In today's fast-paced enterprise environment, companies must adopt efficient tools to remain competitive, scale effectively, and deliver exceptional value to clients. A critical factor in achieving this is streamlining workflows to reduce inefficiencies and empower teams to focus on high-value tasks. StartingPoint, a comprehensive workflow management platform, offers a simple and effective solution to simplify enterprise workflows, ensuring that businesses are equipped to thrive.

If you are reading this, you likely understand how Machine Learning (ML) and Artificial Intelligence (AI) can improve your organization, from operational efficiency to boosting your bottom line. Yet, setting up and running a robust AI infrastructure, including a massive dataset for training and specialized tools, can be expensive. Amazon Bedrock wants to simplify this challenge, enabling you to build generative AI applications faster and easier. But is Amazon Bedrock pricing worth it?

Managing OpenTelemetry (OTel) collectors across diverse, cloud-native environments is key to streamlining monitoring and gathering valuable insights. But, managing them effectively, especially across multiple servers, has been a manual and time-consuming process. That changes today. Sumo Logic’s Otel Remote Management is designed to simplify OpenTelemetry Collector management, all from a single unified user interface.

Session Replay for Mobile is now generally available. I could bombard you with hyperbolic statements about why Session Replay is worth using, but instead, A…I… wrote you a haiku: Screen freeze, devs all sigh. Replay uncovers the crime: Forgot.addListener.

As someone deeply embedded in the cloud-native space, I’ve witnessed how the rapid evolution of cloud computing has unlocked remarkable opportunities—but also introduced significant challenges. Recently, I revisited some research we conducted back in 2022, which revealed a critical trend: despite the hundreds of services offered by the major cloud providers (the Big Three hyperscalers), most businesses use fewer than 10 services from their cloud provider.

Service-level objectives (SLOs) can be a great way to ensure you’re hitting your goals, but many software teams struggle to set realistic targets when they first set up the service-level indicators (SLIs) that underpin those efforts. Sometimes management has a decree that all services will operate with “three 9s” of availability; other times engineers pick a number out of thin air.

As your applications scale, so too does the flood of logs, metrics, profiles, and traces—along with the costs to store and manage them. Collecting everything might feel like the safest bet, but it often leaves you buried in noise and struggling to find the signals that matter, all while costs spiral out of control.

Artificial intelligence (AI) and machine learning operations (MLOps) have become crucial across a wide swath of industries, with the two technologies working in tandem to provide value. AI enables data-driven insights and automation, while MLOps ensures efficient management of AI models throughout their lifecycle. With AI’s growing complexity and scale, organizations need robust infrastructure to manage intensive computational tasks, giving rise to platforms like Kubernetes.