AI-powered automated rightsizing for Azure VMs is a process where Sedai's platform uses advanced AI to analyze your Azure virtual machine usage and automatically adjust VM types and sizes to minimize costs while maintaining performance and reliability. This eliminates manual effort and ensures your VMs are always optimally configured for your workloads.
Rightsizing is crucial because most Azure VMs are significantly underutilized—analysis shows a median CPU utilization of just 8.2%, with 72% of users below 20%. Overprovisioning leads to unnecessary cloud costs. Effective rightsizing can halve costs by doubling utilization, ensuring you only pay for the resources you actually need.
Common causes include developer bias towards overprovisioning for safety, bursty workloads that are hard to predict, application architectures that don't support horizontal scaling, and the complexity of choosing from over 400 Azure VM types. These factors make it difficult to select the optimal VM size manually.
Sedai's platform continuously analyzes your Azure VM infrastructure, identifies inefficiencies, and autonomously implements optimizations. The process includes discovering your VM fleet, recommending optimal settings, validating changes with safety and timing checks, executing the changes, and tracking results for continuous improvement.
The process includes: 1) Discovering your VM infrastructure and application patterns, 2) Recommending optimal VM types and configurations, 3) Validating changes with safety and timing checks, 4) Executing the changes, and 5) Learning from outcomes to further improve future optimizations.
Sedai performs multiple safety checks before making any changes, including validating if the action can be performed safely and determining the right timing for execution. The platform tracks all changes with a full audit trail and verifies application health after each action.
Early adopters have seen Azure VM cost reductions of up to 30% or more. For example, a healthcare company identified over $250,000 in annual savings (28%) in its dev/test environments through Sedai's optimization.
Sedai's automated optimization can reduce the time to rightsize Azure VMs by up to 90%, streamlining what is typically a complex and time-consuming process.
Yes, Sedai offers both agentless and agent-based deployment options for Azure VM optimization, allowing you to choose the approach that best fits your environment and security requirements.
Sedai's AI analyzes workload patterns, including bursty traffic, and recommends suitable VM types (such as burstable instances) to ensure cost efficiency without sacrificing performance, even when workloads are unpredictable.
Sedai uses a comprehensive set of metrics, including CPU and memory utilization, latency, error rates, saturation, and throughput. These 'golden signals' ensure that optimizations improve both cost efficiency and application performance.
Sedai's approach is fully autonomous, uses a broader set of metrics (not just utilization), and performs safety checks before making changes. In contrast, Azure Advisor often requires manual effort, uses limited metrics, and does not validate changes for safety, making Sedai's solution more comprehensive and less labor-intensive.
Sedai discovers your VM infrastructure using cloud APIs, analyzes traffic patterns and VM tags to identify application boundaries, and groups VMs performing similar tasks. This enables collective optimization actions across all instances of an application.
Yes, Sedai provides a dashboard that allows you to scan your VM fleet at a glance, showing which applications are over-provisioned, under-provisioned, or optimized based on Sedai's findings. For example, in one environment, 61% of apps were optimized (shown as green).
Sedai offers flexible pricing based on the scale of your Azure VM deployment and usage levels. For detailed pricing information or a custom quote, you can request a demo directly from Sedai.
You can request a demo to see how Sedai can help rightsize your Azure VMs. The platform supports quick onboarding and offers both agentless and agent-based deployment options.
Comprehensive technical documentation is available at docs.sedai.io/get-started, including setup guides, feature explanations, and troubleshooting resources.
Sedai provides personalized onboarding sessions, a dedicated Customer Success Manager for enterprise customers, detailed documentation, a community Slack channel, and email/phone support to ensure a smooth adoption process.
Sedai's platform offers autonomous optimization, proactive issue resolution, full-stack cloud coverage (including Azure VMs), release intelligence, enterprise-grade governance, and multiple modes of operation (Datapilot, Copilot, Autopilot). These features help reduce costs, improve performance, and enhance reliability across cloud environments.
Yes, Sedai supports optimization across AWS, Azure, GCP, and Kubernetes environments, providing a unified solution for multi-cloud and hybrid cloud operations.
Sedai integrates with Cloudwatch, Prometheus, Datadog, Azure Monitor, GitLab, GitHub, Bitbucket, Terraform, ServiceNow, Jira, Slack, Microsoft Teams, and various runbook automation platforms, ensuring seamless workflow integration.
