Top 10 Alternatives to F5 BIG-IP WAF AWS Deployment & Integration Recently Reviewed By G2 Community
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Compute Engine enables you to create and run large-scale workloads on virtual machines hosted on Google Cloud. Get running quickly with pre-built and ready-to-go configurations or create machines of your own with the optimal amount of vCPU and memory required for your workload.
Multiple clouds. One cluster. All the benefits combined. Cast AI allows you to combine the benefits from multiple cloud providers in any way you want. Start with one or multiple clouds and Cast AI will create one cluster spanning them all. With auto-scaling, key metrics dashboard and management in vanilla Kubernetes out of the box.
AWS Auto Scaling is a service that helps to maintain application availability and allows to scale Amazon EC2 capacity up or down automatically according to conditions define.
Reduce K8s costs by up to 80%. Ensure workloads SLA. Free engineers from repeated manual configurations. ScaleOps is the industry-first Kubernetes Optimization Platform that automatically adjusts Compute Resources to changes in real-time, streamlining a new Kubernetes experience for engineering teams.
Pepperdata automatically optimizes system resources while providing a detailed, correlated understanding of each application using hundreds of application and infrastructure metrics collected in real-time. It highlights applications that need attention, automatically identifies bottlenecks, and alerts on duration, failure conditions, and resource usage. In the cloud or on-premises, this automated approach gives you complete observability and insight into your big data stack
Amazon EC2 Auto Scaling is a service that helps maintain application availability by automatically adjusting Amazon EC2 instance capacity to meet changing demand. It enables users to define scaling policies that dynamically add or remove instances based on real-time metrics or predictable schedules, ensuring optimal performance and cost efficiency. Key Features and Functionality: - Automatic Scaling: Seamlessly launches new EC2 instances when demand increases and terminates unneeded instances when demand decreases, optimizing resource utilization. - Dynamic and Predictive Scaling: Adjusts capacity based on Amazon CloudWatch metrics or predefined schedules, allowing for proactive scaling in anticipation of traffic changes. - Health Monitoring and Replacement: Continuously monitors instance health and automatically replaces impaired instances to maintain desired capacity and application availability. - Multi-AZ Deployment: Distributes instances across multiple Availability Zones to enhance fault tolerance and resilience against localized failures. - Support for Multiple Instance Types and Purchase Options: Allows provisioning of various instance types and purchase options, including On-Demand and Spot Instances, to optimize performance and cost. - Integration with Load Balancing: Works with Elastic Load Balancing to distribute incoming traffic evenly across healthy instances, ensuring consistent application performance. Primary Value and Problem Solved: Amazon EC2 Auto Scaling addresses the challenge of fluctuating application demand by automatically adjusting compute capacity, ensuring applications remain responsive and cost-effective. By automating the scaling process, it reduces the need for manual intervention, minimizes the risk of over-provisioning or under-provisioning resources, and enhances overall application reliability and performance.
Xosphere's super power is reducing AWS EC2 expense by up to 80%. Xosphere is the world's only intelligent cloud orchestration company empowering enterprises to seamlessly move applications to the right place at the right time to reduce cloud expense and increase reliability. Xosphere's intelligent cloud software transforms unreliable Spot instances into robust resources that have the same reliability as On-Demand but at a fraction of the cost, yielding unparalleled savings. For enterprises that want to reduce cloud costs, Xosphere's optimization engine maximizes savings with the fastest speed of implementation in the industry. Xosphere Instance Orchestrator is a cloud-native, self-hosted subscription software application. It installs into your Amazon Web Services (AWS) account using either a CloudFormation stack or a Terraform module and runs using Lambda functions. Instance Orchestrator uses an opt-in design; it only executes on Auto-Scaling groups or individual instances that have explicitly been enabled via an AWS tag. Tags can be applied using any method or tool that is used within the organization to manage tags (for example, AWS Console, AWS CLI, AWS APIs, infrastructure-as-code platforms such as CloudFormation or Terraform, cloud management platforms, etc.). Once this enabling tag has been applied, Instance Orchestrator will automatically perform its management duties on an ongoing basis.
Avi Networks enables public-cloud-like simplicity and flexibility for application services such as load balancing, application analytics, and security in any data center or cloud.
UbiOps by Dutch Analytics is an all-in-one software platform that enables you to very quickly turn your algorithms into scalable, robust and secure end-to-end applications. This without requiring knowledge to set up Cloud infrastructure, micro-services, automated scaling, or DevOps practices. Save months of work as UbiOps takes care of a smooth transition from where data science ends to where IT starts. Easily deployed across public/private Cloud or On-Premise. Centrally managed and fully secured with data and code encryption.
Sedai incorporates key autonomous system characteristics in a cloud context. By leveraging a massive influx of data streams, Sedai builds a layer of intelligence via its core decision engine, which derives concepts from probability theory and applied machine learning techniques. Its self-learning and self-correcting model seamlessly manages cloud platforms with a focus on explainable decisions. Our products S.Watch Sedai connects with various monitoring tools, including Prometheus, Datadog, Cloudwatch, etc., and tracks four golden signals: Latency, Traffic, Errors, and Saturation. S-Watch distills noise to provide insights and recommendations to bring key KPIs such as MTTD, MTTF, MTBF, and MTTR to acceptable levels. S.Run Sedai distills data into an explainable and tunable knowledge base that powers its machine learning models. These models fuel Sedai’s core decision engine, which determines efficient and corrective workflows for all identified drifts to infer optimal strategies for detection and safe remediations. Its true closed-loop learning model enables self-configuration at optimal levels, ensuring the highest levels of availability. Armed with vast data, deep insights, and a rich knowledge base, platforms that are managed by Sedai are able to achieve a self-optimized state.