beyond using AI as a tool. Today, most organizations aren’t just adopting AI—they’re delegating real work to it, shifting from proof-of-concept experiments to real-world production deployments.

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Introduction

Gone are the days of proprietary software and technologies. As AI becomes more democratized, open-source models are gaining momentum. You must have heard of DeepSeek—a Chinese AGI (artificial general intelligence) startup that has made it to the headlines in the last few weeks. It’s everywhere, from social media to developer community forums.

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