CloudTalk

Category: AI in Cloud

  • AI at the Edge: Akamai’s India Inference Cloud & the Shifting Power from Central Compute

    Akamai’s India Move: What’s Changing

    Inference at the edge, rather than training in a central hub
    The idea is to reduce response times, save bandwidth, and offload heavy requests from the core cloud.

    Hardware integration
    Akamai intends to deploy NVIDIA’s newer Blackwell chips to power the inference cloud by end of December 2025.

    Strategic growth in a high-demand market
    India has been buzzing as a major AI growth region — local infrastructure for inference means better access, lower cost, and potential for new local AI apps.

  • How OpenAI’s Custom AI Chips & the Push for Efficiency Are Reshaping the AI Race

    Introduction

    The AI boom isn’t just about bigger models anymore. Behind the scenes, the war for efficiency, proprietary hardware, and smarter architectures is heating up. In a recent move that could shift the AI landscape, OpenAI partnered with Broadcom to design its own AI processors — just one example of the deeper transformation underway.

    OpenAI + Broadcom: Building In-House Chips

    OpenAI has struck a deal with Broadcom to build custom chips tailored for AI workloads, with deployment expected in 2026–2029.

    The reasoning? General-purpose GPUs (like Nvidia’s) are great, but custom silicon can be optimized for inference, memory, interconnects — giving speed, power, and cost advantages.

    Still, analysts see challenges ahead: the cost, R&D complexity, and keeping up with rapid model evolution.

  • Cloud Support: What You Need to Know

    Cloud Support: What You Need to Know

    Cloud support refers to the services, tools, and resources provided to help businesses manage, troubleshoot, and optimize their cloud infrastructure. As organizations increasingly rely on cloud platforms like AWS, Microsoft Azure, and Google Cloud, having strong cloud support becomes essential to ensure reliability, performance, and security.

    Cloud support typically includes technical assistance, documentation, real-time monitoring, incident management, and account management. Depending on the provider and support plan, assistance may be available 24/7 via chat, email, or phone. Support tiers vary—from basic plans offering access to community forums and knowledge bases, to premium enterprise-level support with dedicated account managers and rapid response times.

    Common areas where cloud support is critical include deployment issues, service outages, billing queries, security alerts, and performance tuning. Cloud support teams also assist with best practices, architecture reviews, and proactive system monitoring to prevent issues before they impact operations.

    Choosing the right cloud support plan depends on your organization’s size, technical expertise, and business-critical needs. For startups or small teams, a basic plan may be sufficient. However, enterprises running mission-critical workloads typically require comprehensive, fast-response support to minimize downtime and ensure compliance.

    In short, cloud support is a safety net that enables businesses to focus on growth and innovation, while cloud experts handle the complexities of managing infrastructure. It’s a crucial part of a resilient and scalable cloud strategy.

  • Cloud Orchestration: What You Need to Know

    Cloud Orchestration: What You Need to Know

    Cloud orchestration is the automated coordination and management of cloud services, resources, and workflows. It streamlines complex processes—like deploying applications, scaling infrastructure, or provisioning resources—by bringing them under a single, automated system.

    Unlike basic automation (which handles individual tasks), orchestration connects multiple automated actions into a cohesive process. This reduces manual work, minimizes errors, and increases operational efficiency.

    Key functions of cloud orchestration include:

    • Resource provisioning (compute, storage, network)

    • Auto-scaling and load balancing

    • Workflow automation

    • Policy enforcement and security management

    Popular tools include:

    • Kubernetes (for container orchestration)

    • Terraform (for infrastructure as code)

    • AWS CloudFormation, Azure Resource Manager, and Google Cloud Deployment Manager

    Benefits of cloud orchestration:

    • Speed: Faster deployment and scaling

    • Consistency: Repeatable, error-free processes

    • Cost control: Efficient resource usage

    • Agility: Easier to adapt and update environments

    As cloud environments grow more complex, orchestration becomes essential for managing dynamic workloads, hybrid clouds, and DevOps pipelines.

    In short, cloud orchestration helps businesses move faster, operate smarter, and stay in control of their cloud infrastructure—without the chaos.

     

  • Cloud AI: What You Need to Know

    Cloud AI: What You Need to Know

    Cloud AI refers to artificial intelligence services and tools delivered through cloud platforms. It allows businesses and developers to access powerful AI capabilities—like image recognition, speech processing, chatbots, and predictive analytics—without building or managing complex infrastructure.

    Major cloud providers like Google Cloud, Amazon Web Services (AWS), and Microsoft Azure offer AI services through tools such as:

    • Google Cloud AI/Vertex AI
    • AWS AI & ML Services
    • Azure AI

    These platforms provide both pre-trained models (for tasks like language translation, sentiment analysis, and object detection) and custom model training options for specific business needs.

    Benefits of Cloud AI include:

    • Scalability: Easily handle large datasets and workloads
    • Speed: Quickly deploy AI features into apps and services
    • Cost-efficiency: Pay only for what you use—no expensive hardware needed
    • Accessibility: No need for deep AI expertise to get started

    Common use cases include customer support chatbots, recommendation engines, fraud detection, and real-time analytics.

    Cloud AI accelerates innovation by making advanced technologies more accessible to all organizations, from startups to enterprises. It empowers teams to solve complex problems, automate tasks, and make smarter decisions using data-driven insights.

  • Cloud Latency: What You Need to Know

    Cloud Latency: What You Need to Know

    Cloud latency is the delay between a user’s action and the cloud system’s response. It’s measured in milliseconds and plays a critical role in how fast applications and services perform in the cloud.

    Latency can be caused by several factors:

    • Physical distance between the user and the data center.

    • Network congestion or poor routing.

    • Server processing time and overloaded systems.

    In real-time applications—like video conferencing, online gaming, or financial trading—low latency is crucial. High latency can lead to delays, buffering, and a poor user experience.

    Cloud providers like AWS, Azure, and Google Cloud offer edge computing and Content Delivery Networks (CDNs) to reduce latency by processing data closer to users.

    Ways to reduce cloud latency:

    • Choose cloud regions closest to your users.

    • Use load balancers and CDNs.

    • Optimize code and reduce payload size.

    • Monitor latency using tools like CloudWatch, Pingdom, or Datadog.

    Understanding cloud latency helps ensure your applications stay fast and responsive. In today’s digital world, users expect instant interactions—so managing latency is not just technical, it’s strategic.

    A faster cloud means happier users and better business outcomes.

  • Cloud Scalability: What You Need to Know

    Cloud Scalability: What You Need to Know

    Cloud scalability refers to a system’s ability to handle increasing workloads by adjusting its resources—such as processing power, storage, and memory—on demand. It is a core advantage of cloud computing, allowing businesses to grow without facing performance issues or overprovisioning costs.

    There are two main types of scalability: vertical and horizontal. Vertical scaling (scaling up) involves adding more power to an existing machine, while horizontal scaling (scaling out) means adding more machines or servers to handle the load.

    Scalability is especially important for startups, e-commerce platforms, or any company expecting rapid growth or seasonal traffic spikes. With scalable cloud infrastructure, organizations pay only for the resources they use, leading to greater efficiency and cost savings.

    Key cloud providers like AWS, Azure, and Google Cloud offer auto-scaling features, which automatically adjust resources based on real-time demand.

    Understanding cloud scalability helps in planning infrastructure, improving user experience, and ensuring system reliability. Businesses that leverage scalable solutions are more agile, competitive, and ready to meet changing market demands.