Sedai's release intelligence tracks changes in cost, latency, and errors for each deployment, helping Azure VM users improve release quality, minimize risks, and ensure smoother deployments.
Sedai offers three modes: Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution), allowing organizations to choose the level of automation that fits their needs.
Sedai is SOC 2 certified, demonstrating adherence to stringent security and compliance standards. The platform integrates with Infrastructure as Code (IaC), IT Service Management (ITSM), and compliance workflows to ensure safe and auditable changes. More details are available on Sedai's Security page.
Organizations running applications on Azure VMs—especially those with underutilized or overprovisioned resources—can benefit. This includes companies in healthcare, financial services, IT, travel, and more, as well as roles like platform engineering, IT/cloud ops, technology leadership, SRE, and FinOps.
Customers can expect up to 30% or more in Azure VM cost savings, improved application performance (up to 25% better latency), and up to 90% reduction in operations effort for rightsizing. These outcomes drive operational efficiency and free up engineering resources for innovation.
Yes, a healthcare company identified over $250,000 in annual savings (28%) in its dev/test environments by rightsizing Azure VMs with Sedai's optimization technology.
Sedai addresses pain points such as overprovisioning, manual and repetitive rightsizing tasks, complexity in VM selection, risk of performance degradation, and lack of actionable insights. The platform automates optimization, reduces costs, and ensures performance and reliability.
Sedai tracks all optimization actions with a full audit trail, integrates with compliance workflows, and ensures that all changes are safe, validated, and reversible, supporting compliance and audit requirements for cloud operations.
Customers have highlighted the simplicity and efficiency of Sedai's platform, quick setup (5–15 minutes), agentless integration, and comprehensive onboarding support as key benefits. The 30-day free trial allows users to experience the platform's value risk-free.
Sedai stands out by offering 100% autonomous optimization, proactive issue resolution, application-aware intelligence, and comprehensive safety checks. Unlike solutions like Azure Advisor, Sedai uses a broader set of metrics, automates the entire process, and ensures safe, validated changes with full auditability.
Sedai's uniqueness lies in its autonomous, AI-driven approach, use of golden signals for optimization, safety-by-design philosophy, and ability to reduce both costs and operational effort without manual intervention. The platform's flexibility (agentless/agent-based) and integration with compliance workflows further differentiate it.
Platform engineers benefit from reduced toil and consistent optimization; IT/cloud ops teams see lower ticket volumes and safer automation; technology leaders gain measurable ROI and cost savings; FinOps teams get actionable savings and multi-cloud visibility; SREs experience fewer incidents and less manual work.
For most use cases, Sedai's setup process takes just 5 minutes. For specific scenarios like AWS Lambda, it may take up to 15 minutes. More complex environments may require additional time, and a demo can be scheduled to discuss your setup.
Sedai supports both agentless and agent-based deployment options, allowing you to choose the best fit for your environment and security policies.
Technical documentation, including setup guides and feature explanations, is available at docs.sedai.io/get-started. Additional resources such as case studies and datasheets can be found on the resources page.
Sedai is SOC 2 certified, ensuring adherence to industry standards for data protection and compliance. More information is available on the Sedai Security page.
Sedai's customers include Palo Alto Networks, HP, Experian, KnowBe4, Expedia, CapitalOne Bank, GSK, and Avis. These organizations trust Sedai to optimize their cloud environments and improve operational efficiency.
Sedai's case studies span industries such as cybersecurity, IT, financial services, healthcare, travel, car rental, retail/e-commerce, SaaS, and digital commerce, demonstrating the platform's versatility and impact across sectors.
Yes. For example, KnowBe4 achieved up to 50% cost savings in production and saved $1.2 million on their AWS bill. Palo Alto Networks saved $3.5 million and reduced Kubernetes costs by 46%. Belcorp reduced AWS Lambda latency by 77%. More case studies are available on the Sedai resources page.
John Jamie
Content Writer
May 7, 2024
Featured
In environments where applications are not suitable for microservices architectures, rightsizing and in particular vertical scaling becomes a critical strategy to achieve cost-effective operations while meeting performance requirements. This approach involves choosing the right virtual machine type based on the CPU and memory resources required. Rightsizing is a known best practices for Azure VM cost optimization but is hard to implement in practice.
