Best Auto Scaling Software for Microsoft Azure

Find and compare the best Auto Scaling software for Microsoft Azure in 2025

Use the comparison tool below to compare the top Auto Scaling software for Microsoft Azure on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

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    RunPod Reviews

    RunPod

    RunPod

    $0.40 per hour
    167 Ratings
    See Software
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    RunPod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, RunPod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, RunPod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.
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    VMware Avi Load Balancer Reviews
    Streamline the process of application delivery by utilizing software-defined load balancers, web application firewalls, and container ingress services that can be deployed across any application in various data centers and cloud environments. Enhance management efficiency through unified policies and consistent operations across on-premises data centers as well as hybrid and public cloud platforms, which include VMware Cloud (such as VMC on AWS, OCVS, AVS, and GCVE), AWS, Azure, Google Cloud, and Oracle Cloud. Empower infrastructure teams by alleviating them from manual tasks and provide DevOps teams with self-service capabilities. The automation toolkits for application delivery encompass a variety of resources, including Python SDK, RESTful APIs, and integrations with Ansible and Terraform. Additionally, achieve unparalleled insights into network performance, user experience, and security through real-time application performance monitoring, closed-loop analytics, and advanced machine learning techniques that continuously enhance system efficiency. This holistic approach not only improves performance but also fosters a culture of agility and responsiveness within the organization.
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    StarTree Reviews
    StarTree Cloud is a fully-managed real-time analytics platform designed for OLAP at massive speed and scale for user-facing applications. Powered by Apache Pinot, StarTree Cloud provides enterprise-grade reliability and advanced capabilities such as tiered storage, scalable upserts, plus additional indexes and connectors. It integrates seamlessly with transactional databases and event streaming platforms, ingesting data at millions of events per second and indexing it for lightning-fast query responses. StarTree Cloud is available on your favorite public cloud or for private SaaS deployment. StarTree Cloud includes StarTree Data Manager, which allows you to ingest data from both real-time sources such as Amazon Kinesis, Apache Kafka, Apache Pulsar, or Redpanda, as well as batch data sources such as data warehouses like Snowflake, Delta Lake or Google BigQuery, or object stores like Amazon S3, Apache Flink, Apache Hadoop, or Apache Spark. StarTree ThirdEye is an add-on anomaly detection system running on top of StarTree Cloud that observes your business-critical metrics, alerting you and allowing you to perform root-cause analysis — all in real-time.
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    UbiOps Reviews
    UbiOps serves as a robust AI infrastructure platform designed to enable teams to efficiently execute their AI and ML workloads as dependable and secure microservices, all while maintaining their current workflows. In just a few minutes, you can integrate UbiOps effortlessly into your data science environment, thereby eliminating the tedious task of establishing and overseeing costly cloud infrastructure. Whether you're a start-up aiming to develop an AI product or part of a larger organization's data science unit, UbiOps provides a solid foundation for any AI or ML service you wish to implement. The platform allows you to scale your AI workloads in response to usage patterns, ensuring you only pay for what you use without incurring costs for time spent idle. Additionally, it accelerates both model training and inference by offering immediate access to powerful GPUs, complemented by serverless, multi-cloud workload distribution that enhances operational efficiency. By choosing UbiOps, teams can focus on innovation rather than infrastructure management, paving the way for groundbreaking AI solutions.
  • 5
    Lucidity Reviews
    Lucidity serves as a versatile multi-cloud storage management solution, adept at dynamically adjusting block storage across major platforms like AWS, Azure, and Google Cloud while ensuring zero downtime, which can lead to savings of up to 70% on storage expenses. This innovative platform automates the process of resizing storage volumes in response to real-time data demands, maintaining optimal disk usage levels between 75-80%. Additionally, Lucidity is designed to function independently of specific applications, integrating effortlessly into existing systems without necessitating code alterations or manual provisioning. The AutoScaler feature of Lucidity, accessible via the AWS Marketplace, provides businesses with an automated method to manage live EBS volumes, allowing for expansion or reduction based on workload requirements, all without any interruptions. By enhancing operational efficiency, Lucidity empowers IT and DevOps teams to recover countless hours of work, which can then be redirected towards more impactful projects that foster innovation and improve overall effectiveness. This capability ultimately positions enterprises to better adapt to changing storage needs and optimize resource utilization.
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    NVIDIA DGX Cloud Serverless Inference Reviews
    NVIDIA DGX Cloud Serverless Inference provides a cutting-edge, serverless AI inference framework designed to expedite AI advancements through automatic scaling, efficient GPU resource management, multi-cloud adaptability, and effortless scalability. This solution enables users to reduce instances to zero during idle times, thereby optimizing resource use and lowering expenses. Importantly, there are no additional charges incurred for cold-boot startup durations, as the system is engineered to keep these times to a minimum. The service is driven by NVIDIA Cloud Functions (NVCF), which includes extensive observability capabilities, allowing users to integrate their choice of monitoring tools, such as Splunk, for detailed visibility into their AI operations. Furthermore, NVCF supports versatile deployment methods for NIM microservices, granting the ability to utilize custom containers, models, and Helm charts, thus catering to diverse deployment preferences and enhancing user flexibility. This combination of features positions NVIDIA DGX Cloud Serverless Inference as a powerful tool for organizations seeking to optimize their AI inference processes.
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