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Cloud computing has reshaped the digital landscape, offering unparalleled scalability, flexibility, and cost-effectiveness to businesses worldwide. Imagine entrusting your most valuable possessions to a high-tech, automated storage facility—accessible anytime, anywhere, but also potentially vulnerable if not adequately protected. This analogy perfectly encapsulates cloud computing: convenient, yet requiring robust security measures to guard against threats.

Confluent has been instrumental in enabling customers from various industries to develop real-time stream processing solutions using Apache Kafka. While many of these use cases demand low-latency and real-time processing, stream processing is also increasingly being utilized for ingesting logging and telemetry data. This type of data typically features a high ingest rate, but allows for a higher tolerance for end-to-end processing time.

The performance of your AI applications depends on your underlying infrastructure. Whether leveraging high-performance GPUs, accelerators, or CPUs, AI workloads require high-performance hardware. With a range of different GPUs and accelerators available, choosing the best one for your specific workload is critical. On top of selecting the best GPU for your workload's needs, efficiently running AI workloads in production and at scale is a challenge.

Discover how Paylocity, Vida Health, Nando’s, and Trinny London use Fivetran and Google Cloud to drive efficiency and cut costs across their global operations.

Orbit Codes is solving how blockchain data is indexed and analyzed within the COSMOS ecosystem. It extracts raw blockchain data, transforms it into a structured PostgreSQL database, and provides easy access through a GraphQL interface. For deeper insights, data is replicated to ClickHouse, enabling advanced analytics on transactions, fees, and trends. Designed for blockchains, not just users, Orbit Codes offers white-label solutions and custom branding to integrate seamlessly into existing ecosystems.

Enterprises working in Microsoft Azure need to mask data, and they need to mask it quickly.Yet, it can be difficult to find Azure data masking methods that are both swift and secure. The wrong approach can lead to bottlenecks, compromised analytics quality, and significant regulatory risks.

AI models are not just about LLMs and generating text. Multimodal vision models—which understand and generate images, videos, and even audio alongside text—are enabling new AI applications. At their core, multimodal vision models combine: There are several different types of multimodal vision models: vision-language models (VLMs) that generate text based on images, vision-reasoning models that answer complex questions based on images, and more.

Today, we are thrilled to announce our partnership with the Vultr Cloud Alliance. As you may already know, we provide a serverless cloud for developers and businesses to seamlessly deploy AI apps, inference endpoints, and APIs globally across GPUs, CPUs, and accelerators. Vultr provides high-performance cloud infrastructure and offers composable infrastructure that lets you deploy any stack, anywhere, in seconds.

Managing cloud costs in 2025 is challenging. The costs have spiraled out of control, and it’s difficult to get the balance right. So, how exactly did we get here? In this blog, I will share my insights and recommendations based on my experience working with enterprises successfully managing cloud costs.