Analysis of the most recently released Azure VM usage dataset (available from Microsoft’s Github account here) shows that Azure VM users had an an average utilization of just 8.2%, with 72% of users having an average utilization of below 20% (see distribution below). A common pattern uncovered in the dataset was the selection of a small number of powerful, but oversized VMs.
Source: “Using Virtual Machine Size Recommendation Algorithms to Reduce Cloud Cost”, March 2023
Below is an example from a Sedai Azure customer environment of a heavily underutilized instance with just a few percent of CPU being used across a two day period:
This is a customer facing metric. Azure and other cloud providers achieve higher rates internally due to operating shared environments (AWS reported 65% utilization a few years ago). The pricing for shared instances reflect this with dedicated instances costing 250% or more.
Effective vertical scaling is also important as developers often default to overprovisioning Azure VM resources, opting for a simpler and quicker setup rather than conducting extensive testing across multiple instance types. This approach, while expedient, typically results in selecting VM configurations that exceed the application's actual requirements, leading to increased costs. The reluctance to engage in detailed testing stems from the time and complexity involved in evaluating each instance type's performance under different workloads. Consequently, developers lean towards a 'better safe than sorry' strategy. Although reducing the risk of underperformance thisinefficiently raises cloud costs.
Many Virtual Machine workloads have bursty traffic patterns, especially for small databases, development environments, and low-traffic websites. For example, in the case below of a dev/test workload CPU utilization stays around 2-4% but surges twice to the 13-15% range.
Given warmup periods may range from a few minutes to a few hours, horizontal scaling may not be viable. Finding suitable burstable instance types may be the preferred approach. In the absence of that, low average utilization will be achieved.
A high proportion of applications running on Azure run directly on virtual machines. These applications have not been replatformed to a microservice architecture such as Kubernetes (including Azure Kubernetes Service (AKS)), or serverless frameworks (Azure Functions). One key reason is that many of these applications do not benefit significantly from the horizontal scaling capabilities offered by microservices architectures. They may have architectural or design constraints that make such a transition complex or suboptimal, limiting their ability to efficiently use newer computing paradigms.
Vertical scaling is also complicated by the many types of VMs available. There are currently over 400 types of Azure Virtual Machine options offering varying Compute, Memory and other characteristics. Below is a chart showing the density of options based on vCPU and Memory size:
Asking an engineer to make the optimal choice across potentially hundreds or thousands of services can be challenging, especially if new code updates change the service's characteristics and then require a new determination of the right instance type.
Vertical scaling is particularly advantageous for applications that require high-performance levels from single instances or have dependencies that complicate distribution across multiple servers. By optimizing the configuration of Azure VMs to align closely with actual workload requirements, organizations can ensure that their applications perform optimally without incurring unnecessary costs from overprovisioning. This method allows for more precise control over resource allocation, leading to enhanced performance and reduced expenditures.
Sedai’s Automated Optimization utilizes advanced AI technology to deeply comprehend Azure VM configurations and their impact on application cost and performance. This results in Azure VMs that are optimally sized and configured to meet the specific needs of applications without incurring unnecessary costs or performance issues. Key benefits include:
Sedai’s Automated Optimization uses advanced AI that not only deeply understands Azure VM configurations and how they are impacting application cost and performance. This results in VMs that are optimally sized and configured to meet the specific needs of their applications without any excess cost or underperformance.
Our AI-driven platform continuously analyzes your Azure VMs to detect inefficiencies. It then autonomously implements optimizations, adjusting resources in real-time without requiring manual intervention.
The Sedai platform operates on a simple yet effective process: Discover, Recommend, Validate, Execute, and Track:
These capabilities form part of Sedai’s overall Azure VM optimization approach which can be seen below:
Some of the key elements above are:
Early adopters have seen significant improvements in both performance and cost efficiency. For instance, a healthcare company has identified over $250k of annual savings, a 28% saving, in its dev / test environments through rightsizing using Sedai’s optimization.
Below is an example of the safety check process being performed during a VM resizing. In this case it took 11 steps, most completed quickly but stopping and restarting the VM taking around 30 seconds each:
To gain insights on the state of your VM fleet you can scan it at a glance to see where applications are over or under provisioned as well as optimized based on Sedai’s findings. The the example below 61% of the apps have been optimized (shown as green).
The service is available now, with flexible pricing based on the scale of your Azure VM deployment. Request a demo to see how Sedai can hello you rightsize your Azure VMs.
